Camera-linked lighting control system

Abstract

To adjust the chromaticity of illumination light to suit a target object without replacing a lighting device.SOLUTION: A camera-linked lighting control system 100 including a camera 110, a camera / lighting control device 130, and an object lighting device 160 is configured to calculate from an image captured by a camera 110 on the basis of an R value, G value, and B value of each pixel their average values Rave, Gave, and Bave and the standard deviation values Rsd, Gsd, and Bsd. The object lighting device 160 irradiates illumination light with a correlated color temperature (CCT) corresponding to these values.SELECTED DRAWING: Figure 12

Description

[Technical Field]

[0001] The present invention relates to a camera-linked lighting control system equipped with a camera and a lighting device. [Background technology]

[0002] A system has been proposed that links lighting devices with cameras and controls the lighting devices based on image information captured by the cameras. Patent Document 1 proposes a system that uses a camera and an image processing unit as a person position detection means and controls lighting so as to turn on lighting devices corresponding to the positions of people.

[0003] Patent Document 2 describes an illumination device that makes meat look vibrant.

[0004] Patent Document 3 describes an illumination system in which a light-emitting unit is driven based on light-emitting mode data associated with the type of food that is the illumination target.

[0005] Patent Document 4 describes a lighting device that can irradiate optimal light for each food ingredient using a first light source with a warm color system in which the spectral components of blue light, red light, and green light are mixed in a predetermined ratio, a second light source with a high color temperature in which the spectral components of blue light, red light, and green light are mixed in a ratio different from that of the first light source, and a third light source that has only the spectral component of blue light. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Laid-Open No. 2014-007123 [Patent Document 2] Japanese Patent Application Laid-Open No. 2013-127855 [Patent Document 3] Japanese Patent Application Laid-Open No. 2013-077387 [Patent Document 4] Patent No. 5507148 Summary of the Invention [Problem to be solved by the invention]

[0007] For example, in supermarkets, it is desirable to provide lighting that is optimal for the products on display, and lighting devices that optimize the color of the light depending on the type of product are commercially available. However, in the fruit section, for example, it is necessary to stop selling spring fruits and display summer fruits, or to expand the display area depending on sales. In such cases, there is a problem in that the lighting devices also need to be replaced.

[0008] SUMMARY OF THE INVENTION It is therefore an object of the present invention to provide a camera-linked lighting control system that can adjust the chromaticity of illumination light to suit an object without the need to change lighting devices depending on the object. [Means for solving the problem]

[0009] The present invention provides a camera-linked lighting control system including a camera, a device with an image recognition function, a lighting control device, and an object lighting device, Photographing an object with the camera; The device having the image recognition function calculates an average value of the R values, an average value of the G values, an average value of the B values, a standard deviation value of the R values, a standard deviation value of the G values, and a standard deviation value of the B values, based on an R value, which is the red value of each pixel in the original image or the target area image obtained by cutting out the area of ​​the object from the original image or the target area image, the lighting control device sets a correlated color temperature according to the average value of the R values, the average value of the G values, the average value of the B values, the standard deviation value of the R values, the standard deviation value of the G values, and the standard deviation value of the B values; the object lighting device irradiates the object with illumination light having the correlated color temperature; It is a camera-linked lighting control system.

[0010] In the present invention, the correlated color temperature may be a value corresponding to a value obtained by multiplying the average value of the R values, the average value of the G values, the average value of the B values, the standard deviation value of the R values, the standard deviation value of the G values, and the standard deviation value of the B values ​​by coefficients and adding them together.

[0011] The present invention provides a camera-linked lighting control system including a camera, a device with an image recognition function, a lighting control device, and an object lighting device, Photographing an object with the camera; The device having the image recognition function calculates average values ​​of the R value, the G value, and the B value from an original image captured by the camera or a target area image obtained by cutting out the area of ​​the target from the original image, based on an R value, which is the red value of each pixel in the original image or the target area image, a G value, which is the green value of each pixel, and a B value, which is the blue value of each pixel; the lighting control device sets a correlated color temperature according to a value obtained by multiplying the average value of the R value, the average value of the G value, and the average value of the B value by a coefficient, the coefficient by which the average value of the R value is multiplied is a negative value, and the coefficient by which the average value of the G value is multiplied is a positive value, the object lighting device irradiates the object with illumination light having the correlated color temperature; It is a camera-linked lighting control system.

[0012] In the present invention, the coefficient by which the average value of the B value is multiplied may be a negative value.

[0013] In the present invention, the lighting control device changes the color deviation in conjunction with the correlated color temperature, The object lighting device may irradiate the object with illumination light having the correlated color temperature and the color deviation. [Effects of the Invention]

[0014] In the present invention, the illumination light of the object illumination device can be controlled to a chromaticity suited to the hue of the object, thereby making it possible to make the object appear in a desired hue. [Brief explanation of the drawings]

[0015] [Figure 1] Configuration diagram of a camera-linked lighting control system according to the first embodiment [Figure 2] Flowchart of image recognition and lighting by the camera-linked lighting control system of embodiment 1 [Figure 3] 1. An image taken for setting an area in the camera-linked lighting control system of the first embodiment. [Figure 4] An image showing an object and a background in the camera-linked lighting control system of the first embodiment [Figure 5] Chromaticity diagram for explaining chromaticity control in the camera-linked lighting control system of embodiment 1. [Figure 6] Chromaticity diagram illustrating the chromaticity of three colors: red, yellow-white, and bluish-white [Figure 7] Configuration diagram of a camera-linked lighting control system according to a second embodiment [Figure 8] Graph showing an example of scheduled operation of general lighting devices [Figure 9] Chromaticity diagram for explaining chromaticity control of general lighting devices and object lighting devices [Figure 10] FIG. 1 is an explanatory diagram of a method using a two-dimensional barcode to indicate the lighting conditions for illuminating an object or a target area in the first embodiment. [Figure 11] FIG. 1 is an illustration of a method of using an LED signal transmitter to indicate lighting conditions for illuminating an object or target area in accordance with embodiment 1. [Figure 12] FIG. 10 is an explanatory diagram showing the relationship between food and the color temperature of illumination light that makes it taste delicious in the third embodiment. [Figure 13] Configuration diagram of a camera-linked lighting system according to the fourth embodiment [Figure 14] Inside the lamp body of the object lighting device of embodiment 4 [Figure 15] FIG. 10 is a plan view of a light source used in the object illumination device of the fourth embodiment. [Figure 16] Chromaticity diagram of three-color LEDs used in the object illumination device of embodiment 4 [Figure 17]Spectral diagram of LEDs (D, F) used in the object illumination device of embodiment 4 [Figure 18] Spectral diagram of LEDs (D, E) used in the object illumination device of embodiment 4 [Figure 19] Chromaticity diagram (full chromaticity range) of three-color LEDs used in the object illumination device of embodiment 4 [Figure 20] Schematic diagram to explain color unevenness [Figure 21] FIG. 10 is a diagram showing the configuration of an object lighting device used in the camera-linked lighting system of the fifth embodiment. [Figure 22] FIG. 10 is an exploded perspective view of a lamp body of an object illumination device according to a fifth embodiment. [Figure 23] Chromaticity diagram of four-color LEDs used in the object illumination device of embodiment 5 [Figure 24] Synthetic spectrum obtained in the object illumination device of embodiment 5 [Figure 25] Configuration diagram of a camera-linked lighting control system according to the sixth embodiment [Figure 26] Flowchart of image recognition and lighting by the camera-linked lighting control system of embodiment 6 [Figure 27] FIG. 10 is an explanatory diagram of image segmentation in the camera-linked lighting control system of the sixth embodiment. [Figure 28] Configuration diagram of a camera-linked lighting control system according to a seventh embodiment [Figure 29] FIG. 13 is an explanatory diagram of image segmentation in the camera-linked lighting control system of the seventh embodiment. [Figure 30] Flowchart of image recognition and lighting by the camera-linked lighting control system of embodiment 7 [Figure 31] FIG. 13 is an explanatory diagram of a photograph of a refrigerated case with a meat tray and its image segmentation in the camera-linked lighting control system of the seventh embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0016] <Embodiment 1> <Application> The configuration of a camera-linked lighting control system 100 according to embodiment 1 is shown in Fig. 1. This is a camera-linked lighting control system that photographs an object with a camera and illuminates the object with light of a color suited to its color.

[0017] <Configuration> The camera-linked lighting control system 100 includes a camera 110 installed on a ceiling 190, a camera and lighting control device 130, object lighting devices 160A and 160B, a gateway 155, a camera control device monitor 132 at a person's hand, a second lighting control device 150, and the like.

[0018] <Object illumination device> One or more object lighting devices 160 (the number can be any number, but for example, if there are two, they will be referred to as object lighting devices 160A and 160B) are, for example, spotlights, and each emits illumination light 162A and 162B (referred to as illumination light 162 if A and B are not distinguished) to illuminate objects 180A and 180B (referred to as object 180 if A and B are not distinguished) in object areas 183A and 183B (referred to as object area 183 if A and B are not distinguished). The object lighting device 160 can change the chromaticity of the illumination light, and preferably can change the color deviation Duv (described below), which is a factor other than color temperature. Such change in chromaticity of the illumination light is performed using the second lighting control device 150 or the camera and lighting control device 130.

[0019] <Object> Objects 180A and 180B are placed on backgrounds 182A and 182B, respectively (when A and B are not distinguished, they are referred to as background 182).

[0020] <Lighting control device> The second lighting control device 150 is a tablet-type terminal device equipped with a touch panel display that serves as both an input unit and a display unit, and also includes an internal control unit, memory unit, and wireless communication unit. By running lighting control software in the second lighting control device 150, the brightness (total luminous flux) and color of the object lighting device 160 and the like can be controlled. The lighting control software can also perform schedule control that changes the control content depending on the time. The second lighting control device 150 may be a computer, smartphone, etc.

[0021] <Gateway> It is possible for second lighting control device 150 to send a lighting control signal directly (without going through gateway 155) to object lighting device 160, but particularly when second lighting control device 150 is a portable device such as a tablet, it is also possible to send, for example, schedule control information via wireless communication 152 to gateway 155 installed in a fixed location, which then stores the content, and at a predetermined time transmits a lighting control signal to object lighting device 160 via wireless communication 154. This is because there are cases where normal wireless communication cannot be performed from portable second lighting control device 150.

[0022] <Camera and camera / lighting control device> The camera 110 is, for example, a camera with a fisheye lens, and captures an image 113 within a field of view 112 and transmits it to a camera and lighting control device 130 .

[0023] The camera and lighting control device 130 is a device that has camera control functions and lighting control functions, and also has an image recognition function. These functions are realized by a computer (hardware) and software. As an output device, for example, a camera control device monitor 132 is provided, which displays the image 113 and control contents. The camera and lighting control device 130 is connected to a gateway 155 via a communication line 157.

[0024] The camera and lighting control device 130 may not be provided with the camera control device monitor 132 and may be operated by communicating with another PC or tablet.

[0025] <Lighting control configuration> The object lighting device 160 performs an operation preset by the second lighting control device 150, which may be a tablet or the like, or the camera and lighting control device 130. The operation is stored in, for example, a gateway 155, which is a repeater. When an instruction is received from the camera and lighting control device 130 or the second lighting control device 150, the gateway 155 transmits the operation preset in response to the instruction to the object lighting device 160, and the object lighting device 160 operates according to the instructions from the second lighting control device 150 and the camera and lighting control device 130. Note that communication between these devices is performed via a communication line 117 between the camera 110 and the camera and lighting control device 130, a communication line 157 between the camera and lighting control device 130 and the gateway 155, wireless communication 152 between the second lighting control device 150 and the gateway 155, and wireless communication 154 between the gateway 155 and the object lighting device 160. However, wired communication may be replaced with wireless communication, or wireless communication may be replaced with wired communication. Infrared or visible light communication may also be used for communication.

[0026] <Image Recognition and Lighting Flowchart> A flowchart of image recognition and lighting by the camera-linked lighting control system 100 is shown in Figure 2. Steps S1 to S5 are initial settings, and are performed when the target area is changed due to room rearrangements, etc. Steps S6 and onwards are performed for each target area (although the camera 110 captures an image of the entire area). The loop from S11 to S6, or S9 to S6, can be performed manually when the target object is changed, or in the case of a store, it can be performed once a day before opening, or at regular intervals, etc., as appropriate.

[0027] In "S1. Initial lighting", the object lighting devices 160A and 160B are turned on.

[0028] In "S2. Acquisition of initial image", the camera 110 captures the image 113, and the image data is input to the camera and lighting control device .

[0029] In "S3. Area Setting," the image 113 captured for area setting, as shown in Fig. 3, is displayed on the camera control device monitor 132. Although the image 113 has distortions typical of fisheye lens images, the operator manually specifies target areas 183A and 183B, which are as small as possible and have a rectangular or circular shape, to include the objects 180A and 180B, and stores the specified target areas in the camera and lighting control device 130. The coordinates of the target areas set here are also used to cut out the target areas in images captured later.

[0030] In addition to manually setting the target area, it can also be set automatically. Figure 4 shows an image containing an object 180 and a background 182. The image recognition function can recognize the area containing the background 182 and set it as the target area 183. In this case, if a "distinctive background" that is easy for the image recognition function to recognize is used as the background pattern, there is an advantage that the background can be recognized even on devices with relatively low-cost image recognition functions. Here, a grid pattern of white (W), light gray (WD), and gray (D) is used as the "distinctive background." In this case, the image recognition function can recognize corners, etc., indicated by CN, and recognize them as "background" rather than regular patterns.

[0031] In "S4. Matching areas and lighting devices", the camera control device monitor 132 matches the target area 183A with the object lighting device 160A, and the target area 183B with the object lighting device 160B (similarly if there are other object lighting devices and target areas).

[0032] This allows the camera and lighting control device 130 to control the object lighting devices 160A and 160B via the gateway 155.

[0033] In "S5. Acquiring background image", an image of the entire area is captured without the object, thereby obtaining images of the target areas 183A and 183B without the object.

[0034] "S6. Zero the color deviation of the illumination light in the target area" is a desirable step in "S10. Recognizing the target object" to accurately recognize the chromaticity of the target object, but it is not essential. "Zeroing the color deviation" means using illumination light close to the "blackbody radiation locus BBL" described below.

[0035] In "S7. Acquire target image", the camera 110 acquires an image 113 of the target object placed thereon.

[0036] In "S8. Cut out area of ​​target image", the camera and lighting control device 130 cuts out the image of the target area 183A, 183B whose area was set in S3.

[0037] In "S9. Comparison of background image and target image of target area," the camera and lighting control device 130 compares the background image and target image acquired in S5 with respect to the target area. Here, the "target image" is as shown in FIG. 4.

[0038] If the object 180 is placed in the target image, it is determined to be "different" from the background image, and the process proceeds to the next step (recognition of the object).

[0039] On the other hand, if the object 180 is not placed in the target image, it will be "same", so the process returns to S6 and the color deviation of the illumination light in the target area is set to zero. This is because, since there is no object, there is no need to emphasize the color, and it is desirable to illuminate with a natural color.

[0040] <Object Recognition> In "S10. Recognizing an object", the image recognition function recognizes the object 180 in the object area 183.

[0041] Here, the "image recognition function" is realized as a function executed by software in the camera and lighting control device 130, and at least a part of the camera and lighting control device 130 becomes the "image recognition function." On the other hand, the "image recognition function" may be realized by being built into the camera 110. Furthermore, the "image recognition function" may be realized in an external server or external PC connected to the camera-linked lighting control system 100.

[0042] There are various methods for recognizing objects. One method is to use an image recognition function that does not recognize the shape, but recognizes the color histogram for each pixel, extracts colors other than the color components of the background image, and identifies the color of the object 180. Here, it is also possible to simply detect the color of the object without going as far as recognizing what the object is. This also makes it possible to perform lighting control, as described below, such as "if the object is red, make the color deviation Duv of the chromaticity of the illumination light negative," or "if the object is green or yellow, make the color deviation Duv of the chromaticity of the illumination light positive."

[0043] If the color of the object is similar, the size and shape of the object can be identified, and the object and its color can be recognized based on that. For example, a small "cherry" (e.g., 1 cm diameter) and a large "apple" (e.g., approximately 7 cm diameter) can be recognized based on their size. Also, a "cherry" and a "strawberry" (e.g., 3 cm diameter) can be recognized based on their size.

[0044] In addition, if the color of the object is similar, the object can be recognized by its shape, such as a "round lemon" or a "long, thin banana."

[0045] In other words, it is desirable to recognize an object using at least one of its color distribution, size, and shape.

[0046] Furthermore, objects can be recognized using a technique called deep learning. By having AI (Artificial Intelligence) learn the relationship between many images and objects in advance, the AI ​​can recognize objects as an image recognition function.

[0047] On the other hand, as a method of recognizing the object 180, instead of the object 180 itself, a poster displaying the object 180 such as a sign with the word "apple" or a barcode or two-dimensional barcode may be recognized.

[0048] Fig. 10 is an explanatory diagram of a method for identifying the lighting conditions for illuminating an object or a target area using a two-dimensional barcode. As shown in Fig. 10, a QR code (registered trademark) 184, which is a two-dimensional barcode that covers a large area within a target area 183, is prepared, and this QR code (registered trademark) 184 is photographed by camera 110. The area of ​​QR code (registered trademark) 184 is made large because camera 110 has a wide angle, and the target area appears small; however, QR code (registered trademark) 184 need only be large enough to be identified by camera 110.

[0049] If QR code (registered trademark) 184 indicates the name of an object (for example, "marbled beef"), camera and lighting control device 130 controls the chromaticity of object lighting device 160 to the chromaticity corresponding to the object name. At this time, a table showing the correspondence between object names and lighting conditions (chromaticity, color temperature, color deviation, total luminous flux) is stored in advance in camera and lighting control device 130 or in a storage device accessible by camera and lighting control device 130. If QR code (registered trademark) 184 indicates the lighting conditions themselves, camera and lighting control device 130 controls the lighting conditions, such as the chromaticity, of object lighting device 160 according to the lighting conditions indicated by QR code (registered trademark) 184 read by camera 110.

[0050] Instead of the QR code (registered trademark) 184, the target area 183 may be provided with a single or multiple patterns or marks such as circles, squares, triangles, or crosses corresponding to the target object or lighting conditions (chromaticity, color temperature, color deviation, total luminous flux), and specifically, a tablecloth or other covering with a pattern or mark may be placed.

[0051] Also, an LED signal transmitter 185 may be installed in the target area to indicate the lighting conditions for illuminating the target object or target area. Fig. 11 is an explanatory diagram of a method using an LED signal transmitter. The LED signal transmitter 185 flashes visible light or infrared light corresponding to, for example, the name of the target object or the lighting conditions. The camera and lighting control device 130 analyzes the flashing light via the camera 110 and controls the lighting conditions of the target lighting device 160.

[0052] The LED signal transmitter 185 may be provided with a plurality of LEDs, and the number of LEDs that light up within the target area may be controlled to control the lighting conditions, such as "for vegetables" when one LED lights up or flashes, or "for meat" when two LEDs light up or flash.

[0053] The above-mentioned notices, barcodes, two-dimensional barcodes, patterns, marks, LED signal transmitters, etc. that indicate the object are collectively referred to as object identification members.

[0054] The camera-linked lighting control system 100 may operate constantly, for example, during store business hours, with the camera 110 automatically capturing images of the object identification members and the camera-linked lighting control system 100 automatically controlling the lighting conditions. For example, the store manager may train a new salesperson to replace the object identification members and display different products when a product in the target area is sold out, and the camera-linked lighting control system 100 will automatically illuminate the product under lighting conditions suitable for the new product without the new salesperson having to manually give instructions to change the lighting conditions.

[0055] <Lighting control according to the color of the object> In "S11. Illuminate the target area with illumination light according to the recognition result," illumination control is performed according to the color of the target. If there are multiple target areas, illumination control is performed individually for each object illumination device associated with each target area, and the target areas are illuminated.

[0056] 5 is a chromaticity diagram for explaining chromaticity control in the camera-linked lighting control system 100, showing chromaticity (x, y) in the CIE 1931 chromaticity coordinates. The x-axis represents the x chromaticity coordinate, the y-axis represents the y chromaticity coordinate, BBL represents the blackbody radiation locus, and T CCT indicates the correlated color temperature, and Duv indicates the color deviation (1000 times duv).

[0057] As described in "S6. Reducing the color deviation of the illumination light in the target area to zero," in order to accurately capture the color of object 180 when recognizing object 180, it is preferable that the emitted color of object illumination device 160 is a color close to the blackbody radiation locus BBL, and one example is light with a color temperature of 4000K and color deviation Duv=0, as shown at point Z. In other words, if light of a color matched to a different object was irradiated before recognizing object 180, it is preferable (but not essential) to "reset" it to a color that follows the blackbody radiation locus BBL before recognizing the color of the object.

[0058] As described in "S5. Acquiring a Background Image," before recognizing the object 180, the color recognition of the object 180 can be improved by illuminating only the background 182 with light from point Z without placing the object 180 on it, and comparing that image with the image of the object 180. In this case, it is preferable that the background 182 be an achromatic color such as gray.

[0059] Returning to "S11. Illuminate the target area with illumination light according to the recognition results," if the recognized object is "bread" or "sweets," the chromaticity coordinates of the illumination light will be point N1 or N2. If the object is "beef," the chromaticity of the illumination light will be point M2. If the object is pork, the chromaticity of the illumination light will be point M1. If the object is blue fish (sardines, mackerel, etc.), the chromaticity of the illumination light will be point F. If the object is leafy vegetables, the chromaticity of the illumination light will be point G. If the object is a yellow lemon, the chromaticity of the illumination light will be point L.

[0060] These illumination chromaticities are merely examples, and other illumination chromaticities may be used depending on the type and condition of the food. For example, even for red foods, it is preferable to finely change the illumination chromaticity depending on whether the food is apples, strawberries, beef, salmon roe, wine, etc. Even for green foods, it is preferable to finely change the illumination chromaticity depending on whether the food is melon or leafy vegetables. Even for yellow foods, it is preferable to finely change the illumination chromaticity depending on whether the food is lemon, banana, or egg dish. Furthermore, because the illumination chromaticity is influenced by personal preferences, it is preferable that it can be adjusted by users such as supermarket sales staff. In this case, adjustments can be made based on sales trends, but adjustments can also be made taking into account the age, gender, eye color (race), and income level of major customers.

[0061] This camera-linked lighting control system can be used not only for lighting food products, but also for clothing, car bodies, stationery, and other products where color is important, by using lighting with a chromaticity that brings out the product's appeal, which can lead to increased sales.

[0062] In "S9. Comparison of background image of target area with target image," if camera 110 determines that target object 180 does not exist, it is preferable to reduce the total luminous flux of the illumination light from target object lighting device 160 to less than the total luminous flux when target object 180 exists (for example, to one-tenth), or to turn off the light. The purpose of this is not only to reduce power consumption, but also, if the target object is a product, to visually indicate that the product is sold out.

[0063] Whether or not the object 180 exists is determined, for example, as follows. First, a background image without any target object is captured in advance, and the target area is cut out. Then, the feature values / vectors are calculated in the target region, and the calculation results are saved as A. Then, for example, images of the target area are taken at regular time intervals during business hours, and the target area is cut out. Then, the feature values / vectors are calculated in the target region, and the calculation results are saved as B. B and A are compared to determine whether the difference is greater than a preset threshold. If the difference between B and A is not greater than the threshold, it is determined that B and A are similar, i.e., there is no target object.

[0064] AKAZE (Accelerated KAZE) feature detection can be used to calculate features / vectors. This is a method of matching by converting the input image to monochrome (an image containing only the V values ​​of HSV (Hue, Saturation, and Value)). Using an image containing only V has the advantage of reducing the amount of processing and absorbing color changes in the background image due to lighting color.

[0065] AKAZE feature detection cannot accurately calculate similarity for images with no pattern or roughness, so the following image correlation method can also be used.

[0066] Calculate the average values ​​Aave and Bave of the pixel information of A and B. This is used to correct the brightness of the background image and the target image. Next, the average value of B*Ave / Bave-A = E is taken as the similarity. In terms of meaning, it is the sum of the differences between each pixel after brightness correction. The presence or absence of an object is determined as follows: if E<threshold (images are similar), it is determined that there is no object; if E≧threshold (images are different), it is determined that there is an object.

[0067] These algorithms have been described as an example of determining whether an object exists, but they can also be used to determine what the object is.

[0068] In "S10. Object Recognition," the K-means method can be used to extract representative colors from the image data of the object and recognize what the object is based on the representative colors. This is a method in which each pixel of the image plotted in a color space such as RGB or HSV is classified into N color regions (N is 2 or more), and the average value of the region with the most pixels in the classified color space is used as the representative color.

[0069] In such cases, the representative color may be the color of the background, tray, plate, label, or other object, but it is desirable to perform processing to remove such representative colors from the candidates for the object's color.

[0070] As a result, if the object is beef, for example, it can be determined that its representative color is closer to the representative color of beef than the representative color of an apple that has been acquired in advance, and the object can be identified.

[0071] As described above, it is desirable to match the chromaticity of the illumination light to the color of the object. In particular, it is preferable to control the chromaticity of the illumination light so that, if the object is red, the color deviation Duv of the illumination light is negative, and if the object is green or yellow, the color deviation Duv of the illumination light is positive. In other words, it is preferable to change the chromaticity of the illumination light in the same direction as the color deviation of the object color, depending on the color deviation, which is the difference between the chromaticity coordinates of the object and the chromaticity coordinates on the blackbody radiation locus that are closest to those chromaticity coordinates. Such chromaticity of the illumination light emphasizes the hue of the object, highlighting the deliciousness of food and the vividness of other objects, which can lead to increased sales.

[0072] It is advisable to use the chromaticity of the illumination light not as the color of the object itself, but as a chromaticity on the blackbody radiation locus that is slightly closer to the color of the object. For example, the chromaticity coordinates of the above-mentioned "leafy vegetables" are (0.35, 0.50), which is green, but the chromaticity of the illumination light for G is (0.37, 0.40), which is closer to the blackbody radiation locus. This way, people who view the object will perceive the color of the object as vivid when illuminated with normal white light. If the object is illuminated with its color, even a white tray containing the object will appear to be the color of the object, resulting in very unnatural coloring.

[0073] In S11, the total luminous flux of the object lighting device 160 may be controlled (dimmed) in addition to adjusting the chromaticity. The camera 110 can detect whether the object 180 is bright or dark in the image of the object area 183, and can dim the object lighting device 160 that illuminates the object area so that the object 180 has an appropriate brightness.

[0074] <Light source of lighting device> The light source used for the object illuminator 160 is one that can vary the color deviation Duv. Typical lighting devices include fixed-color lighting devices that use a single-color light source and color-adjustable lighting devices that use two-color light sources and have variable color temperatures, but neither of these has the function of varying the color deviation Duv. Suitable object illuminators 160 have light sources of three or more colors, such as those that use the three primary colors R, G, and B (red, green, and blue). Also suitable are lighting devices that use the three colors R, Yw, and Bw (red, yellow-white, and bluish-white), as described below.

[0075] Figure 6 shows a chromaticity diagram that explains the chromaticity of the three colors R·Yw·Bw (red, yellowish-white, and bluish-white). The R-LED, which is a light-emitting element for the primary color red (R), the Bw-LED, which is a light-emitting element for the non-primary color bluish-white (Bw), and the Yw-LED, which is a light-emitting element for the non-primary color yellowish-white (Yw), are used. For red (R), the chromaticity range is (0.66, 0.23), (0.423, 0.355), and (0.5, 0.5) in the CIE1931 chromaticity coordinates shown in Figure 6, and the chromaticity range is the range enclosed by the chromaticity boundary line E. For bluish-white (Bw), the CIE1931 chromaticity coordinates are (0.66, 0.23), (0.423, 0.355), and (0.5, 0.5). For blueish-white (Bw), the CIE1931 chromaticity coordinates are (0.66, 0.23), (0.423, 0.355), and (0.5, 0.5). In terms of coordinates, the color in the chromaticity range enclosed by (0.336, 0.24), (0.352, 0.44), (0.15, 0.2), and (0.2, 0.1), and yellow-white (Yw), is preferably a color in the chromaticity range enclosed by (0.5, 0.5), (0.423, 0.355), (0.342, 0.312), (0.352, 0.44), and (0.37, 0.63) in the CIE 1931 chromaticity coordinates and the chromaticity boundary line E. This makes it possible to reproduce a wide range of colors, for example, colors along the blackbody radiation locus, such as 1800K to 12000K, and colors in a practical range, such as the colors of blue skies and sunsets, while also allowing Duv to be varied. Furthermore, by using the three colors R, Yw, and Bw, the visual sensitivity of Bw (bluish-white) light is higher than that of B (blue) light, which is closer to the chromaticity boundary line. This has the advantage of making it possible to achieve a relatively good power-to-luminous flux conversion efficiency (lm / W) compared to when light-emitting elements of the three primary colors of R, G, and B are used.

[0076] <Embodiment 2> <Application> A camera-linked lighting control system 100B of embodiment 2 is shown in Fig. 7. This is a system that photographs an object with a camera and illuminates it with light of a color appropriate to the color of the object, and is configured by adding general lighting devices 170A and 170B (collectively referred to as general lighting device 170) to camera-linked lighting control system 100.

[0077] <Configuration> A general lighting device 170, which is a new component of the camera-linked lighting control system 100B, illuminates the entire room, and is installed on a ceiling 190 above a passageway, for example.

[0078] The second lighting control device 150 controls the general lighting device 170 to perform a scheduled operation in which the color temperature and dimming rate change according to the time of day. A graph illustrating an example of scheduled operation is shown in FIG. 8, but for simplicity, only three color temperature changes (A, B, and C) are shown. It is desirable to change the time of day when the color temperature and dimming rate change, such as at sunrise or sunset. However, FIG. 8 merely illustrates an example of a specific season that does not take such changes into consideration. In the example, during time period A (morning), the color temperature is increased to 5000 K (point Z(C) in FIG. 9, described later) and the dimming rate is set to 100% for bright illumination. During time period B (daytime), the color temperature is slightly decreased to 4000 K (point Z) and the dimming rate is set to 90%. During time period C (evening), the color temperature is slightly decreased to 3000 K (point Z(W)) and the dimming rate is set to 80%.

[0079] In this embodiment, when the color temperature of the general lighting device 170 is changed, the color temperature of the object lighting device 160 for the object is also changed in conjunction with the color temperature of the general lighting device 170. Figure 9 is a chromaticity diagram for explaining the chromaticity control of the general lighting device and the object lighting device.

[0080] When the chromaticity of the illumination light from the general illumination device 170, which is the overall illumination, is Z(C), Z, and Z(W), the chromaticity of the illumination light from the object illumination device 160, which is the object, is set to N2(C), N2, and N2(W), respectively. This reduces the difference in chromaticity between the illumination light from the general illumination and the illumination light from the object illumination. Note that the correlated color temperature (T CCT ) does not need to change in the same direction, but rather it is sufficient to change in approximately the same direction.

[0081] <Embodiment 3> <Summary> In this embodiment, experiments are conducted in advance to determine the correlated color temperature that humans perceive as delicious for different types of food as an object, and the results are stored in camera and lighting control device 130. Camera-linked lighting control system 100 then photographs the actual food as object 180 with camera 110, performs pixel analysis, and controls object lighting device 160 to a correlated color temperature that humans perceive as delicious. Therefore, although the control content differs from embodiment 1, the same hardware as in Figure 1 is used, and therefore the same reference numerals as in embodiment 1 are used for each element.

[0082] <Application> This system can be used to adjust lighting according to the type of food or prepared dish in the prepared food section of a supermarket, and also to adjust lighting according to the type of food in a restaurant, which can stimulate customers' desire to purchase and increase customer satisfaction.

[0083] <Experiment on correlated color temperature that makes food taste delicious> In an experiment to determine the correlated color temperature perceived as desirable by humans, four subjects were shown 10 different dishes (including dishes with unprocessed ingredients, such as salads containing carrots, tomatoes, corn, and leafy vegetables). The correlated color temperature of the lighting shining on the dishes was varied, and the subjects' electroencephalograms were measured. Each subject's electroencephalogram was used to determine whether the dishes appeared delicious based on their appearance. The electroencephalogram measurements were performed according to the instructions of experts, and the judgments were also entrusted to experts. In addition, photographs of each dish were taken using automatic exposure, and the average and standard deviation of the R, G, and B values ​​of the pixels were analyzed. The R, G, and B values ​​of a pixel each range from 0 to 255. The correlated color temperature perceived as delicious was then expressed as a function of the RGB values ​​of the pixel.

[0084] Figure 12 shows the relationship between the correlated color temperature of the lighting that makes food taste delicious, as determined by the results of this experiment. The horizontal axis represents the correlated color temperature of the lighting, and the photographs of food and their corresponding correlated color temperatures that make them taste delicious are shown. The relationship between the image data of food and the correlated color temperature of lighting that makes food taste delicious, obtained in this experiment, is as follows:

[0085]

number

[0086] This method does not identify the type of food and set a correlated color temperature according to the type of food, but rather adjusts the correlated color temperature based on pixel analysis of the food. Therefore, it is possible to set a correlated color temperature for lighting that makes any food, even food without a name, taste delicious. Note that this method can also be used in conjunction with a method that identifies the type of food and sets lighting with a correlated color temperature that is predetermined for that food, which allows for more stable lighting control in places such as restaurants where the type of food is fixed.

[0087] Each parameter in this formula is an experimental value, so it will change depending on the dish and the subject, but we found that the following trends can be observed.

[0088] In the case of a dish with a large pixel R value in the photographed area (when Rave is large), for example, a dish with a large area of ​​red or brown, the Rave coefficient is negative, so a low color temperature tends to be preferred.

[0089] In the case of food with a high pixel G value in the photographed area (when Gave is large), such as green leafy vegetables, the Gave coefficient is positive, so a high color temperature tends to be preferred. This is a new finding that emerged from this experiment. Previous predictions had been that the effect of the amount of "green" in the pixels on color temperature would be relatively neutral, meaning the green coefficient would be small.

[0090] In the case of dishes with a high pixel B value in the shooting area (when Bave is large), the coefficient of Bave is negative, so no tendency for high color temperatures to be preferred for dishes with a high blue component was found. This is also a surprising result, but it may be related to the fact that many dishes do not appear blue, and that Bave can be high for dishes with high R and G values ​​in addition to B (for example, white rice).

[0091] The coefficients for Rsd, Gsd, and Bsd, which indicate the standard deviation (variation) of the R, G, and B values ​​of the pixels in the photographed area, were positive for Rsd, negative for Gsd, and slightly positive for Bsd. Therefore, it was found that objects with large variations in the R values ​​of each pixel in the photographed area (large Rsd) prefer a positive correlated color temperature, while objects with large variations in the G values ​​of each pixel in the photographed area (large Gsd) prefer a negative correlated color temperature. However, with colorful dishes, Rsd, Gsd, and Bsd all tend to be large, which can result in some negation of the effect on CCT.

[0092] <Lighting control to achieve correlated color temperature that tastes delicious> In the camera-linked lighting control system 100 shown in Figure 1, the above formula is entered into the camera and lighting control device 130, or into a computer or server accessible by the camera and lighting control device 130, and an image of the food, which is the object 180, is captured by the camera 110, and the object region 183 is extracted. In this case, it is preferable to extract only the area where the food is present, for example, the plate portion, as the object region 183. However, it is also possible to use the entire prepared food section of a supermarket or the entire table in a restaurant as the object region 183, and exclude background pixels other than the object. Methods for excluding background pixels include capturing an image of the background in advance to recognize the characteristics of the background pixels, or manually setting the chromaticity range of the background pixels.

[0093] Derive the average value and standard deviation of the RGB values for each pixel in the target area 183, preferably excluding the background, apply them to the above formula, and control the correlated color temperature of the object lighting device 160. By performing such control in a restaurant or a general food corner, it is considered possible to make the dishes look delicious, contributing to an increase in customer satisfaction and sales.

[0094] Note that the above formula is merely an example. The key point of this embodiment is to analyze the color components of the pixels in the target area and perform chromaticity control including controlling the correlated color temperature of the illumination light based on the analysis results. In this regard, the sign (plus or minus) of each coefficient in the above formula is also a relatively important point.

[0095] In the above description, it is assumed that a camera with a primary color system filter of either R (red), G (green), or B (blue) is used for each pixel. However, a camera with a complementary color system filter of any of the three colors C (cyan), M (magenta), Y (yellow) or the four colors C, M, Y, G may also be used for each pixel.

[0096] <Example of changing Duv together> In the above experiment, only CCT was changed, but a preliminary experiment to change Duv together has also been started.

[0097] As a method of changing Duv, there is a method of making it stepwise联动 with CCT. For example CCT < 3,500 → Duv = -6 3,500 ≤ CCT < 4,000 → Duv = -3 4,000 ≤ CCT < 5,000 → Duv = 0 5,000 ≤ CCT < 5,500 → Duv = 3 5,500 ≤ CCT → Duv = 6 This is a method of changing Duv stepwise to the negative side when CCT is small and to the positive side when CCT is large.

[0098] As a method of changing Duv, there is also a method of making it continuously联动 with CCT. For example CCT<3500 → Duv=-6 3500≦CCT<5500 → Duv=(CCT-3500)*12 / 2000-6 5500≦CCT → Duv=6 This is a method of continuously changing Duv to the negative side when CCT is small, and to the positive side when CCT is large.

[0099] Either method makes it clear how the lighting changes appearance as the dishes change, making the presentation more appealing.

[0100] The above values ​​are just examples. For example, if Duv is on the positive side, it may be objectionable that the object has a slight greenish cast. Therefore, the Duv value may be shifted from the above value, such as by making Duv negative at low CCTs and zero at high CCTs.

[0101] <Embodiment 4> <Basic configuration> 13 shows a configuration diagram of a camera-linked lighting control system 300 according to this embodiment. The camera-linked lighting control system 300 includes a camera 310, a camera / lighting control device 350, an object lighting device 360, and an object 370. The object lighting device 360 ​​is a spotlight, and includes a lamp body 361, a power supply unit 380, and an arm 375 that connects the lamp body 361 to the power supply unit 380.

[0102] <Camera> Camera 310 installed on ceiling 390 is connected to camera and lighting control device 350 via control signal 312. Camera 310 captures an image of object 370, and the image is used to recognize the object within the target area in the image or the chromaticity (color distribution) of the object by an image recognition function within the camera, an external server, or camera and lighting control device 350.

[0103] <Camera and lighting control device> The camera and lighting control device 350 is composed of a tablet, smartphone, or PC, and runs lighting control software 351. The lighting control software 351 adjusts the light emission intensity of the three color LEDs according to the object or the chromaticity of the object photographed by the camera 310 and recognized by the image recognition function. The light emission intensity conditions of the three color LEDs are transmitted as wireless control signals 352.

[0104] <Light body> 14 is an explanatory diagram showing the interior of a lamp body 361 surrounded by a frame 367. The lamp body 361 comprises a heat sink 364, a light source 320 mounted thereon, a lens 368, and a frame 367. As shown in FIG. 13, light emitted from the light source 320 is emitted by the lens 368 as illumination light 362 at a predetermined radiation angle, and illuminates an object 370.

[0105] <Power supply section> 13, power supply unit 380 is attached to wiring duct rail 395 installed on ceiling 390 by front attachment part 386 and rear attachment part 388, and is supplied with power (the inside of the wiring duct rail is not normally visible, but in this drawing the wiring duct rail is shown semi-transparent so that the attached state of power supply unit 380 can be seen). Attachment is performed by turning lever 385.

[0106] The power supply unit 380 has a slot for inserting a wireless module 382 , and the wireless module 382 transmits a wireless control signal 352 sent from an external camera / lighting control device 350 to the power supply unit 380 .

[0107] The power supply unit 380 has three channels of drive output for independently driving the three color LEDs. The wireless module 382 transmits a control signal to the power supply unit 380, thereby controlling the drive outputs of the three channels.

[0108] <Light source> A plan view of the light source 320 used in this embodiment is shown in FIG. 15. The light source 320 is formed by arranging three-color light-emitting elements in a light-emitting region 322 on a substrate 321 having a wiring pattern. As the light-emitting elements, 17 CSP (Chip Size Package) type LEDs 323-1 (E), CSP type LEDs 323-2 (D), and CSP type LEDs 323-3 (F) are arranged as shown in FIG. 15 and are connected in series by wiring inside the substrate. The light source 320 has a mounting hole 325, and is fastened to the heat sink 364 by passing a screw through the mounting hole 325. The light source 320 includes wiring terminals 326-1 (for CSP type LED 323-1), wiring terminals 326-2 (for CSP type LED 323-2), wiring terminals 326-3 (for CSP type LED 323-3), and a wiring terminal 326-0 (common terminal), and is connected to a power supply unit 380 having three outputs, and the light emission of each LED is controlled.

[0109] <Structure of LED> The CSP type LED 323-1, CSP type LED 323-2, and CSP type LED 323-3, which are light-emitting elements used in the embodiment, include a blue LED chip made of InGaN or the like, and a phosphor-containing resin that covers the side surface and the upper surface of the blue LED chip, and have terminals for passing current through the bottom surface or the side surface of each LED. As the phosphor, one or several of a green phosphor that absorbs the light of the blue LED and emits green light, a yellow phosphor that emits yellow light, and a red phosphor that emits red light are used.

[0110] In each of the above LEDs, as the yellow phosphor, for example, (Y 1-x Gd x )3Al5O 12 :Ce 2+ (0≦x≦1) is used. As the green phosphor, for example, Lu3Al5O 12 :Ce 2+ is used. As the red phosphor, for example, Sr x Ca 1-x AlSiN3:Eu 3+ (0≦x≦1) phosphor, Sr[LiAl3N4]:Eu 2+ or K2SiF6:Mn 4+ phosphor can be preferably used. Quantum dots can also be preferably used.

[0111] <Chromaticity of LED> FIG. 16 is a chromaticity diagram (CIE1931 chromaticity coordinate diagram) for explaining the chromaticity of these LEDs, and a line connecting the chromaticity of the blackbody radiation locus is shown as BBL. Also, the correlated color temperature T CT on the BBL, which is the chromaticity that appears to be closest to the color, and a line representing the color deviation Duv from the BBL are shown. CCT The chromaticities of the CSP type LEDs 323-1, 323-2, and 323-3, which are the light-emitting elements used in this embodiment, are "E" (LED for meat), "D" (LED for bread), and "F" (LED for fresh fish) in FIG. 16, respectively. However, "for meat, for bread, for fresh fish" are merely examples, and "E" is also suitable for various objects such as red fruits, and "D" is suitable for fried foods.

[0112] The chromaticity coordinates of the CSP type LED 323-1 (D) are (0.458, 0.407). The chromaticity coordinates of the CSP type LED 323-2 (E) are (0.412, 0.327). The chromaticity coordinates of the CSP type LED 323-3 (F) are (0.358, 0.342). The correlated color temperature T CCT (unit: K (kelvin)) is approximately 2700K for E and D, approximately 4500K for F, and the difference is approximately 1800K.

[0113] The color deviation Duv from the BBL is approximately -1 for D, approximately -31 for E, and the difference is approximately 30. Also, for all three of these LEDs, the color deviation Duv is on the negative side of the blackbody radiation locus (BBL = Black Body Locus).

[0114] , In addition, the chromaticity of each LED can also be expressed in terms of the chromaticity coordinates (u', v') in CIE1976 instead of the chromaticity coordinates (x, y) in CIE1931, and the two can be mutually converted by the conversion formula u' = 4x / (-2x + 12y + 3), v' = 9y / (-2x + 12y + 3). It may also be expressed in other chromaticity coordinate systems.​

[0115] In particular, the chromaticity of the CSP LED 323-2 is characterized by a color that is far from the blackbody radiation locus (BBL), with a color deviation Duv of -31, which is a large absolute value. By combining the CSP LED 323-2 with the CSP LED 323-1 and CSP LED 323-3, which are relatively close to the blackbody radiation locus, the color deviation can be adjusted appropriately.

[0116] By appropriately combining the light from the light-emitting elements CSP type LED 323-1(D), CSP type LED 323-2(E), and CSP type LED 323-3(F), it is possible to obtain light of the chromaticity of the triangle and the chromaticity inside it in Figure 16. For example, the colors of the points indicated as DE, EF, and EF in the figure can be suitably used to match the food ingredients.

[0117] Conventionally, color-adjustable lighting, which combines multiple colors, has been used to change color temperature. By changing the "color temperature" along the blackbody radiation locus, color-adjustable lighting can adjust between "cool, high color temperature, fluorescent light color" and "warm, low color temperature, incandescent light color," achieving lighting that feels natural to people. Unlike such "living space" lighting, this example uses lighting that changes the "color deviation from the black body light," which feels strange to people, to create lighting that brings out the color of the object.

[0118] The light-emitting elements CSP LED323-1(D), CSP LED323-2(E), and CSP LED323-3(F) also have distinctive spectra. Figure 17 shows the spectra of D and F, and Figure 18 shows the spectra of D and E after passing through an optical system such as lens 368. Each graph is normalized by the maximum value of G (green) in the wavelength range of 490 to 570 nm. Unlike the composite of RGB monochromatic light (described below), the spectrum of each light-emitting element contains spectral components across the entire wavelength range. However, the maximum value of R (red) in the wavelength range of 600 to 660 nm > the maximum value of G (green) in the wavelength range of 490 to 570 nm > the maximum value of Y (yellow) in the wavelength range of 570 to 600 nm. The Y spectrum is suppressed (the maximum value of Y is smaller than the maximum values ​​of R and G), emphasizing the red color. Therefore, the spectrum of the composite of D, E, and F also has an R>G>Y relationship.

[0119] Figure 17, which compares the spectra of D and F by normalizing the light intensity by the maximum value of G, shows that F, which has a high color temperature, weakens the R component and strengthens the B component compared to D, which has a low color temperature.

[0120] Figure 18 compares the spectra of D and E by normalizing the light intensity by the maximum value of G. It can be seen that E, which has a large absolute value on the negative side of Duv, strengthens the B and R components compared to D, which has a small absolute value of Duv.

[0121] It should be noted that the vertical axis in Figures 17 and 18 represents light intensity and does not take luminosity into account. The luminosity coefficients in the CIE 1931 relative luminosity curve are 0.06472 at a wavelength of 450 nm (B), 0.95447 at a wavelength of 540 nm (G), 0.89636 at a wavelength of 580 nm (Y), and 0.29803 at a wavelength of 630 nm (R), with the G and Y coefficients being close to 1 and the B and R coefficients being small. Therefore, the G and Y components have a large impact on the overall appearance of color.

[0122] Taking this into consideration, in the spectrum of each color, the maximum value of Y (a value that does not take luminosity into account) should be 0.25 or more, and preferably 0.5 or more, with the maximum value of G being 1. On the other hand, it is desirable that the maximum value of Y is below the line connecting the maximum values ​​of G and R, so that the Y component is suppressed, and it is more desirable that the maximum value of Y is below the maximum value of G.

[0123] Figure 19 will be used to explain the difference between the method using light-emitting elements of the three colors D, E, and F, which have distinctive spectra, and the RGB (red, green, blue) method, which can theoretically produce all colors. In Figure 19, R is red, G is green, and B is blue. Since all colors can be obtained using RGB, one might wonder if using the three colors D, E, and F makes no difference to the RGB method. However, as shown in Figure 19, the area enclosed by the three colors D, E, and F is extremely small compared to the area enclosed by the three RGB colors.

[0124] FIG. 20 shows a schematic diagram illustrating color unevenness. Light source 320′ consists of three color LEDs (E, F, and G) and illuminates white object 370′ through lens 368′. Because the light from the three LEDs does not completely overlap, areas indicated as D-rich, E-rich, and F-rich result. If an "RGB" primary color light source were used, these areas would be R-rich, G-rich, and B-rich. However, because R, G, and B are different primary colors, color unevenness would be noticeable. In the present invention, by using three color LEDs with a limited chromaticity range instead of a primary color light source, the chromaticity difference in areas where the light does not overlap sufficiently is small to begin with, thereby essentially reducing color unevenness.

[0125] By using D, E, and F lighting, which contain not only R, G, and B components but also a moderate Y component and have spectra that are naturally suited to object lighting, or by using lighting with a mixture of these colors, the camera can identify a variety of foods, such as red and yellow fruits, green vegetables, red meat, sashimi, and salmon, blue-backed fish, and brown bread, baked goods, and fried foods, and then provide lighting appropriate for each food. Although this object lighting system was developed with food ingredients in mind, it has also been shown to produce beautiful results when used to illuminate a variety of objects, including clothing, car bodies, furniture, accessories, and flowers. However, in some cases, a color may appear beautiful in the store but different at home, so it is preferable to adjust and add light such as E, which has a large absolute color deviation from the BBL. This object lighting system is advantageous because it allows for fine-tuning of the amount of color deviation, or color appearance, through "color mixing."

[0126] Furthermore, in light source 320 shown in FIG. 15, the chromaticity difference between the three color LEDs arranged in light-emitting area 322 is small, which has the advantage that color unevenness when irradiated onto an object is small and almost unnoticeable.

[0127] The correlated color temperature T of each light-emitting element used in the present invention CCT The temperature is 2000K or higher, preferably 2500K or higher, and 6000K or lower, preferably 5000K or lower.

[0128] The color deviation Duv (1000 times the color deviation duv) from the BBL of each light-emitting element used in the present invention is preferably -50 or more (absolute value 50 or less), preferably -40 or more (absolute value 40 or less), and +20 or less, preferably +10 or less. The difference in color deviation between two light-emitting elements with the largest difference in color deviation is preferably 50 or less, and more preferably 40 or less. By using light-emitting elements with such limited color deviation, color unevenness can be reduced. On the other hand, in order to accommodate objects of various colors, the color deviation between two light-emitting elements with the largest difference in color deviation is preferably 10 or more, preferably 20 or more, and more preferably 30 or more.

[0129] The correlated color temperature T CCT In order to accommodate objects of various colors, the difference should be 400 K or more, preferably 800 K or more, and more preferably 1200 K or more. On the other hand, from the viewpoint of reducing color unevenness, the difference should be 3000 K or less, and preferably 2000 K or less.

[0130] <Embodiment 5> <Basic configuration> 21 shows object lighting device 560 used in the camera-linked lighting control system according to this embodiment. Object lighting device 560 is a spotlight, and comprises a lighting body 561, a power supply unit 580, and an arm 575 that connects lighting body 561 to power supply unit 580, and can be connected to a wiring duct rail. To wirelessly control the emitted light color, wireless module 582, which is a wireless transceiver that can be attached to power supply unit 580, is provided, and the emitted light color is changed using camera-lighting control device 350 connected to camera 310.

[0131] <Light body> 22 is an exploded perspective view of the lighting body 561. The lighting body 561 includes a heat sink 564, a printed circuit board 521 placed on the heat sink 564, a multi-lens 566, and twelve lenses 568 integrally molded within the multi-lens. Twelve surface-mounted LEDs 523 are mounted on the printed circuit board 521, and each of the surface-mounted LEDs 523 corresponds to a lens 568.

[0132] <Light source> FIG. 23 is a chromaticity diagram for explaining the chromaticity of an LED. The surface-mounted LED 523 has B27 (2700K), A27 (T CCT =2700K, Duv=-10), B46(4600K), A46(T CCT = 4600K, Duv = -10). This allows the color of the chromaticity within the square shown in Figure 23, for example, AW30 (T CCT =3000K,Duv=-4), AW35(T CCT =3500K,Duv=-5),AW42(TCCT = 4200K, Duv = -5).

[0133] In Figure 23, an example of a spectrum obtained by combining each color is AW35, or T CCT Figure 24 shows the composite spectrum at 3500K. The blue component around 450 nm is represented by B, the green component around 550 nm by G, the yellow component around 590 nm by Y, and the red component around 630 nm by R. In the clothing industry, to ensure natural color appearance, it is preferable to have light across the entire spectral range and a color rendering index (Ra) of 90 or higher, with 93 or higher and even 95 or higher being preferable. Given the preference for whitish colors with less yellowing in the clothing industry, the Y component is slightly reduced and the R component is increased compared to the typical high-color-rendering LED shown by the dotted line in Figure 23. As a result, the color temperature does not change significantly, and the color deviation (Duv) is approximately -5. The half-width (FWHM) shown by the dotted line is approximately 180 nm, but it is preferable that it be 150 nm or higher. In particular, when the R peak is 100%, Y (590 nm) should be 50% or higher, preferably 60% or higher, and more preferably 70% or higher.

[0134] Here, the difference in color deviation Duv between the first light-emitting element, designated B, and the second light-emitting element, designated A, is preferably 5 or more, and more preferably 10 or more. On the other hand, for objects that are commonly seen under ordinary lighting, such as clothing, car bodies, furniture, small items, and flowers, the difference in Duv is preferably 15 or less, so that there is little sense of incongruity when illuminated with this object lighting device and when illuminated with a normal object lighting device. For food products, the difference in Duv should be 15 or more, with an emphasis on how the colors look in the store, making it preferable to make meat and red fruit look particularly attractive.

[0135] As an example of controlling chromaticity in clothing, first analyze the clothing category or chromaticity distribution based on the color trends used in clothing photographed by a camera. It is generally considered best to control lighting to a higher color temperature for summer and casual clothing, and a lower color temperature for winter and luxury clothing. Furthermore, to emphasize red, it is best to set DuV to a negative side. It is desirable to fine-tune the control program for this type of camera-linked lighting control in accordance with the clothing store's brand image.

[0136] <Embodiment 6> <Application> The configuration of a camera-linked lighting control system 600 of this embodiment is shown in Fig. 25. This is a camera-linked lighting control system that uses a camera to capture an image of clothing as an object and illuminates the object with light of a color tone suited to the color of the object.

[0137] <Configuration> The camera-linked lighting control system 600 includes a camera 610 installed on a ceiling 690, a camera and lighting control device 630, an object lighting device 660, a gateway 655, a camera control device monitor 632 at a person's hand, and the like.

[0138] <Camera and camera / lighting control device> The camera 610 captures an image 613 of an object 680 within its field of view 612 and transmits it to the camera and lighting control device 630 via communication line 617 .

[0139] The camera and lighting control device 630 is a device that has camera control functions and lighting control functions, and also has an image recognition function. These functions are realized by a computer (hardware) and software. As an output device, for example, a camera control device monitor 632 is provided, which displays the image 613 and control contents. The camera and lighting control device 630 is connected to a gateway 655 via a communication line 657.

[0140] <Gateway> The gateway 655 transmits the lighting control signals to the object lighting device 660 via wireless communication 654 .

[0141] <Object illumination device> The object illuminator 660 is, for example, a spotlight, and illuminates an object 680 (here, object 680F, which is clothing) in an object area 683 with illumination light 662. The object illuminator 660 can change the chromaticity of the illumination light, and in particular, can change the color deviation Duv, which is a factor other than color temperature. Such control of the chromaticity of the illumination light is performed using the camera and illumination control device 630.

[0142] <Operation overview> The operational flowchart of the camera-linked lighting control system 600 is shown in Figure 26. In the first step, the lighting device is turned on to illuminate the object. In the second step, the object is photographed with a camera and an image is acquired. In the third step, image segmentation, as explained below, is performed to extract the object image region. In the fourth step, the colors within the object image region are divided using four-way clustering (dividing into four groups of similar color). In the fifth step, the average image of the object image region is calculated by taking a weighted average of the chromaticity and area of ​​each cluster. In the sixth step, the object is illuminated with illumination light corresponding to the average chromaticity of the object image region (this is referred to as the "chromaticity of the object image region" or "object color").

[0143] <Identifying object image regions using image segmentation> Image segmentation, an AI technology, is used to identify the area of ​​clothing, which is the object. Figure 27 is an explanatory diagram of image segmentation. Figure 27(a) shows a portion of an image captured by camera 610. From this captured image, the area of ​​the object reflected in the image is extracted as object image area F in Figure 27(b), and the object is identified for the part identified by object image area F. To achieve high prediction accuracy in image segmentation, training data is required. Here, training to recognize the shape of clothing is performed in advance, and the image processing device (a software function included in the camera and lighting control device 630, a software function on an external server accessed by the camera and lighting control device 630, or an independent dedicated image processing device) refers to the training data to identify which parts of the image are clothing and defines them as the object image area F.

[0144] As will be described in detail in embodiment 7, by referring to the learning data, it is possible to not only identify the object as clothing but also identify the type and color of the clothing, such as a white shirt, a navy blue jacket, a red cardigan, or red and blue pants, and the lighting system may irradiate lighting according to the identified type and color of the clothing. The learning data used in this case may be stored in the camera and lighting control device 630 equipped with an image recognition function, or may be stored in an external server.

[0145] <Identifying the color of objects> An object image region F determined to be "clothing" may include, for example, the entirety of a blue jacket and a white shirt. Therefore, multi-division clustering is performed to classify the region into groups with similar hues, such as F1 and F2 in Figure 27(b), and the average chromaticity is calculated. The number of divisions in multi-division clustering is preferably between 2 and 6, and in this study, four divisions were used. With current technology, a division of 6 or less tends to result in faster processing speeds and fewer classification errors, as the amount of information is not excessively large.

[0146] For simplicity, we will explain this as a two-division clustering. The chromaticity coordinates (x1, y1) of the first color and the number of pixels a1, which is its area, are calculated, and the chromaticity coordinates (x2, y2) of the second color and the number of pixels a2, which is its area, are calculated. From these, the average chromaticity is (x f , y f )=(a1(x1,y1)+a2(x2,y2)) / (a1+a2) It is calculated as:

[0147] This "multi-segment clustering" is the same technology as the one described as the K-means method, which, in the previous "S10. Object Recognition", classifies each pixel of an image into N (N is an integer greater than or equal to 2) color regions from the image data of the object, extracts a representative color with the average value of the region with the largest number of pixels existing in the classified color space as the representative color, and recognizes what the object is from the representative color. Here, since the object image area is narrowed down using AI in advance and the representative color is extracted for it, the object can be judged more appropriately.

[0148] <Illumination of chromaticity according to the object> The camera-illumination control device 630 uses the reference chromaticity coordinates as (x w , y w ), makes the color of the illumination light closer to the color of the above-mentioned clothes slightly more than white (x f , y f ), for example, a color of chromaticity (x w + k(x f - x w ), y w + k(y f - y w )), where 0 < k ≤ 1, and controls the object illumination device 660. The object illumination device 660 performs illumination of that color. However, if the color of the illumination light obtained here does not change much from the current color of the illumination light, in order to avoid fine changes in the color of the illumination light, it may be decided not to perform illumination control.

[0149] The reference chromaticity coordinates may be, for example, white (0.33, 0.33) or the chromaticity coordinates of the illumination light of the initial illumination. Also, if it is desired to emphasize freshness by illuminating summer clothes with bluish light, for example, the reference chromaticity coordinates may be, for example, (0.38, 0.38) or (0.43, 0.40) etc. which are blackbody radiations of about 4000K.

[0150] In the above example, the chromaticity coordinates according to CIE1931 were used, but since CIE1931 is a coordinate system where the difference in coordinates and the color difference felt by people are different, other chromaticity coordinate systems, for example, the uv coordinate system according to CIE1960 or the u'v' chromaticity coordinate system according to the aforementioned CIE1976 may be used. That is, the reference chromaticity coordinates are (u‘w , v' w ) and the color of the illumination light is made a color that is slightly closer to the color of the clothing than white, for example, chromaticity (u' w +k(u' f -u' w ), v' w +k(v' f -v' w )), but 0 <k≦1としてもよい。

[0151] <Embodiment 7> <Application> The camera-linked lighting control system 600 of this embodiment is the same as that of embodiment 6, so a description thereof will be omitted. However, the image processing is targeted at "meat 680M in a tray 681T" as shown in Fig. 28. Assuming that the meat 680M is wrapped, a price tag label 681L1 or a label 681L2 indicating a sale or the like may be attached, and these need to be removed by image processing.

[0152] <Identifying object image regions using image segmentation> Image segmentation, an AI technology, is used to identify the area of ​​meat, which is the object. Figure 29 is an explanatory diagram of image segmentation. Figure 29(a) shows a portion of an image captured by camera 610. From this captured image, the area of ​​the object reflected in the image is extracted as object image area M in Figure 29(b). The image processing device identifies what object the part specified by object image area M is. A large amount of training data is required to achieve high prediction accuracy in image segmentation. Here, training to recognize meat is performed in advance, and the image processing device identifies which parts of the image are meat based on the discrimination ability obtained from the training data, and sets them as the object image region M. In this case, the image processing device determines that labels 681L1 and 681L2 are not objects.

[0153] <Identifying the color of objects> For the object image region M determined to be "meat" by image segmentation, the image processing apparatus obtains the average color of the pixels in the region by performing multi-partition clustering as necessary (it may not be performed). The chromaticity coordinates of the average color are set as (x m , y m ).

[0154] <Illumination of chromaticity according to the object> In the first control method, the camera-illumination control device 630 sets the reference chromaticity coordinates as (x w , y w ), makes the color of the illumination light closer to the color of meat than white, for example, the chromaticity (x w + k(x m - x w ), y w + k(y m - y w )), where 0 < k ≤ 1, and controls the object illumination device 660. The object illumination device 660 performs illumination of that color.

[0155] In the second control method, image segmentation and multi-partition clustering performed as necessary are used to identify the types of meat, specifically, lean beef, marbled beef, cut-off pork, pork for cutlets, chicken thigh meat, chicken wing tips, etc. In this case, information on the distribution of red meat and white fat can be used by multi-partition clustering. According to the recognized type of meat of the object, the object illumination device 660 illuminates the object 680 with the chromaticity corresponding to the preset type of meat. The flow in this case is shown in FIG. 30.

[0156] Lighting control that uses a method for identifying the object image area, such as image segmentation, has the advantage that it is not affected by the background area and does not change color even when the object area decreases and the background area in the image increases, for example, when meat is sold and there is little left. For example, Figure 31(a) is a photo of a refrigerated case containing trays of meat, and Figure 31(b) shows the meat area extracted from that photo using image segmentation (image segmentation was performed by dividing the case into upper and lower halves). This method allows the same lighting control to be performed regardless of the number of meat trays. Note that when the number of trays reaches zero, control may be performed such as lowering the dimming rate of the lights, turning off the lights, or changing the lighting color to the standard color (the color before it was used for meat).

[0157] <Modifications and other changes> The above describes an embodiment of the lighting fixture according to the present invention, but the exemplified lighting fixture can also be configured as follows, for example, and it goes without saying that the present invention is not limited to the lighting fixture as shown in the above embodiment.

[0158] (1) The illustration of the object lighting device 160 shown in the figure is based on the image of a spotlight, and the illustration of the general lighting device 170 is based on the image of a square-shaped recessed lighting device, but the form is not limited to other lighting devices, such as downlights, universal downlights with adjustable lighting direction, linear base lights, floodlights, flexible tape lights, etc.

[0159] (2) General lighting devices may be used for indoor lighting as well as outdoor lighting, and installation location is not important.

[0160] (3) When the three primary colors R (red), G (green), and B (blue) are used as the colors of 3CH, blue can be a color that is y<0.4, x<0.2, and is closer to the chromaticity boundary line E than blue-white, and green can be a color that is y>0.4 and is not yellow-white or blue-white.

[0161] (4) The illumination light before object recognition is not limited to the color of point Z (color temperature 4000K). For example, if the object is meat, it may be set to a color on the blackbody radiation locus of 3500K in advance, or if the object is fish, it may be set to 5000K. Note that, with regard to colors on the blackbody radiation locus, if the absolute value of Duv is 1 or less, for example, it may be considered to be a "color on the blackbody radiation locus."

[0162] (5) A lighting control signal may be sent directly from the lighting control device to the lighting device without using a gateway, which is a wireless communication repeater.

[0163] (6) The wireless communication method may be, for example, Bluetooth (registered trademark) or Wi-Fi (registered trademark).

[0164] (7) The camera / lighting control device is a device that combines camera control functions such as capturing images with a camera, image recognition functions such as segmenting target areas and recognizing objects, and lighting control functions such as lighting control based on object recognition. While the present application describes an example in which these three functions are implemented in software on a single computer, these three functions may be handled by separate devices. For example, the function of the "lighting control device" may be provided in a "second lighting control device," or the "image recognition function" may be built into a camera or implemented by, for example, a server on a network (not shown). In other words, the terms "device" and "function" in this specification are not limited to simple hardware, but also refer to a combination of hardware and software, and the location where the function is implemented is not limited to the locations exemplified in this application.

[0165] (8) The light-emitting element is not limited to an LED, but may be any light-emitting element, such as an organic EL (organic LED), an element in which the phosphor and excitation light source are separated, an element that uses discharge, or an element that is excited by an electron beam.

[0166] (9) As for the LEDs of each color, in the fourth embodiment, CSP type LEDs are mounted on a single substrate, and in the fifth embodiment, SMD (Surface Mount Device) type LEDs are mounted on a printed circuit board, but COB type LEDs, in which blue LED chips are arranged on a substrate and multiple blue LED chips are covered with a resin containing phosphor, may also be used. These LEDs can also be used in the first to third embodiments.

[0167] (10) Although examples of three-color LEDs and four-color LEDs have been given as light sources using LEDs of multiple light colors, it is also possible to use only two-color LEDs with different color deviations (for example, D and E, F and E in embodiment 4, and A46 and B27 in embodiment 5).

[0168] (11) The chromaticity of each LED in the embodiment is an example, and other chromaticities may be used.

[0169] (12) Although the example of wireless control has been given as a method for controlling the luminous color of the object lighting device, wired control may also be used.

[0170] (13) In this application, the term "object" is used to include not only "merchandise" but also "non-saleable items" such as art objects and antiques in a museum, and children's paintings in an exhibition.

[0171] It should be noted that the above-described embodiments disclosed herein are illustrative in all respects and are not intended to be limiting. Therefore, the technical scope of the present invention should not be interpreted solely by the above-described embodiments, but should be defined by the claims. Furthermore, all modifications within the scope and meaning equivalent to the claims are included. [Explanation of symbols]

[0172] 100, 100B, 300, 600 Camera linked lighting control system 110, 310, 610 Camera 112, 612 field of view 113,613 images 117, 617 communication lines 130, 350, 630 Camera and lighting control device 132, 632 Camera control device monitor 150 Second lighting control device 152, 154, 654 Wireless communication 155, 655 Gateway 157, 657 communication lines 160, 160A, 160B, 360, 560, 660 Object illumination device 162, 162A, 162B, 362, 662 illumination light 170, 170A, 170B General lighting equipment 180, 180A, 180B, 370, 680, 680F, 680M Object 182, 182A, 182B background 183, 183A, 183B Target Area 184 QR Code (registered trademark) 185 LED signal transmitter 190, 390, 690 ceiling 312, 352 control signals 320 light source 321 Substrate 322 Luminous Area 323 CSP type LED 325 mounting holes 326 Wiring terminal 351 Lighting Control Software 361, 561 light body 364, 564 heat sink 367 slots 368, 568 lenses 375, 575 Arm 380, 580 power supply section 382, 582 Wireless Module 385 Lever 386 Front mounting part 388 Rear mounting part 395 Wiring Duct Rail 521 Printed Circuit Board 523 Surface Mount LED 566 Multi-Lens F, M Object image area

Claims

1. A camera-linked lighting control system including a camera, a device with an image recognition function, a lighting control device, and an object lighting device, Photographing an object with the camera; The device having the image recognition function calculates an average value of the R values, an average value of the G values, an average value of the B values, a standard deviation value of the R values, a standard deviation value of the G values, and a standard deviation value of the B values, based on an R value, which is the red value of each pixel in the original image or the target area image obtained by cutting out the area of ​​the object from the original image or the target area image, a G value, which is the green value of each pixel, and a B value, which is the blue value of each pixel in the original image or the target area image, the lighting control device sets a correlated color temperature according to the average value of the R values, the average value of the G values, the average value of the B values, the standard deviation value of the R values, the standard deviation value of the G values, and the standard deviation value of the B values; the object lighting device irradiates the object with illumination light having the correlated color temperature; Camera-linked lighting control system.

2. the correlated color temperature is a value corresponding to a value obtained by multiplying the average value of the R values, the average value of the G values, the average value of the B values, the standard deviation value of the R values, the standard deviation value of the G values, and the standard deviation value of the B values ​​by coefficients and then adding them together; The camera-linked lighting control system according to claim 1 .

3. A camera-linked lighting control system including a camera, a device with an image recognition function, a lighting control device, and an object lighting device, Photographing an object with the camera; The device having the image recognition function calculates average values ​​of the R value, the G value, and the B value from an original image captured by the camera or a target area image obtained by cutting out the area of ​​the target from the original image, based on an R value, which is the red value of each pixel in the original image or the target area image, a G value, which is the green value of each pixel, and a B value, which is the blue value of each pixel; the lighting control device sets a correlated color temperature according to a value obtained by multiplying the average value of the R values, the average value of the G values, and the average value of the B values ​​by a coefficient, the coefficient by which the average value of the R values ​​is multiplied is a negative value, and the coefficient by which the average value of the G values ​​is multiplied is a positive value, the object lighting device irradiates the object with illumination light having the correlated color temperature; Camera-linked lighting control system.

4. the coefficient by which the average value of the B values ​​is multiplied is a negative value; The camera-linked lighting control system according to claim 3.

5. the lighting control device changes the color deviation in conjunction with the correlated color temperature, the object lighting device irradiates the object with illumination light having the correlated color temperature and the color deviation.

4. The camera-linked lighting control system according to claim 1.

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