Heating Cooker

Abstract

The heating cooker disclosed in the present invention comprises: a heating chamber that accommodates a heating object; a photographing unit that photographs the interior of the heating chamber; and an exposure control unit that performs exposure control. The exposure control unit performs exposure control based on grayscale information obtained from an image photographed by the photographing unit. Thus, the illumination in the heating chamber can be accurately detected, and an image for recognition can be obtained after performing exposure control.

Description

Technical Field

[0001] The present disclosure relates to a heating cooker for heating food and a control method of the heating cooker. Background Art

[0002] A microwave oven as an example of a heating cooker can heat food in a state placed in a container without using a pot or a frying pan. Therefore, in sales stores such as convenience stores, food such as bento boxes and dishes is sometimes heated and provided using a microwave oven.

[0003] Usually, a heating time suitable for heating in a microwave oven is displayed for a bento or a dish, and generally, a salesperson at a store sets the heating time in the microwave oven by observing the display.

[0004] Specifically, the clerk can set the heating time by operating the numeric keys arranged on the operation part of the microwave oven. Alternatively, when using a microwave oven with a plurality of cooking buttons corresponding to the heating time and wattage, the clerk can heat the food appropriately by operating the button corresponding to the food to be heated.

[0005] However, setting the heating time with numeric keys is cumbersome. In addition, in a microwave oven where heating times for different foods are assigned to multiple operation buttons, the clerk needs to remember the correspondence between the multiple buttons and the foods. As a result, as the variety of products increases, the burden on the clerk to remember the correspondence also increases.

[0006] In order to solve such a problem, it has been proposed to provide a microwave oven in which, when a store clerk reads information on a barcode attached to a product, the product is heated according to heating control contents corresponding to the barcode.

[0007] Alternatively, a microwave oven has been proposed that has a camera on the top surface of the heating chamber of the microwave oven for photographing the interior. In this conventional microwave oven, a barcode portion is extracted from an image of a commodity placed in the chamber, and the barcode is read to retrieve heating control contents corresponding to the commodity from the code information, thereby performing appropriate heating (e.g., Patent Document 1).

[0008] In addition, there is a disclosed example of a microwave oven that acquires an image of food using a camera installed so as to be able to capture the interior of a heating chamber, performs image recognition processing, and performs cooking according to the recognition result.

[0009] Prior art literature

[0010] Patent Literature

[0011] Patent Document 1: Japanese Patent Application Publication No. 2001-349546 Summary of the invention

[0012] In the existing microwave oven, the heating chamber is illuminated with appropriate illumination by LED or other lighting, and an image suitable for image recognition processing is obtained by a camera. At this time, depending on the environment in which the microwave oven is installed, sometimes external light such as sunlight enters the heating chamber from the opening of the door, and the illumination in the heating chamber cannot be maintained at a level suitable for image processing. In the image captured under the condition of external light incident, the object to be recognized may be whitened, which significantly reduces the recognition rate.

[0013] The object of the present invention is to provide a cooking device that can accurately detect the illumination in a heating chamber based on an image captured by a camera even when external light such as sunlight enters from an opening of a door, and obtain an image for recognition after performing exposure control, thereby improving the recognition accuracy of an image to be recognized.

[0014] In order to solve the above-mentioned conventional problems, the present invention discloses a heating cooker comprising: a heating chamber that accommodates a heating object; an imaging unit that images the interior of the heating chamber; and an exposure control unit that performs exposure control based on gray level information obtained from an image captured by the imaging unit.

[0015] The heating cooker disclosed herein can accurately detect the illumination in the heating chamber based on the image captured by the camera even when external light such as sunlight enters from the door opening, and obtain an image for recognition after performing exposure control, thereby improving the recognition accuracy of the image to be recognized. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a perspective view showing the appearance of the heating cooker according to the first embodiment of the present invention.

[0017] Figure 2 It is a schematic configuration diagram of the heating cooker according to the first embodiment of the present disclosure.

[0018] Figure 3 The diagram shows a label of product information including heating control information displayed on a product heated by the heating cooker according to the first embodiment of the present disclosure.

[0019] Figure 4 It is a figure which shows the indoor image of the heating cooker in the empty state according to Embodiment 1 of the present disclosure.

[0020] Figure 5A This is a diagram showing an example of an indoor image of the heating cooker according to Embodiment 1 of the present disclosure in which food is placed.

[0021] Figure 5BThis is a diagram showing an example of an indoor image of the heating cooker according to Embodiment 1 of the present disclosure in which food is placed.

[0022] Fig. 6A This is a diagram showing a histogram of grayscale values ​​of an indoor image of the heating cooker in the first embodiment of the present disclosure in which food is placed.

[0023] Figure 6B This is a diagram showing a histogram of grayscale values ​​of an indoor image of the heating cooker in the first embodiment of the present disclosure in which food is placed.

[0024] Fig. 7A This is a diagram showing an example of a filter pass area when the photometry target detection unit according to the first embodiment of the present disclosure performs a high-gradation pass filter process on an indoor image.

[0025] Figure 7B This is a diagram showing an example of a filter pass area when the photometry target detection unit according to the first embodiment of the present disclosure performs a high-gradation pass filter process on an indoor image.

[0026] Fig. 8A This is a diagram showing an example of a histogram of grayscale values ​​of an indoor image after the photometry target detection unit according to Embodiment 1 of the present disclosure has performed a high grayscale pass filter process on the indoor image.

[0027] Figure 8B 1 is a diagram showing an example of a histogram of grayscale values ​​of an indoor image after the photometry target detection unit according to Embodiment 1 of the present disclosure has performed a high grayscale pass filter process on the indoor image.

[0028] Fig.9A This is a diagram showing an example of a region that passes through a filter when the photometry target detection unit according to the first embodiment of the present disclosure performs contrast filtering on an indoor image.

[0029] Fig. 9B This is a diagram showing an example of a region that passes through a filter when the photometry target detection unit according to the first embodiment of the present disclosure performs contrast filtering on an indoor image.

[0030] Fig. 10A 1 is a diagram showing an indoor image when the photometry target detection section according to Embodiment 1 of the present disclosure performs chromaticity filtering on the indoor image.

[0031] Fig. 10B This is a diagram showing an example of a region that passes through a filter when the photometry target detection unit according to the first embodiment of the present disclosure performs chromaticity filtering processing on an indoor image.

[0032] Fig.11This is a diagram showing an example of a filter range on an xy chromaticity diagram when the photometry target detection unit according to Embodiment 1 of the present disclosure performs chromaticity filter processing on an indoor image.

[0033] Fig.12 This is a flowchart showing the flow of operations of the cooking device according to the first embodiment of the present disclosure. DETAILED DESCRIPTION

[0034] The cooking device according to the first embodiment includes: a heating chamber that accommodates a heating object; a photographing unit that photographs the interior of the heating chamber; and an exposure control unit that performs exposure control based on grayscale information obtained from an image photographed by the photographing unit.

[0035] Thus, even when external light such as sunlight enters from the door opening, the illumination in the heating chamber can be accurately detected from the image captured by the camera, and an image for recognition can be obtained after performing exposure control. Therefore, the recognition accuracy of the image to be recognized can be improved.

[0036] The second mode of the heating cooker is based on the first mode and further includes a photometry object detection unit, which extracts a photometry object from the image captured by the capturing unit, and the photometry object detection unit allows the grayscale information of the photometry object part in the image to pass, and the exposure control unit performs the exposure control based on the grayscale information of the photometry object part.

[0037] Thus, exposure control can be performed based on grayscale information of an image obtained by pre-filtering the captured image so as to allow only the features of the light measurement target to pass. Therefore, the accuracy of exposure control can be improved.

[0038] A cooking device according to a third aspect is the cooking device according to the second aspect, wherein the photometry target detection unit allows only a predetermined proportion of relatively high grayscale values ​​in the image to pass.

[0039] This makes it possible to perform filtering processing with high accuracy and at high speed on a photometric object such as a white label, which is known in advance to have high brightness and a relatively high grayscale value when photographed.

[0040] A cooking device according to a fourth aspect is the cooking device according to the second aspect, wherein the photometry target detection unit allows only pixels having a grayscale value having a predetermined grayscale difference from pixels in a predetermined pixel area of ​​the photometry target to pass through.

[0041] Thus, when a portion with a high black-white contrast, such as a label with black characters or a black barcode printed on a white background, is determined in advance as a photometry target, filtering processing can be performed with high accuracy.

[0042] A cooking device according to a fifth aspect is the cooking device according to the second aspect, wherein the photometry target detection unit allows only grayscale values ​​of pixels within a predetermined chromaticity range to pass.

[0043] Thus, when the color of the label to be measured is limited in advance, the filter process can be performed with high accuracy and high speed.

[0044] A cooking device according to a sixth aspect is a cooking device according to any one of the second to fifth aspects, wherein the photometry target detection unit notifies a user that the identification target is not detected when the number of pixels detected as the photometry target does not reach a predetermined number.

[0045] Thus, when the identification object does not enter the shooting range or exceeds the field of view, the user is notified that the identification object is not detected at an early stage before the identification process is performed. Thus, the user can be urged to put food back in, check the label status of the identification object, etc.

[0046] Hereinafter, the embodiments will be described in detail with reference to the accompanying drawings as appropriate. However, unnecessary detailed descriptions may be omitted. For example, detailed descriptions of known matters and repeated descriptions of substantially the same structures may be omitted. This is to avoid the following description from becoming unnecessarily lengthy and to make it easy for those skilled in the art to understand.

[0047] In addition, the drawings and the following description are provided to enable those skilled in the art to fully understand the present disclosure, and are not intended to limit the subject matter described in the claims.

[0048] (Implementation Method 1)

[0049] Hereinafter, a heating cooker according to Embodiment 1 of the present disclosure will be described with reference to the drawings.

[0050] Figure 1 It is a figure which shows the external appearance of the heating cooker which concerns on 1st Embodiment of this disclosure.

[0051] Figure 1 The microwave oven 100 shown includes a housing 101 and a door 102 pivotally supported in an openable and closable manner on the housing 101. A heating chamber 201 is provided inside the housing 101 for accommodating food (heating object 203) such as a lunch box or a dish to be heated.

[0052] The door 102 has a transparent glass window 103, so that the user can see the interior of the housing 101. In addition, in order to allow the user to easily grasp the door 102, the door 102 also has a handle 104.

[0053] In the present embodiment, the following description is given with the side of the housing 101 having the door 102 as the front, the right side from the front toward the rear as the right, and the left side from the front toward the rear as the left.

[0054] An operation display unit 105 is disposed next to the door 102. The operation display unit 105 includes a liquid crystal display 106, a time setting button group 107, a heating start button 108, a cancel button 109, and a temporary stop button 110. The user can set the heating time by using the numeric buttons and the minute and second buttons. In addition, the liquid crystal display 106 displays the set heating time, etc.

[0055] The heating start button 108 is a button for starting heating after the user confirms the heating time, wattage, etc. using the liquid crystal display 106. The cancel button 109 is a button for stopping heating after heating is started by pressing the heating start button 108, or for canceling the setting of the heating time displayed on the liquid crystal display 106.

[0056] The temporary stop button 110 is a button for temporarily stopping heating in the middle of heating. After the temporary stop of heating, the user can continue the remaining heating in the middle by pressing the heating start button 108 again.

[0057] Figure 2 This is a schematic configuration diagram of microwave oven 100 in Embodiment 1.

[0058] The microwave oven 100 includes two magnetrons 202a and 202b as a heating unit for outputting microwaves into a heating chamber 201.

[0059] The magnetron 202a is arranged on the ceiling side of the heating chamber 201, and outputs microwaves from the upper part into the heating chamber 201. On the other hand, the magnetron 202b is arranged on the bottom side of the heating chamber 201, and outputs microwaves from the lower part into the heating chamber 201. The food such as a lunch box and a dish contained in the heating chamber 201, that is, the heating object 203 is heated by the radiated microwaves.

[0060] In addition, in the present disclosure, microwaves generated by a magnetron are exemplified as the heating unit, but heating may be performed using microwaves generated by semiconductor oscillation, a heater, warm air, steam, etc. In addition, the number of heating sources such as magnetrons may be one instead of two.

[0061] A camera 204 (an example of a photographing unit) is disposed on the top side of the heating chamber 201. The camera 204 is composed of, for example, an imaging element such as a CCD (Charge Coupled Device) and optical elements such as a lens. The camera 204 photographs the interior of the heating chamber 201 to generate an image. The generated image, for example, represents the brightness of each pixel using a value ranging from 0 (dark) to 255 (bright). In addition, an image can be generated in which each pixel is represented by a value of 0 to 255 for each color of red, blue, and green. In addition, the value corresponding to each pixel can also be represented by a range or expression method other than 0 to 255.

[0062] Furthermore, in the present embodiment, the camera 204 is disposed on the top side of the heating chamber 201. However, the camera 204 may also be disposed on other surfaces such as the left and right sides of the heating chamber 201. In addition, in the present disclosure, by studying the exposure conditions as described later, even if the photographing unit 204 is composed of one camera component, the recognition accuracy of the image can be improved. Therefore, it is possible to achieve cost reduction during manufacturing and miniaturization of the housing 101. Not limited to this, the photographing unit 204 may also be composed of a plurality of camera components.

[0063] Lighting 205 using LED as a light source is arranged on the side of the heating chamber 201 to illuminate the interior of the heating chamber 201 .

[0064] In this embodiment, the lighting 205 is arranged so as to face the inside of the heating chamber 201 from the left side surface of the heating chamber 201. However, the lighting 205 may be arranged on any one of the four side surfaces, the top surface, the bottom surface, etc., or a plurality of lighting 205 may be arranged.

[0065] In this embodiment, a structure using LED is disclosed as the light source of the illumination 205, but other light sources such as light bulbs, fluorescent lamps, and natural light may also be used as the light source.

[0066] The control unit 300 is disposed below the operation display unit 105. The control unit 300 controls each component of the microwave oven 100.

[0067] exist Figure 2 In the embodiment, the control section 300 includes a heating control section 301 , a recognition section 302 , a photometry target detection section 303 , and an exposure control section 304 .

[0068] In the present embodiment, the control unit 300 is a structure in which the heating control unit 301, the recognition unit 302, the photometric object detection unit 303, and the exposure control unit 304 are integrated. However, these structures may be realized by different semiconductor elements, etc. In addition, the control unit 300 may be composed of a microcomputer having a processor such as a CPU (Central Processing Unit).

[0069] The heating control unit 301 controls the magnetron 202a and the magnetron 202b. The heating target 203 accommodated in the heating chamber 201 is heated by microwaves radiated from the magnetron 202a and the magnetron 202b.

[0070] The recognition unit 302 recognizes an object such as food, or recognizes a graphic code such as a barcode or characters attached to the food, based on the image captured by the camera 204 .

[0071] The photometry object detection unit 303 calculates the average grayscale value of the entire image after filtering the image captured by the camera 204 so as to pass the grayscale values ​​of the pixels of the food label portion as the object of recognition and photometry. The photometry object is extracted by using the calculated average grayscale value as the estimated grayscale value of the photometry object portion. That is, the photometry object detection unit 303 passes the grayscale information of the photometry and recognition object portion based on the image captured by the camera 204.

[0072] The exposure control section 304 determines whether exposure adjustment is necessary based on the estimated grayscale value output by the light measurement target detection section 303. Then, the exposure control section 304 controls the camera 204 or the illumination 205 as necessary to change the exposure setting.

[0073] Figure 3 This is an example of a label attached to food.

[0074] Commodities such as lunch boxes, rice balls, and dishes are placed and heated in the heating chamber 201. Labels indicating the heating power and heating time required for cooking the commodities as described above are attached to these commodities.

[0075] In addition, in this embodiment, the information for cooking such as the heating power and the heating time is referred to as heating control information.

[0076] Label 401 displays information such as product name 402, heating control information 403, price information 404, expiration date information 405, a barcode 406 (or other graphic code) for identifying the product, nutritional information 407, and notification information 408. In this embodiment, the heating control information is surrounded by a frame 409 in order to easily extract the heating control information from the information.

[0077] In addition, the label 401 also contains a heating time as a rough standard when heating with a general household microwave oven, for example, heating at 500 W, and a heating time as a rough standard when heating with high power for a short time, for example, heating at 1500 W. For example, the label 401 displays "500 W 2 minutes 00 seconds 1500 W 0 minutes 40 seconds" as heating control information.

[0078] When the user opens the door 102 and puts food in, the camera 204 takes a picture of the inside of the heating chamber 201. Then, the recognition unit 302 recognizes the part where the heating control information is displayed from the image captured by the camera 204, and recognizes the characters and numbers of the heating control information.

[0079] Specifically, the recognition unit 302 recognizes the frame 409 from the image captured by the camera 204 .

[0080] Next, the recognition unit 302 recognizes the character string of alphanumeric characters “500W 200 1500W040” enclosed by the frame 409 .

[0081] Then, according to the predetermined parsing rules, the recognition unit 302 decomposes the character string into a digital string up to "W", a 3-digit digital string after "W", the next digital string up to "W", and a 3-digit digital string after "W", namely "500", "200", "1500", and "040". The recognition unit 302 further recognizes the second digital string and the fourth digital string as follows: the first digit is "minutes" and the next 2 digits are seconds. In addition, the power of the first digital string corresponds to the time of the second digital string, and the power of the third digital string corresponds to the time of the fourth digital string. Then, the recognition unit 302 recognizes heating control information such as 2 minutes at 500W and 40 seconds at 1500W.

[0082] In this embodiment, the food label portion (label 401) is not the entire indoor image but the object of recognition and photometry. In addition, the grayscale value of the food label portion (label 401) is also the object of grayscale estimation for exposure adjustment, which will be described in detail later.

[0083] Figure 4 An example of an indoor image showing a state where the heating chamber 201 is empty. In the microwave oven chamber, a box indicating a rough reference for the placement of food and precautions for use are printed on the bottom surface. In this embodiment, the following description is made based on the case where the box, precautions and other characters are printed in black on a white background, but the design including the color of the bottom surface of the chamber may be other designs.

[0084] Figure 5A and Figure 5B This is another example of an image captured inside the heating chamber 201. In this example, Figure 5A FIG. 2 shows an example of an image captured when “white rice”, which is an example of food with high brightness, is present in the heating room 201. Figure 5B An example of an image captured when a “nori bento” as an example of food with low brightness is present in heating chamber 201 is shown.

[0085] Fig. 6A as well as Figure 6B Indicates presence indoors Figure 5A as well as Figure 5B In the case of food as an example, a histogram of image grayscale values ​​corresponding to each captured image and an average grayscale value of the entire image. Figure 5A , Figure 5B The histograms of the images are Fig. 6A as well as Figure 6B The histogram of . That is, Fig. 6A The bright gray area is large, Figure 6B The dark gray area is large.

[0086] Fig. 7A and Figure 7B When a high grayscale filter process is performed as the process performed by the photometric object detection unit 303, the image is expressed as a black and white binary image. Figure 5A and Figure 5B Examples of the filter pass-through pixel areas of each captured image. Fig. 7A and Figure 7B In the figure, the white part represents the pixel area that passes through the filter, and the black part represents the pixel area that is blocked by the filter.

[0087] Fig. 8A and Figure 8B The light measurement object detection unit 303 is used to Figure 5A and Figure 5B The processing of the respective indoor images is performed with a higher grayscale filtering process, that is, Fig. 7A and Figure 7B The dashed line of the histogram is for easy understanding. Fig. 6A and Figure 6B The histograms are compared respectively, and the higher grayscale is processed by filtering, indicating the grayscale part blocked by the filter.

[0088] Fig.9A and Fig. 9B In the case where contrast filtering is performed as the processing performed by the photometric target detection unit 303, the image is expressed as a black and white binary image. Figure 5A and Figure 5B Examples of the filter pass target pixel areas of each captured image. Fig. 7A and Figure 7B Likewise, the white portion represents the pixel area that has passed the filter, and the black portion represents the pixel area that has been blocked by the filtering process.

[0089] Fig. 10A The indoor image is shown in the case where a characteristic color (for example, yellow close to the primary color) is used for the background portion of the label 401 to be measured. Fig. 10B The light measurement object detection unit 303 Fig. 10A 7 , the white portion represents the pixel area that has passed the filter, and the black portion represents the pixel area that has been blocked by the filter. In this embodiment, a label whose background color is yellow close to the primary color is used as an example for explanation, but the color is not particularly limited.

[0090] Fig.11 The xy chromaticity diagram shows the chromaticity range that passes through the filter when the photometric object detection unit 303 performs chromaticity filtering. The inside of the circle indicated by the dotted line 1101 in the figure represents the chromaticity range that passes through the filter. In addition, in addition to the circle, a rectangle or an arbitrary function can be used to specify the range of the chromaticity range that passes through the filter. In addition, as a chromaticity diagram, in addition to the xy chromaticity diagram, a uv chromaticity diagram or a u'v' chromaticity diagram can also be used. As a color system, other than the XYZ color system, for example, a L*u*v* color system, a L*a*b* color system, an HSV color system, etc. can also be used.

[0091] Fig.12 FIG. 1 is a flowchart showing the exposure control operation of the microwave oven 100 in the first embodiment. Fig.12 The flowchart is explained in detail.

[0092] In step S1, the camera 204 captures the indoor image with the initial exposure setting, and the control unit 300 causes the process to proceed to step S2. As a result of the capture, Figure 5A as well as Figure 5B Such interior images.

[0093] In step S2, the photometric object detection unit 303 performs photometric object detection filtering processing on the indoor image. In this embodiment, the photometric object detection unit 303 performs at least one of high grayscale pass filtering processing, contrast filtering processing, and chromaticity filtering processing. Each filtering processing is described in detail below.

[0094] First, the filtering process for a higher grayscale is described. When the light measurement object detection unit 303 uses the filtering process, it is assumed that the background of the food label 401, which is the object of identification and light measurement, is a background with a relatively high brightness such as white. In addition, a series of processes are described on the premise that they are not performed in color but in gray images, so grayscale refers to gray grayscale (black: 0 grayscale, white: 255 grayscale).

[0095] As a specific higher grayscale filtering process, first Figure 5A as well as Figure 5B Indoor images such as those shown in the figure are produced Fig. 6A as well as Figure 6B That's a histogram of gray values. Figure 5A This is an indoor image of "white rice" as an example of a food with relatively high brightness. Figure 5B This is an indoor image of a "nori bento" as an example of food with relatively low brightness. Therefore, as the corresponding histogram, Fig. 6A The histogram shape becomes biased towards high grayscale areas. Figure 6B The histogram has the following shape: there is a peak in the low grayscale area corresponding to the "nori" part of the "nori bento" which has particularly low brightness, and also has a peak in the high grayscale area because the food label 401 and the bottom of the room are white and have high brightness.

[0096] Regarding the average gray value, Fig. 6A as well as Figure 6B As shown in the figure, the average grayscale value of the entire image shows a significant difference in the brightness of foods such as "white rice" and "seaweed bento". In the exposure control described in this embodiment, the average grayscale value of the entire image is calculated as the estimated grayscale value of the food label (label 401) part, which is the metering object when identifying and adjusting the exposure. This is the processing performed in the S3 step described later.

[0097] In the stage where filtering is not performed, even under the same illumination environment, the difference in brightness of the food outside the food label (label 401) area is also expressed as Fig. 6A as well as Figure 6B That is, the grayscale estimation error is large, and it is difficult to use the grayscale average directly for exposure control. In the higher grayscale pass filtering process, only a specified proportion of higher grayscale values ​​relative to the total number of pixels in the image are passed, and lower grayscale values ​​are blocked.

[0098] Fig. 7A as well as Figure 7B It is a binary image with black and white. Figure 5A as well as Figure 5BThe image of the indoor image of FIG. 4 is an image of the passing pixel area when the high-grayscale passing filter processing is performed. The high-brightness portion of the image including the food label (label 401) as the photometry object becomes the passing pixel area.

[0099] For example, considering a filter that passes the upper 50%, Fig. 6A as well as Figure 6B The lower 50% of the area of ​​the histogram is blocked and becomes Fig. 8A as well as Figure 8B The dotted line represents the lower 50% of pixels blocked by the filter. When calculating the average gray value, the blocked pixels are not included in the calculation. Fig. 8A as well as Figure 8B As shown, if Fig. 6A as well as Figure 6B By comparing the two, the difference between the average grayscale values ​​of the two sides is reduced, and the grayscale estimation error becomes smaller. Under the premise that the brightness of the identification and light measurement object is high, through such filtering processing, the grayscale value of the light measurement object (label part) can be estimated at high speed and high precision only through simple filtering processing in the preprocessing stage without detecting the label part (label 401). Therefore, the characters of the label part can be easily recognized.

[0100] Next, the contrast filtering process is explained. The basic idea is the same as the higher grayscale pass filtering process. The purpose is to eliminate the influence other than the food label as the identification and photometry object through the filtering process, and reduce the estimation error of the grayscale value used in the exposure control. When the photometry object detection unit 303 uses the contrast filtering process, it is assumed that the black and white contrast of the identification object part in the food label as the identification and photometry object is large (characters or barcodes, etc.). As a specific filtering process, the photometry object detection unit 303 performs a process of passing only the pixels with grayscale values ​​having a specified grayscale difference with the object pixel and the object pixel annotated. That is, the photometry object detection unit 303 only passes the pixels with grayscale values ​​having a specified grayscale difference with the grayscale values ​​of the pixels in the specified pixel area as the measurement object.

[0101] For example, when determining whether a certain pixel in an image passes through a filter, a 5×5 pixel area centered on the object pixel is processed to confirm the grayscale value. For example, if the grayscale value of the object pixel is 200 grayscales, and there are pixels below 100 grayscales that are -100 grayscales in the 5×5 pixel area, the object pixel becomes a pass pixel, and becomes a blocked pixel if it does not exist. This processing is performed on all pixels in the image. The area size of 5×5 pixels listed here is an example, and the shape of the area (including the size) is not limited to a rectangle. In addition, the grayscale difference of -100 is also an example, and there is no limitation on the numerical value.

[0102] Fig.9A as well as Fig. 9B It is a binary image with black and white. Figure 5A as well as Figure 5B The image of the passing object pixel area when the indoor image is subjected to contrast filtering. The part with high black-white contrast including the character part as the recognition object becomes the passing object area. Under the premise that the contrast of the recognition and metering objects is high, through such contrast filtering processing, the grayscale value of the metering object can be estimated at high speed and high precision only through simple filtering processing in the preprocessing stage without detecting the label part. Therefore, the characters of the label part can be easily recognized.

[0103] Next, the chromaticity filtering process is described. The basic idea is the same as the higher grayscale filtering process. Through the chromaticity filtering process, the influence other than the food label as the identification and metering object can be eliminated, and the estimation error of the grayscale value used in the exposure control can be reduced. When the metering object detection unit 303 uses the chromaticity filtering process, it is based on the premise that the color of the label as the metering object is a characteristic color.

[0104] Fig. 10A This is an example of an indoor image in which a food label is yellow, for example. Since the label is colored, it is captured as a grayscale with a lower grayscale than the white indoor floor. As a specific filtering process, only the grayscale values ​​of pixels that converge within a predetermined range on the chromaticity diagram are passed. For example, suppose a certain pixel in the image is R: 100 grayscale, G: 100 grayscale, and B: 10 grayscale.

[0105] For example, if ITU-R BT.709 is used as a reference, X, Y, and Z can be calculated by X = 0.4124R + 0.3576G + 0.1805B, Y = 0.2126R + 0.7152G + 0.0722B, and Z = 0.0193R + 0.1192G + 0.9505B. And, since x = X / (X+Y+Z) and y = Y / (X+Y+Z), the chromaticity x and y can be calculated as x = 0.4028 and y = 0.4779, respectively. Fig.11 If the target pixel is within the predetermined range 1101 on the xy chromaticity diagram of FIG. 1101, the photometry target detection unit 303 sets the target pixel as a through pixel. Fig.11 If the pixel is outside the range 1101 on the xy chromaticity diagram, the photometry target detection unit 303 sets the target image as a blocked pixel. That is, the photometry target detection unit 303 only allows the grayscale values ​​of pixels within the prescribed chromaticity range to pass. The photometry target detection unit 303 performs such processing on all pixels in the image.

[0106] In this filtering, the filter condition confirmation is performed using RGB grayscale because color information is used, but when calculating the average grayscale, gray grayscale is used. The conversion from RGB grayscale to gray grayscale can be calculated by 0.299R+0.587G+0.114B.

[0107] Fig. 10B It is a binary image with black and white. Fig. 10A The image of the passing object pixel area when the indoor image is subjected to contrast filtering. The label part as the identification object becomes the passing object area. Under the premise that the color of the identification and metering object is a characteristic color, through such filtering processing, the grayscale value of the metering object can be estimated at high speed and high precision only through simple filtering processing in the preprocessing stage without detecting the label part. Therefore, the characters of the label part can be easily recognized.

[0108] The photometry target detection unit 303 performs any of the photometry target detection filtering processes, such as the high grayscale pass filtering process, the contrast filtering process, and the chromaticity filtering process, to pass only the grayscale information of the photometry and recognition target portion of the image captured by the camera 204. Thereafter, the control unit 300 advances the process to step S3.

[0109] In step S3, the photometry target detection section 303 calculates the average grayscale value of the entire image based on the grayscale value that has passed the filtering process in step S2. Thereafter, the control section 300 advances the process to step S4.

[0110] In step S4, the exposure control unit 304 uses the average grayscale value calculated in step S3 to determine whether exposure adjustment is required. For example, when the average grayscale value is greater than 100 and less than 150, it is determined that the image has been captured at a brightness suitable for recognition, and the exposure control unit 304 does not make exposure adjustment, and the control unit 300 causes the process to proceed to step S6. When the average grayscale value is less than 100 or greater than 150, the exposure control unit 304 determines that exposure adjustment is required, and the control unit 300 causes the process to proceed to step S5. In addition, the thresholds of 100 grayscale and 150 grayscale are examples, and the numerical range is not specifically limited.

[0111] In step S5, the exposure control unit 304 performs exposure adjustment based on the average grayscale value. For example, when the average grayscale value is less than 100, the exposure control unit 304 determines that the indoor image is too dark and sets the camera 204 to extend the exposure time. That is, the exposure control unit 304 performs exposure control based on the grayscale information obtained from the image captured by the camera 204.

[0112] In addition, when the average grayscale value is greater than 150, the exposure control unit 304 determines that the indoor image is too bright, and sets the camera 204 to shorten the exposure time. As a specific method for exposure adjustment, a method of adjusting the exposure time is listed, but other methods may also be used. A method of adjusting the gain setting of the camera 204 may be used, or a method of combining exposure time adjustment and gain adjustment may be used. Alternatively, a method of controlling the brightness of the room by lighting 205 may be used. After the exposure adjustment is performed, the process returns to step S1 and the process is performed again from shooting the indoor image.

[0113] In step S6, the control unit 300 determines whether the number of pixels that have passed through the filtering process in step S2 exceeds a predetermined number. For example, if the number of pixels that have passed through is greater than 10,000, the control unit 300 causes the process to proceed to step S7, which is an image recognition process. If the number of pixels that have passed through is less than 10,000, the control unit 300 causes the process to proceed to step S9.

[0114] In step S9, the control unit 300 determines that the result of step S6 is that the food label as the identification object is not captured in the captured image, or that a portion of the food label is captured but exceeds the image, and is therefore not suitable as an identification image, and notifies the user that the identification object is not detected, does not perform the identification process and heating, and ends the process. That is, when the number of pixels detected as the photometric object is less than the specified number, the control unit 300 notifies the user that the identification object is not detected. Since the identification process is not performed and the user is notified that the identification object is not detected, the user can be urged to re-place the food and confirm the status of the food label as soon as possible.

[0115] In step S7 , the recognition unit 302 performs image recognition processing on the indoor image captured by the camera 204 , acquires heating control information, and proceeds to step S8 .

[0116] In step S8, the heating control unit 301 performs heating cooking based on the heating control information acquired in step S7, and the process ends.

[0117] As described above, according to the present embodiment, even when there is the influence of external light incident from the opening of the door 102 of the microwave oven, the grayscale of the food label portion, which is the object of recognition and light measurement, is estimated with high accuracy based on the grayscale information of the image captured by the camera 204. Furthermore, the exposure control unit 304 performs appropriate exposure adjustment based on the estimated grayscale, thereby enabling high-accuracy image recognition processing to be performed on the image captured at a brightness suitable for recognition.

[0118] (Other embodiments)

[0119] The above embodiment can also be implemented as a heating cooking system that can connect a heating cooker such as a microwave oven to a network and control the heating cooker through a server on the network. In such a heating cooking system, the processing performed by the photometric object detection unit 303 in the microwave oven 100 of embodiment 1 is executed on the server side. As a result, the processing load of the image filtering processing in the heating cooker can be reduced.

[0120] In addition, the above-mentioned embodiments are for illustrating the technology in the present disclosure, and therefore various changes, substitutions, additions, omissions, etc. can be made within the scope of the claims or their equivalents.

[0121] The heating cooker disclosed in the present invention can use a camera to take pictures, recognize features such as shapes, characters and other indoor conditions, and reflect the recognition results in cooking control. Therefore, in addition to microwave ovens used in sales stores, it can also be widely used in household microwave ovens, rice cookers, IH cooking heaters and other heating cookers.

[0122] Description of symbols

[0123] 100: microwave oven (heating cooker); 101: housing; 102: door; 103: glass window; 104: handle; 105: operation display unit; 106: liquid crystal display; 107: time setting button group; 108: heating start button; 109: cancel button; 110: temporary stop button; 201: heating chamber; 202a, 202b: magnetron; 203: heating object; 204: camera (shooting unit); 205: lighting; 300: control unit; 301: heating control unit; 302: identification unit; 303: photometric object detection unit; 304: exposure control unit.

Claims

1. A heating cooker comprising: A heating chamber that accommodates a heating object; a photographing unit for photographing the interior of the heating chamber; an exposure control section that performs exposure control; and a photometry object detection unit that extracts the grayscale information of the photometry object as a part of the image by filtering the grayscale information of the photometry object in the image captured by the capturing unit, The exposure control section performs the exposure control based on grayscale information of the light measurement object.

2. The heating cooker according to claim 1, wherein: The photometry target detection unit allows only a predetermined ratio of relatively high grayscale values ​​in the image to pass.

3. The heating cooker according to claim 1, wherein: The photometry target detection unit allows only pixels having a grayscale value having a predetermined grayscale difference from pixels in a predetermined pixel area of ​​the photometry target to pass.

4. The heating cooker according to claim 1, wherein: The photometry target detection unit allows only grayscale values ​​of pixels within a predetermined chromaticity range to pass.

5. The heating cooker according to any one of claims 1 to 4, wherein: The photometry target detection unit notifies a user that a recognition target has not been detected when the number of pixels detected as the photometry target is less than a predetermined number.

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