Data processing apparatus, method and optical element, photographic optical system and photographic apparatus

Abstract

The present application provides a data processing device, method and program, and an optical element, a photographic optical system and a photographic device, which can easily determine a wavelength combination of a first wavelength and a second wavelength suitable for detection of a detection object from a plurality of subjects including the detection object. In the data processing device provided with a processor, the processor performs the following processing: data acquisition processing to acquire spectral data of a plurality of subjects; calculation processing to calculate intensity characteristics at the first wavelength and the second wavelength selected from a wavelength region of the acquired spectral data of the plurality of subjects according to a relationship between the two wavelengths; data conversion processing to convert the intensity characteristics calculated in the calculation processing into identification data of a specific subject with respect to the wavelength region; and output processing to externally output the identification data.

Description

Data processing apparatus, methods, optical elements, photographic optical systems, and photographic apparatus Technical Field

[0001] This invention relates to a data processing apparatus, method, and program, as well as optical elements, photographic optical systems, and photographic apparatus, and particularly to the selection of a wavelength suitable for detecting an object. Background Technology

[0002] Previously, hyperspectral cameras were capable of using wavelengths above 100 for spectral sensing.

[0003] In such hyperspectral cameras, since multiple wavelengths are measured, the object is generally sensed and detected by looking for wavelengths where reflection or absorption changes drastically (e.g., by taking the second derivative of the spectral reflectance in the wavelength direction to find its peak wavelength).

[0004] Furthermore, in the past, foreign object infiltration detection devices have been provided for detecting foreign objects such as hair in food, cosmetics, pharmaceuticals, and other inspection objects (Patent Document 1).

[0005] The foreign object inclusion detection device illuminates the object with light in the first band and the second band, whose brightness values ​​are different from those of the object being inspected. It acquires a first spectral image based on the brightness value of the first band and a second spectral image based on the brightness value of the second band. By performing calculations on the acquired first and second spectral images, it obtains an image of the foreign object that can be identified from the object being inspected.

[0006] Previous technical documents

[0007] Patent documents

[0008] Patent Document 1: Japanese Patent Application Publication No. 2007-178407 Summary of the Invention

[0009] The technical problem to be solved by the invention

[0010] One embodiment of the present invention provides a data processing apparatus, method, and procedure, as well as optical elements, photographic optical systems, and photographic apparatus, capable of easily determining a wavelength combination of a first wavelength and a second wavelength suitable for detecting the object from a plurality of subjects including the object to be detected.

[0011] means for solving technical problems

[0012] The invention according to the first method is in a data processing device equipped with a processor, wherein the processor performs the following processing: data acquisition processing to acquire spectral data of multiple subjects; calculation processing to calculate intensity characteristics at a first wavelength and a second wavelength selected from the wavelength region of the acquired spectral data of multiple subjects based on the relationship between the two wavelengths, a first wavelength and a second wavelength; data conversion processing to convert the intensity characteristics calculated in the calculation processing into identification data of a specific subject relative to the wavelength region; and output processing to externally output the identification data.

[0013] In the data processing apparatus according to the second aspect of the present invention, the preferred strength characteristics are strength difference and / or strength ratio.

[0014] In the data processing apparatus according to the third aspect of the present invention, it is preferable that if the spectral data of the first subject in the spectral data of a plurality of subjects is set as A(λ), the spectral data of the second subject is set as B(λ), the selected first wavelength is set as λ1, and the second wavelength is set as λ2, then the intensity difference and / or intensity ratio are obtained by the following [Equation 1] and / or [Equation 2] formulas.

[0015] [Formula 1]

[0016]

[0017] [Formula 2]

[0018]

[0019] Perform the calculation.

[0020] In the data processing apparatus according to the fourth aspect of the present invention, it is preferable to identify the data as a first distribution map representing the change of intensity characteristics with wavelength as the variable.

[0021] In the data processing apparatus according to the fifth aspect of the present invention, the first distribution map is preferably a two-dimensional distribution map, and the coordinate axes of the two-dimensional distribution map are the first wavelength and the second wavelength.

[0022] In the data processing apparatus according to the sixth aspect of the present invention, the external output terminal for identifying data is preferably a display device, and the processor performs the following processing: receiving a specific position displayed on a first distribution map on the display device by means of a user instruction; and determining a wavelength combination of a first wavelength and a second wavelength for detecting a detection object among a plurality of subjects based on the specific position.

[0023] In the data processing apparatus according to the seventh aspect of the present invention, the processor preferably performs the following processing: extracting positions in one or more first distribution maps from the first distribution map where the intensity characteristics exceed a threshold; and determining, based on the extracted positions, one or more wavelength combinations of a first wavelength and a second wavelength for detecting a detection object in a plurality of subjects.

[0024] In the data processing apparatus according to the eighth aspect of the present invention, the external output terminal for identifying data is preferably a display device, and the processor performs the following processing: displaying candidates of a specific position in one or more wavelength combinations of a first wavelength and a second wavelength overlapping on a first distribution map displayed in the display device; receiving a specific position from the candidates of the specific position by user instruction; and determining the wavelength combination of the first wavelength and the second wavelength based on the received specific position.

[0025] In the data processing apparatus according to the ninth aspect of the present invention, preferably, a plurality of subjects include a first subject, a second subject, and a third subject. The data acquisition process acquires the spectral data of the first subject, the spectral data of the second subject, and the spectral data of the third subject. The calculation process calculates two or more intensity characteristics among the intensity characteristics at the first and second wavelengths of the spectral data of the first subject and the spectral data of the second subject, the intensity characteristics at the first and second wavelengths of the spectral data of the second subject and the spectral data of the third subject, and the intensity characteristics at the first and second wavelengths of the spectral data of the first subject and the spectral data of the third subject. The data conversion process converts the two or more intensity characteristics into two or more identification data.

[0026] The invention involved in the 10th aspect is an optical element having a first wavelength selection element and a second wavelength selection element, wherein the first wavelength selection element allows transmission of a first wavelength band determined by the data processing device of any one of the 6th to 8th aspects, and the second wavelength selection element allows transmission of a second wavelength band determined by the data processing device of any one of the 6th to 8th aspects.

[0027] The invention involved in the 11th aspect is a photographic optical system that arranges the optical elements of the 10th aspect at or near the pupil position.

[0028] The invention involved in the 12th aspect is a photographic apparatus comprising: a photographic optical system of the 11th aspect; and an imaging element for capturing images of a first optical image transmitted through a first wavelength selection element and a second optical image transmitted through a second wavelength selection element, which are imaged by the photographic optical system.

[0029] The invention involved in the 13th aspect is a data processing method, which includes: a data acquisition step, acquiring spectral data of multiple subjects; a calculation step, calculating intensity characteristics at a first wavelength and a second wavelength selected from the wavelength region of the acquired spectral data of multiple subjects based on the relationship between the two wavelengths, a first wavelength and a second wavelength; a data conversion step, converting the intensity characteristics calculated in the calculation step into identification data of a specific subject relative to the wavelength region; and an output step, externally outputting the identification data, and having the processor execute the processing of each step.

[0030] In the data processing method according to the 14th aspect of the present invention, the preferred strength characteristics are strength difference and / or strength ratio.

[0031] In the data processing method according to the 15th aspect of the present invention, it is preferable that if the spectral data of the first subject in the spectral data of a plurality of subjects is set as A(λ), the spectral data of the second subject is set as B(λ), the selected first wavelength is set as λ1, and the second wavelength is set as λ2, then the intensity difference and / or intensity ratio are obtained by the following [Equation 1] and / or [Equation 2] formulas.

[0032] [Formula 1]

[0033]

[0034] [Formula 2]

[0035]

[0036] Perform the calculation.

[0037] In the data processing method according to the 16th aspect of the present invention, it is preferable to identify the data as a first distribution map representing the change of intensity characteristics with wavelength as the variable.

[0038] In the data processing method according to the 17th aspect of the present invention, the first distribution map is preferably a two-dimensional distribution map, and the coordinate axes of the two-dimensional distribution map are the first wavelength and the second wavelength.

[0039] In the data processing method according to the 18th aspect of the present invention, it is preferred that the external output terminal for identifying data is a display device. The data processing method includes: a step of receiving a specific position displayed on a first distribution map on the display device by means of a user instruction; and a determination step of determining a wavelength combination of a first wavelength and a second wavelength for detecting a detection object among a plurality of subjects based on the specific position.

[0040] The data processing method according to the 19th aspect of the present invention preferably includes: a step of extracting positions in one or more first distribution maps from the first distribution map where the intensity characteristics exceed a threshold; and a determination step of determining a wavelength combination of one or more first wavelengths and second wavelengths for detecting a detection object in a plurality of subjects based on the extracted positions.

[0041] In the data processing method of the 20th aspect of the present invention, it is preferred that the external output terminal of the data is a display device. The data processing method includes the following steps: displaying candidates of a specific position in one or more wavelength combinations of a first wavelength and a second wavelength overlapping on a first distribution map displayed in the display device; receiving a specific position from the candidates of the specific position by user instruction; and determining the wavelength combination of the first wavelength and the second wavelength based on the received specific position.

[0042] In the data processing method according to the 21st aspect of the present invention, it is preferable that the plurality of subjects include a first subject, a second subject, and a third subject. In the data acquisition step, spectral data of the first subject, spectral data of the second subject, and spectral data of the third subject are acquired. In the calculation step, two or more intensity characteristics are calculated from the intensity characteristics at the first and second wavelengths of the spectral data of the first subject and the spectral data of the second subject, the intensity characteristics at the first and second wavelengths of the spectral data of the second subject and the spectral data of the third subject, and the intensity characteristics at the first and second wavelengths of the spectral data of the first subject and the spectral data of the third subject. In the data conversion step, the two or more intensity characteristics are converted into two or more identification data.

[0043] The data processing method according to the 22nd aspect of the present invention preferably includes: an image acquisition step, which acquires a first image containing a band of a first wavelength in a determined wavelength combination and a second image containing a band of a second wavelength; a step of calculating the difference or ratio between the acquired first image and the second image; and a second distribution map production step, which produces a second distribution map representing the calculated difference or ratio.

[0044] In the data processing method according to the 23rd aspect of the present invention, preferably, in the determination step, two or more wavelength combinations of a first wavelength and a second wavelength for detecting a detection object among multiple subjects are determined based on the first distribution map; in the image acquisition step, a first image containing the first wavelength band of each of the two or more wavelength combinations and a second image containing the second wavelength band are acquired respectively; in the second distribution map creation step, a second distribution map is created for each of the two or more wavelength combinations; and the data processing method includes the step of combining the created two or more second distribution maps to create one second distribution map.

[0045] In the data processing method according to the 24th aspect of the present invention, it is preferable to include a step of detecting the object to be detected based on the second distribution map that has been created.

[0046] The invention involved in the 25th method is a data processing program that performs the following functions by computer: acquiring spectral data of multiple subjects; calculating intensity characteristics at a first wavelength and a second wavelength selected from the wavelength region of the acquired spectral data of multiple subjects based on the relationship between the two wavelengths, a first wavelength and a second wavelength; converting the calculated intensity characteristics into identification data of a specific subject relative to the wavelength region; and outputting the identification data externally. Attached Figure Description

[0047] Figure 1 is a functional block diagram illustrating a first embodiment of the data processing apparatus according to the present invention.

[0048] Figure 2 shows a photograph of multiple subjects containing the object being detected, taken by a hyperspectral camera.

[0049] Figure 3 is a chart showing the spectral data of paper, leaves, and insects, respectively.

[0050] Figure 4 is an example of a first distribution plot showing the intensity distribution of intensity characteristics calculated from spectral data of paper and leaves.

[0051] Figure 5 is an example of a first distribution plot showing the intensity distribution of intensity characteristics calculated from spectral data of leaves and insects.

[0052] Figure 6 is a diagram showing an example of a second distribution plot illustrating the difference or ratio between the first image and the second image obtained from a multispectral camera that captured the subject shown in Figure 2.

[0053] Figure 7 shows a photograph taken by a hyperspectral camera of multiple subjects including other objects being detected.

[0054] Figure 8 is a chart showing the spectral data of two types of soil, namely paper and powder.

[0055] Figure 9 is an example of a first distribution plot showing the intensity distribution of the strength properties calculated from the spectral data of two types of soil.

[0056] Figure 10 is a diagram showing an example of a second distribution plot illustrating the difference or ratio between a first image and a second image acquired from a multispectral camera that captured the subject shown in Figure 7.

[0057] Figure 11 is a diagram showing multiple subjects containing another object being detected, captured by a hyperspectral camera.

[0058] Figure 12 is a chart showing the spectral data of paper, peanut shells, and peanut kernels, respectively.

[0059] Figure 13 is an example of a first distribution plot showing the intensity distribution of intensity characteristics calculated from spectral data of peanut skin and kernel.

[0060] Figure 14 is an example of a second distribution map synthesized from three second distribution maps created from the first, second, and third images of the subject shown in Figure 11 captured by a multispectral camera.

[0061] Figure 15 shows the case where three second distribution maps are combined to create one second distribution map.

[0062] Figure 16 is a functional block diagram illustrating a second embodiment of the data processing apparatus according to the present invention.

[0063] Figure 17 is a schematic diagram showing an example of a multispectral camera.

[0064] Figure 18 is a flowchart illustrating a first embodiment of the data processing method according to the present invention.

[0065] Figure 19 is a flowchart illustrating a second embodiment of the data processing method according to the present invention.

[0066] Figure 20 is a flowchart illustrating a third embodiment of the data processing method according to the present invention. Detailed Implementation

[0067] Hereinafter, preferred embodiments of the data processing apparatus, method and program, optical elements, photographic optical systems and photographic apparatus involved in the present invention will be described with reference to the accompanying drawings.

[0068] [First Embodiment of the Data Processing Apparatus]

[0069] Figure 1 is a functional block diagram illustrating a first embodiment of the data processing apparatus according to the present invention.

[0070] The data processing apparatus 10-1 of the first embodiment can be composed of a personal computer, workstation or the like, which has hardware such as a processor, memory, and input / output interface.

[0071] The processor consists of a CPU (Central Processing Unit) and other components, which centrally controls the various parts of the data processing device 10-1, and functions as, for example, the data acquisition unit 12, intensity characteristic calculation unit 14, data conversion unit 16, output unit 18, user instruction receiving unit 20, and wavelength combination determination unit 22 shown in FIG1.

[0072] The data acquisition unit 12 performs data acquisition processing to acquire spectral data of multiple subjects. In this example, the data acquisition unit 12 acquires spectral data of multiple subjects directly from the hyperspectral camera 1, which has captured images of multiple subjects with different spectral reflectivities, or indirectly via a recording medium, network, or the like.

[0073] Figure 2 shows a photograph of multiple subjects containing the object being detected, taken by a hyperspectral camera.

[0074] The subjects shown in Figure 2 are the first subject, paper 2A, the second subject, leaf 2B, and the third subject, insect 2C. Leaf 2B is placed on paper 2A, and insect 2C is placed on leaf 2B.

[0075] When acquiring spectral data of multiple subjects from the hyperspectral camera 1, for example, the hyperspectral camera 1 captures images of the multiple subjects shown in FIG. 2, and displays the captured images on the display screen of the hyperspectral camera 1. Furthermore, by having the user indicate the area of ​​paper 2A, the area of ​​leaf 2B, and the area of ​​insect 2C on the display screen of the hyperspectral camera 1, spectral data representing the spectral reflectance of paper 2A, the spectral reflectance of leaf 2B, and the spectral reflectance of insect 2C are acquired from the hyperspectral camera 1.

[0076] In this example, the insect 2C is the object to be detected. The present invention relates to a technique for detecting the object (insect 2C) by finding a combination of two wavelengths whose changes in reflection or absorption are greater than those of the background subjects (paper 2A and leaf 2B).

[0077] Figure 3 is a chart showing the spectral data of paper, leaves, and insects, respectively.

[0078] Figure 3 shows a graph of spectral reflectance with the reflectance of the reference plate set to 1 on the vertical axis. Spectral reflectance is the average or central value of the reflectance of the entire or a portion of the object being tested.

[0079] The data acquisition unit 12 acquires the spectral data A(λ) of the paper 2A, the spectral data B(λ) of the leaf 2B, and the spectral data C(λ) of the insect 2C shown in FIG3. In addition, the spectral data of each subject is not limited to the case where it is acquired from the hyperspectral camera 1. For example, the spectral data can also be acquired when the spectral data of the subject (including a part of the subject) is known.

[0080] The intensity characteristic calculation unit 14 performs calculation processing to calculate the intensity characteristics at the first and second wavelengths selected from the wavelength region of the spectral data of multiple subjects acquired from the data acquisition unit 12, based on the relationship between the first and second wavelengths. The intensity characteristics at the first and second wavelengths are characteristics that evaluate the sensing performance of the subject; the larger the intensity characteristics, the easier it is to identify the subject. In addition, the first and second wavelengths may have widths.

[0081] The intensity characteristics at the first and second wavelengths are the intensity difference and / or intensity ratio of the spectral dispersion at the first and second wavelengths.

[0082] If the spectral data of the first subject (paper 2A) is set as A(λ), the spectral data of the second subject (leaf 2B) is set as B(λ), the first wavelength of the combination is set as λ1, and the second wavelength is set as λ2, then the intensity characteristic calculation unit 14 obtains spectral reflectance data A(λ1) and A(λ2) from the spectral data A(λ) of paper 2A at the first wavelength λ1 and the second wavelength λ2, and similarly obtains spectral reflectance data B(λ1) and B(λ2) from the spectral data B(λ) of leaf 2B at the first wavelength λ1 and the second wavelength λ2.

[0083] When calculating the strength difference as a strength characteristic, the strength characteristic calculation unit 14 calculates the absolute value of the difference between the spectral reflectance data A(λ1), A(λ2) of paper 2A at the first wavelength λ1 and the second wavelength λ2 and the difference between the spectral reflectance data B(λ1), B(λ2) of leaf 2B at the first wavelength λ1 and the second wavelength λ2 (the difference between the relative spectral reflectance data of paper 2A and leaf 2B at the two first wavelengths λ1 and λ2). At this time, in order to remove the effects of uneven lighting and shadows, it is preferable to divide the difference by the sum of the spectral reflectance data at the first wavelength λ1 and the second wavelength λ2.

[0084] Specifically, the strength characteristic calculation unit 14 calculates the strength difference based on the spectral reflectance data A(λ1) and A(λ2) of the paper 2A at the first wavelength λ1 and the second wavelength λ2, and the spectral reflectance data B(λ1) and B(λ2) of the leaf 2B, using the following formula [Equation 1].

[0085] [Formula 1]

[0086]

[0087] When calculating the strength ratio as a strength characteristic, the strength characteristic calculation unit 14 can calculate the strength ratio using the following formula [Equation 2].

[0088] [Formula 2]

[0089]

[0090] Furthermore, the intensity characteristic calculation unit 14 calculates the intensity difference and / or intensity ratio for each of the two possible wavelength combinations of the first wavelength λ1 and the second wavelength λ2.

[0091] In the above example, the intensity difference and / or intensity ratio of the sensing performance of the two subjects, paper 2A and leaf 2B, are calculated and evaluated. However, the intensity characteristic calculation unit 14 similarly calculates the intensity difference and / or intensity ratio of the sensing performance of the two subjects, leaf 2B and insect 2C. Moreover, when it is necessary to identify paper 2A and insect 2C, the intensity difference and / or intensity ratio of the sensing performance of the two subjects, paper 2A and insect 2C, are also calculated and evaluated.

[0092] The strength characteristics calculated by the strength characteristic calculation unit 14 are output to the data conversion unit 16.

[0093] The data conversion unit 16 performs data conversion processing to convert the calculated intensity characteristics into identification data of a specific subject relative to the wavelength region. The identification data is a distribution map (first distribution map) showing the variation of intensity characteristics with wavelength as the variable.

[0094] The first distribution map is a two-dimensional distribution map, and the coordinate axes of the two-dimensional distribution map are the first wavelength and the second wavelength.

[0095] Figure 4 is an example of a first distribution plot showing the intensity distribution of intensity characteristics calculated from spectral data of paper and leaves. The first distribution plot shown in Figure 4(A) is a heatmap with added color and concentration according to the magnitude of the intensity characteristics, and the first distribution plot shown in Figure 4(B) is a contour plot corresponding to the magnitude of the intensity characteristics.

[0096] In the first distribution map (heatmap) shown in Figure 4(A), the intensity characteristics of the bright (white) areas are greater. In the first distribution map (contour map) shown in Figure 4(B), the intensity characteristics of the contour lines with larger values ​​are greater.

[0097] Figure 5 is an example of a first distribution plot showing the intensity distribution of intensity characteristics calculated from spectral data of leaves and insects. The first distribution plot shown in Figure 5(A) is a heatmap with added color and concentration by the magnitude of the intensity characteristics, and the first distribution plot shown in Figure 5(B) is a contour plot corresponding to the magnitude of the intensity characteristics.

[0098] The identification data converted by the data conversion unit 16, namely the two first distribution maps (the first distribution maps shown in Figures 4 and 5), are added to the output unit 18.

[0099] The output unit 18 is an output processing unit that outputs the identification data (first distribution map) input from the data conversion unit 16 to an external device. In this example, the destination of the external output is the display device 30.

[0100] Therefore, in this example, the first distribution diagram shown in Figures 4 and 5 is displayed on the display device 30.

[0101] User instruction receiving unit 20 receives a specific location on the first distribution map indicated by the user's operation unit 32 of a pointing device such as a mouse operated by the user.

[0102] As mentioned earlier, in this example, since the insect 2C on the leaf 2B is the object of detection, it is preferable not to identify the positions of paper 2A and leaf 2B (with low intensity characteristics) on the first distribution map shown in FIG4. On the other hand, it is preferable to identify the positions of leaf 2B and insect 2C (with high intensity characteristics) on the first distribution map shown in FIG5.

[0103] The user can identify the specific locations of the paper 2A and leaf 2B that are not recognized and the leaf 2B and insect 2C that are recognized from the two first distribution maps displayed on the display device 30, and indicate the specific locations through the operation unit 32.

[0104] User instruction receiving unit 20 receives a specific location on the first distribution map that has been instructed by the user.

[0105] The wavelength combination determination unit 22 has wavelength information of each coordinate axis of the first distribution map. If a specific position is input by the user-instructed receiving unit 20, the wavelength information of each coordinate axis shown at that specific position, namely the wavelength combination of the first wavelength λ1 and the second wavelength λ2, is determined.

[0106] In this example, in the first distribution diagram shown in Figures 4 and 5, the user indicates a specific location indicated by the asterisk mark M1, and as a result, the wavelength combination determination unit 22 determines the wavelength combination of the first wavelength λ1 (=750nm) and the second wavelength λ2 (=950nm).

[0107] The wavelengths λ1 (=750nm) and λ2 (=950nm) of this determined wavelength combination are suitable for detecting insect 2C, which is the object of detection.

[0108] The first wavelength λ1 (=750nm) and the second wavelength λ2 (=950nm) determined by the wavelength combination determination unit 22 are output and displayed in the display device 30, and can also be output to the recording device or other external devices.

[0109] If the wavelength combination of the first wavelength λ1 and the second wavelength λ2 suitable for detecting the object is determined as described above, then a multispectral camera, described later, having a first wavelength selection element (first bandpass filter) that transmits light containing the first wavelength λ1 and a second wavelength selection element (second bandpass filter) that transmits light containing the second wavelength λ2, captures multiple subjects containing the object to be detected, and simultaneously acquires a first image containing the first wavelength λ1 and a second image containing the second wavelength λ2.

[0110] Additionally, as shown in Figure 2, the wavelength combination suitable for detecting the insect 2C on the leaf 2B placed on the paper 2A, the first wavelength λ1 and the second wavelength λ2, in the above example are the first wavelength λ1 (=750nm) and the second wavelength λ2 (=950nm).

[0111] Figure 6 is a diagram showing an example of a second distribution plot illustrating the difference or ratio between the first image and the second image obtained from a multispectral camera that captured the subject shown in Figure 2.

[0112] In the second distribution map shown in Figure 6, the contrast between insect 2C and the background paper 2A and leaf 2B becomes clear, and insect 2C can be detected with high accuracy by using the second distribution map.

[0113] <Other objects to be tested>

[0114] Figure 7 shows a photograph taken by a hyperspectral camera of multiple subjects including other objects being detected.

[0115] The subjects shown in Figure 7 are two types of powdered soil, 4B and 4C, placed on a background paper 4A. Soil 4B is of acceptable quality, while soil 4C is of unacceptable quality.

[0116] The data acquisition unit 12 (Fig. 1) acquires spectral data of two soils, 4B and 4C, from the hyperspectral camera 1.

[0117] Figure 8 is a chart showing the spectral data of two types of soil, namely paper and powder.

[0118] Figure 8 shows a graph of spectral reflectance with the reflectance of the reference plate set to 1 on the vertical axis. Spectral reflectance is the average or central value of the reflectance of the entire or a portion of the object being tested.

[0119] In Figure 8, D(λ) represents the spectral data of paper 4A, E(λ) represents the spectral data of ±4B, and F(λ) represents the spectral data of ±4C.

[0120] As shown in Figure 8, the spectral data E(λ) and F(λ) of the two soils 4B and 4C are roughly the same, making it difficult to distinguish between the two soils 4B and 4C with the naked eye.

[0121] The intensity characteristic calculation unit 14 calculates the intensity characteristics at the first wavelength λ1 and the second wavelength λ2 selected from the wavelength regions of the spectral data E(λ) and F(λ) of ±4B and 4C based on the relationship between the two wavelengths, the first wavelength λ1 and the second wavelength λ2. The data conversion unit 16 converts the calculated intensity characteristics into the first distribution map.

[0122] Figure 9 is an example of a first distribution map showing the intensity distribution of the strength characteristics calculated from the spectral data of two soils. The first distribution map shown in Figure 9(A) is a heatmap with added color and concentration based on the magnitude of the intensity characteristics, while the first distribution map shown in Figure 9(B) is a contour map corresponding to the magnitude of the intensity characteristics.

[0123] In the first distribution map shown in Figure 9, the user indicates a specific location with a high intensity characteristic. Additionally, in Figure 9, the specific location indicated by the user is marked with an asterisk (M2).

[0124] The wavelength combination determination unit 22 determines the wavelength combination of the first wavelength λ1 (=515nm) and the second wavelength λ2 (=730nm) from a specific position indicated by the mark M2.

[0125] A multispectral camera, having a first bandpass filter that transmits light in a band containing a determined first wavelength λ1 (=515nm) (e.g., 490nm~540nm) and a second bandpass filter that transmits light in a band containing a second wavelength λ2 (=730nm) (e.g., 700nm~760nm), is used to capture images of multiple subjects containing the object to be detected, as shown in FIG7, and simultaneously acquires a first image containing the first wavelength λ1 and a second image containing the second wavelength λ2.

[0126] Calculate the difference or ratio between the first and second images of each band acquired by the multispectral camera, and create a second distribution map representing the calculated difference or ratio.

[0127] Figure 10 is a diagram showing an example of a second distribution plot illustrating the difference or ratio between a first image and a second image acquired from a multispectral camera that captured the subject shown in Figure 7.

[0128] The second distribution map serves as an example, and the following are methods for expressing the difference between the first and second images.

[0129] With the first image set as wavelength image (λ1) and the second image set as wavelength image (λ2), the difference image is calculated by (wavelength image (λ1) - wavelength image (λ2)) ÷ (wavelength image (λ1) + wavelength image (λ2)), and the difference image is displayed as a heatmap.

[0130] Furthermore, the wavelength image (λ1) is assigned the R channel of the color image (R, G, B), and the wavelength image (λ2) is assigned the G channel, and the image is represented using a pseudo-color image. Additionally, the specific channels (R, G, and B) of the color image to which the wavelength images (λ1) and (λ2) are assigned are not limited to the examples described above.

[0131] Thus, by generating thermal or pseudo-color images from wavelength images (λ1) and (λ2), it is easy to distinguish the subject of the detected object from other subjects.

[0132] In the second distribution map shown in Figure 10, the contrast between soil 4B with OK quality and soil 4C with NG quality becomes clear. By using the second distribution map, soil 4B with OK quality and soil 4C with NG quality can be easily identified.

[0133] Figure 11 is a diagram showing multiple subjects containing another object being detected, captured by a hyperspectral camera.

[0134] The subjects shown in Figure 11 are subjects with peanut shells 6B and peanut kernels 6C placed on a piece of paper 6A in the background.

[0135] The data acquisition unit 12 (Fig. 1) acquires spectral data of peanut shell 6B and peanut kernel 6C from the hyperspectral camera 1.

[0136] Figure 12 is a chart showing the spectral data of paper, peanut shells, and peanut kernels, respectively.

[0137] Figure 12 shows a graph of spectral reflectance with the reflectance of the reference plate set to 1 on the vertical axis. Spectral reflectance is the average or central value of the reflectance of the entire or a portion of the object being tested.

[0138] In Figure 12, G(λ) represents the spectral data of paper 6A, H(λ) represents the spectral data of peanut skin 6B, and I(λ) represents the spectral data of peanut kernel 6C. As shown in Figure 12, peanut skin 6B and peanut kernel 6C are approximately the same color and are difficult to distinguish with the naked eye.

[0139] The intensity characteristic calculation unit 14 calculates the intensity characteristics at the first wavelength λ1 and the second wavelength λ2 selected from the wavelength regions of the spectral data H(λ) and I(λ) of peanut skin 6B and peanut kernel 6C based on the relationship between the two wavelengths, the first wavelength λ1 and the second wavelength λ2. The data conversion unit 16 converts the calculated intensity characteristics into the first distribution map.

[0140] Figure 13 is an example of a first distribution plot showing the intensity distribution of intensity characteristics calculated from spectral data of peanut skin and kernel. The first distribution plot shown in Figure 13(A) is a heatmap with added color and concentration by the magnitude of the intensity characteristics, and the first distribution plot shown in Figure 13(B) is a contour plot corresponding to the magnitude of the intensity characteristics.

[0141] In the first distribution diagram shown in Figure 13, the user indicates several specific locations to enhance sensing performance. Additionally, in Figure 13, the multiple (3) specific locations indicated by the user are marked with an asterisk (M3, M4, M5).

[0142] The wavelength combination determination unit 22 determines the wavelength combination of the first wavelength λ1 (=570nm) and the second wavelength λ2 (=690nm) from a specific position indicated by the mark M3, determines the wavelength combination of the first wavelength λ1 (=570nm) and the second wavelength λ2 (=930nm) from a specific position indicated by the mark M4, and determines the wavelength combination of the first wavelength λ1 (=690nm) and the second wavelength λ2 (=930nm) from a specific position indicated by the mark M5.

[0143] In the above case, the wavelengths determined by the wavelength combination are 3 wavelengths (570nm, 690nm, 930nm). Additionally, in the case of wavelength combinations corresponding to 3 specific locations, typically 6 wavelengths (2 wavelengths × 3) are determined. However, in this example, the wavelengths in the wavelength combinations corresponding to the 3 specific locations include repeating wavelengths, therefore 3 wavelengths (570nm, 690nm, 930nm) are determined.

[0144] A multispectral camera, having a first bandpass filter that transmits light in a band containing a defined wavelength (570 nm), a second bandpass filter that transmits light in a band containing a wavelength (690 nm), and a third bandpass filter that transmits light in a band containing a wavelength (930 nm), captures images of multiple subjects including the object to be detected, as shown in Figure 11, and simultaneously acquires a first image containing a wavelength (570 nm), a second image containing a wavelength (690 nm), and a third image containing a wavelength (930 nm).

[0145] Calculate the difference or ratio between the first image and the second image, the difference or ratio between the first image and the third image, and the difference or ratio between the second image and the third image in each band acquired by the multispectral camera, and create three second distribution maps representing these calculated differences or ratios.

[0146] Figure 14 is an example of a second distribution map synthesized from three second distribution maps created from the first, second, and third images of the subject shown in Figure 11 captured by a multispectral camera.

[0147] Figure 15 shows the case where three second distribution maps are combined to create one second distribution map.

[0148] By combining multiple second distribution maps to create a single second distribution map, it is possible to produce a second distribution map with a magnified dynamic range and enhanced sensing performance.

[0149] [Second Embodiment of the Data Processing Apparatus]

[0150] Figure 16 is a functional block diagram illustrating a second embodiment of the data processing apparatus according to the present invention.

[0151] In addition, in FIG16, the same symbols are used for the same parts as those of the data processing apparatus 10-1 of the first embodiment shown in FIG1, and their detailed descriptions are omitted.

[0152] The data processing apparatus 10-2 of the second embodiment shown in Figure 16 differs from the data processing apparatus 10-1 of the first embodiment in that it has a location extraction unit 24 instead of a user instruction receiving unit 20.

[0153] In the location extraction unit 24, a first distribution map representing the intensity distribution of intensity characteristics is added from the data conversion unit 16. The location extraction unit 24 performs the following processing: extracting the locations of one or more first distribution maps whose intensity characteristics exceed a threshold from the first distribution map.

[0154] For example, the location extraction unit 24 detects one or more regions in the first distribution map whose intensity characteristics exceed a threshold. By determining the centroid position of the detected regions, it can extract one or more locations in the first distribution map. Furthermore, within the one or more regions exceeding the threshold, the location with the highest intensity characteristic can be set as the location within that region.

[0155] The position information (coordinate information on the first distribution map) extracted by the position extraction unit 24 is output to the wavelength combination determination unit 22.

[0156] The wavelength combination determination unit 22 has wavelength information of each coordinate axis of the first distribution map. If the position (specific position) extracted by the position extraction unit 24 is input, the wavelength information of each coordinate axis shown at the specific position is determined, that is, the wavelength combination of the first wavelength λ1 and the second wavelength λ2.

[0157] The wavelength combination (first wavelength λ1, second wavelength λ2) determined by the wavelength combination determination unit 22 is output and displayed on the display device 30, and can also be output to the recording device or other external devices. When two or more wavelength combinations are determined, each determined wavelength combination is output.

[0158] Furthermore, as a variation of the data processing apparatus 10-1 of the first embodiment and the data processing apparatus 10-2 of the second embodiment, the data processing apparatus 10-2 of the second embodiment may determine a plurality of candidates for specific positions (a plurality of wavelength combinations), and display the candidates for specific positions overlapping on the first distribution map, and determine one or more specific positions from the candidates for specific positions overlapping on the first distribution map by means of user instruction.

[0159] [Multispectral Camera]

[0160] Figure 17 is a schematic diagram showing an example of a multispectral camera.

[0161] The multispectral camera (photographic device) 100 shown in Figure 17 consists of a photographic optical system 110 including lenses 110A, 110B and filter unit 120, an image sensor 130 and a signal processing unit 140.

[0162] The filter unit 120 is preferably composed of a polarizing filter unit 122 and a bandpass filter unit 124, and is disposed at or near the pupil position of the photographic optical system 110.

[0163] The polarizing filter unit 122 is composed of a first polarizing filter 122A and a second polarizing filter 122B that linearly polarize the light in the first pupil region and the second pupil region of the transmission photography optical system 110, respectively. The polarization directions of the first polarizing filter 122A and the second polarizing filter 122B are 90° apart.

[0164] The bandpass filter unit 124 is composed of a first bandpass filter (first wavelength selection element) 124A and a second bandpass filter (second wavelength selection element) 124B, which respectively select the wavelength bands of light in the first pupil region and the second pupil region of the transmission photography optical system 110. The first bandpass filter 124A selects a wavelength band containing one wavelength (first wavelength) in the determined wavelength combination, and the second bandpass filter 124B selects a wavelength band containing another wavelength (second wavelength) in the determined wavelength combination.

[0165] Therefore, the light in the first pupil region of the transmission imaging optical system 110 is linearly polarized by the first polarizing filter 122A, and only light containing the first wavelength is transmitted through the first bandpass filter 124A. On the other hand, the light in the second pupil region of the transmission imaging optical system 110 is linearly polarized by the second polarizing filter 122B (linearly polarized in a direction 90° different from the first polarizing filter 122A), and only light containing the second wavelength is transmitted through the second bandpass filter 124B.

[0166] The image sensor 130 is configured such that a first polarizing filter and a second polarizing filter with polarization directions 90° apart are regularly arranged among a plurality of pixels composed of photoelectric conversion elements arranged in a two-dimensional shape.

[0167] In addition, the first polarizing filter 122A has the same polarization direction as the first polarizing filter of the image sensor 130, and the second polarizing filter 122B has the same polarization direction as the second polarizing filter of the image sensor 130.

[0168] The signal processing unit 140 acquires a first image of a wavelength-selected band by the first bandpass filter 124A by reading pixel signals from the pixels of the first polarizing filter configured with the image sensor 130, and acquires a second image of a wavelength-selected band by the second bandpass filter 124B by reading pixel signals from the pixels of the second polarizing filter configured with the image sensor 130.

[0169] The first and second images acquired by the signal processing unit 140 are used for the detection of the target object as described above.

[0170] [Optical Components]

[0171] The optical element involved in this invention is an optical element manufactured according to a wavelength combination of a first wavelength and a second wavelength determined by the data processing apparatus 10-1 of the first embodiment shown in FIG1 or the data processing apparatus 10-2 of the second embodiment shown in FIG16.

[0172] That is, the optical element is equivalent to the bandpass filter unit 124 disposed in the multispectral camera 100 shown in FIG17, and has: a first wavelength selection element (first bandpass filter) for transmitting light including a band of a first wavelength determined by the data processing device; and a second wavelength selection element (second bandpass filter) for transmitting light including a band of a second wavelength determined by the data processing device.

[0173] The first bandpass filter and the second bandpass filter preferably have bandwidths in which the first wavelength and the second wavelength are respectively the center wavelengths and the transmission wavelengths of each other do not overlap.

[0174] [Photographic Optical System]

[0175] The photographic optical system of the present invention is equivalent to the photographic optical system 110 of the multispectral camera 100 shown in FIG. 17. The photographic optical system is configured such that an optical element equivalent to the bandpass filter unit 124 is disposed at or near the pupil position of the lenses 110A and 110B, and has an optical element that transmits light including a first wavelength band determined by the data processing device and a second wavelength selection element (second bandpass filter) that transmits light including a second wavelength band determined by the data processing device.

[0176] [Photographic installation]

[0177] The photographic apparatus involved in this invention is, for example, equivalent to the multispectral camera 100 shown in FIG17.

[0178] The multispectral camera 100 shown in Figure 17 includes: a photographic optical system (a photographic optical system in which the optical elements involved in the present invention are arranged at or near the pupil position) 110; and an image sensor (imaging element) 130, which captures optical images (a first optical image and a second optical image) formed by the photographic optical system 110.

[0179] The first optical image is an optical image transmitted through the first wavelength selection element of the optical element, and the second optical image is an optical image transmitted through the second wavelength selection element of the optical element.

[0180] The first optical image and the second optical image are respectively divided by the polarization filter unit 122 (first polarization filter 122A and second polarization filter 122B), which functions as a pupil division unit, and by the first polarization filter pupil corresponding to the first polarization filter 122A and the second polarization filter 122B on each pixel of the image sensor 130, and then captured by the image sensor 130. Thus, the multispectral camera 100 can simultaneously acquire a first image corresponding to the first optical image and a second image corresponding to the second optical image, each with a different wavelength band.

[0181] Furthermore, the imaging device is not limited to a structure having a pupil division section, etc., as shown in FIG17 of the multispectral camera 100. It is sufficient as long as it can capture at least a first optical image that transmits through the first wavelength selection element and a second optical image that transmits through the second wavelength selection element, and acquire a first image and a second image corresponding to the first optical image and the second optical image.

[0182] [Data Processing Methods]

[0183] The data processing method involved in this invention is a method for determining a wavelength combination of a first wavelength and a second wavelength suitable for the detection of a desired object, and is executed by a processor that is the processing body of each part of the data processing apparatus 10-1 and 10-2 shown in FIG1 and FIG16.

[0184] <First Implementation>

[0185] Figure 18 is a flowchart illustrating a first embodiment of the data processing method according to the present invention.

[0186] In Figure 18, the processor acquires spectral data of multiple subjects (step S10, data acquisition step). In step S10, for example, spectral data of multiple subjects are acquired from a hyperspectral camera that has captured images of multiple subjects with different spectral reflectivities.

[0187] Now, as shown in Figure 2, there are three subjects: paper 2A, leaf 2B, and insect 2C. Leaf 2B is placed on paper 2A, and insect 2C is on leaf 2B. The three spectral data A(λ), B(λ), and C(λ) of paper 2A, leaf 2B, and insect 2C are obtained (refer to Figure 3).

[0188] Next, the processor calculates the intensity characteristics at the first wavelength λ1 and the second wavelength λ2 selected from the wavelength regions of the spectral data of the multiple subjects obtained in step S10 based on the relationship between the two wavelengths, the first wavelength λ1 and the second wavelength λ2 (step S12, calculation step).

[0189] The intensity characteristics at the first and second wavelengths are the intensity difference and / or intensity ratio of the spectral dispersion at the first and second wavelengths.

[0190] If the first wavelength of the combination is set to λ1 and the second wavelength is set to λ2 in the spectral data A(λ) of paper 2A and the spectral data B(λ) of leaf 2B, then in step S12, spectral reflectance data A(λ1) and A(λ2) are obtained from the spectral data A(λ) of paper 2A at the first wavelength λ1 and the second wavelength λ2, and similarly, spectral reflectance data B(λ1) and B(λ2) are obtained from the spectral data B(λ) of leaf 2B at the first wavelength λ1 and the second wavelength λ2.

[0191] When calculating the strength difference as a strength characteristic, the absolute value of the difference between the spectral reflectance data A(λ1), A(λ2) of paper 2A at the first wavelength λ1 and the second wavelength λ2 and the difference between the spectral reflectance data B(λ1), B(λ2) of leaf 2B at the first wavelength λ1 and the second wavelength λ2 is calculated. When calculating the strength ratio as a strength characteristic, the absolute value of the ratio between the difference between the spectral reflectance data A(λ1), A(λ2) of paper 2A at the first wavelength λ1 and the second wavelength λ2 and the difference between the spectral reflectance data B(λ1), B(λ2) of leaf 2B at the first wavelength λ1 and the second wavelength λ2 is calculated.

[0192] Specifically, it is preferable to calculate the strength difference or strength ratio using the aforementioned [Formula 1] or [Formula 2].

[0193] Next, the processor converts the intensity characteristics calculated in step S12 into identification data for a specific subject relative to the wavelength region (step S14, data conversion step). Here, the identification data is a distribution map (first distribution map) representing the variation of intensity characteristics with wavelength as the variable. Furthermore, the first distribution map is a two-dimensional distribution map, and the coordinate axes of the two-dimensional distribution map are the first wavelength and the second wavelength.

[0194] Figure 4 is an example of a first distribution plot showing the intensity distribution of intensity characteristics calculated from spectral data of paper and leaves. The first distribution plot shown in Figure 4(A) is a heatmap with added color and concentration according to the magnitude of the intensity characteristics, and the first distribution plot shown in Figure 4(B) is a contour plot corresponding to the magnitude of the intensity characteristics.

[0195] Furthermore, Figure 5 is an example of a first distribution map showing the intensity distribution of intensity characteristics calculated from the spectral data of leaves and insects. The first distribution map shown in Figure 5(A) is a heatmap with added color and concentration by the magnitude of the intensity characteristics, and the first distribution map shown in Figure 5(B) is a contour map corresponding to the magnitude of the intensity characteristics.

[0196] Next, the processor outputs the first distribution map, which is the identification data converted in step S14, to the display device 30, which is the destination of the external output (Fig. 1) (step S16, output step). Thus, in this example, the first distribution map shown in Figs. 4 and 5 is displayed on the display device 30.

[0197] The processor determines whether a specific location on the first distribution map has been indicated by the user (step S18). Furthermore, the user can observe the first distribution map displayed on the display device 30 while indicating a specific location (e.g., a location with high intensity characteristics) using a pointing device such as a mouse.

[0198] In this example, since the insect 2C on the leaf 2B is the object of detection, the specific location indicated by the user on the first distribution map shown in Figure 4 is preferably not identified at the location of paper 2A and leaf 2B (where the intensity characteristics are small). On the other hand, the location of leaf 2B and insect 2C (where the intensity characteristics are large) is preferably identified on the first distribution map shown in Figure 5.

[0199] The user preferably finds the specific location of the unidentified paper 2A and leaf 2B and the identified leaf 2B and insect 2C from the two first distribution maps displayed on the display device 30, and indicates the specific location.

[0200] If a specific location on the first distribution map is indicated by the user (in the case of "Yes"), the processor determines a wavelength combination of a first wavelength and a second wavelength for detecting the object among multiple subjects based on the specific location (step S20, determination step). In this example, based on the specific location indicated by the asterisk mark M1 on the first distribution map shown in Figures 4 and 5, a wavelength combination of a first wavelength λ1 (=750nm) and a second wavelength λ2 (=950nm) representing the coordinates of that specific location is determined.

[0201] The wavelength combination (750nm, 950nm) determined in this way is suitable for detecting insect 2C as the object of detection. The information of the wavelength combination is output and displayed on the display device 30, and also output to the recording device and other external devices (step S22).

[0202] <Second Implementation Method>

[0203] Figure 19 is a flowchart illustrating a second embodiment of the data processing method according to the present invention.

[0204] In addition, in FIG19, the same step numbers are used for the same steps as those in the data processing method of the first embodiment shown in FIG18, and their detailed descriptions are omitted.

[0205] The data processing method of the second embodiment shown in FIG19 differs from the data processing method of the first embodiment shown in FIG18 in that the processing in step S30 is performed instead of the processing in step S18 shown in FIG18.

[0206] In step S30 shown in Figure 19, the following process is performed: on the first distribution map representing the intensity distribution of intensity characteristics, the positions of one or more first distribution maps where the intensity characteristics exceed the threshold are extracted.

[0207] For example, in step S30, one or more regions with intensity characteristics exceeding a threshold are detected in the first distribution map. By determining the centroid position of the detected regions, one or more positions in the first distribution map can be extracted. Furthermore, within one or more regions exceeding the threshold, the position with the highest intensity characteristic can be set as the position within that region.

[0208] The automatically extracted location is set as a specific location on the first distribution map. That is, the automatically extracted specific location can be used instead of the specific location indicated by the user in the first embodiment.

[0209] Furthermore, as a variation of the data processing method of the first and second embodiments, multiple candidates for specific locations (multiple wavelength combinations) can be automatically determined, and the candidates for specific locations can be overlaid on the first distribution map. From the candidates for specific locations overlaid on the first distribution map, one or more specific locations can be determined by user instruction.

[0210] <Third Implementation Method>

[0211] Figure 20 is a flowchart illustrating a third embodiment of the data processing method according to the present invention.

[0212] Furthermore, the third embodiment shown in FIG20 illustrates the case where the target object is detected using a wavelength combination of the first wavelength λ1 and the second wavelength λ2 determined by the data processing methods of the first and second embodiments shown in FIG18 and FIG19. Also, when the insect 2C on the leaf 2B placed on paper 2A is used as the target object, as shown in FIG2, the suitable wavelength combination for detecting the insect 2C is the first wavelength λ1 (=750nm) and the second wavelength λ2 (=950nm).

[0213] In Figure 20, if the data processing methods of the first and second embodiments shown in Figures 18 and 19 determine a wavelength combination (e.g., 750 nm, 950 nm) suitable for detecting the target object, then a multispectral camera 100 (refer to Figure 17) having a first wavelength selection element (first bandpass filter) that transmits light including the first wavelength λ1 and a second wavelength selection element (second bandpass filter) that transmits light including the second wavelength λ2 can simultaneously capture images of multiple subjects including the target object (step S40).

[0214] The processor acquires a first image containing a first wavelength band and a second image containing a second wavelength band from the multispectral camera 100 (step S42, image acquisition step).

[0215] Next, the processor calculates the difference or ratio between the acquired first image and the second image (step S44), and creates a distribution map representing the calculated difference or ratio (second distribution map) (step S46, second distribution map creation step).

[0216] Figure 6 is a diagram showing an example of a second distribution plot illustrating the difference or ratio between the first image and the second image obtained from a multispectral camera that captured the subject shown in Figure 2.

[0217] Next, the processor detects the target object based on the created second distribution map (step S48). In the second distribution map shown in Figure 6, the contrast between the insect 2C and the background paper 2A and leaf 2B becomes clear. Therefore, by using the second distribution map, the position and number of insects 2C on the second distribution map can be detected with high accuracy.

[0218] [other]

[0219] The multiple subjects including the object to be detected are not limited to the subjects in this embodiment, and various subjects can be considered. Furthermore, the determined wavelength combinations can be two or more groups, in which case a multispectral camera with three or more wavelength selection elements is suitable.

[0220] In this embodiment, for example, the hardware structure of the processing unit that performs various processes constituting the data processing device is as shown below, which are various processors.

[0221] Among various processors, there are general-purpose processors that execute software (programs) and function as various processing units, namely CPUs (Central Processing Units); processors that can have their circuit structure changed after manufacturing, such as FPGAs (Field Programmable Gate Arrays), namely Programmable Logic Devices (PLDs); and processors that have circuit structures specifically designed to perform specific processes, such as ASICs (Application Specific Integrated Circuits), namely dedicated circuits.

[0222] A processing unit can be composed of one of these various processors, or it can be composed of two or more processors of the same or different types (e.g., multiple FPGAs or a combination of CPU and FPGA). Furthermore, a single processor can also constitute multiple processing units. As examples of a single processor constituting multiple processing units, firstly, there is the following: Represented by computers such as client and server computers, a single processor is composed of a combination of one or more CPUs and software, and this processor functions as multiple processing units. Secondly, there is the following: Represented by systems on a chip (SoC), a processor that implements the overall system functionality including multiple processing units using a single integrated circuit (IC) chip. Thus, regarding various processing units, as a hardware structure, one or more of the aforementioned processors are used.

[0223] Moreover, more specifically, the hardware structure of these various processors is a circuit composed of combined semiconductor elements and other circuit elements.

[0224] Furthermore, the present invention includes a data processing program installed on a computer to enable the computer to function as a data processing device according to the present invention, and a non-volatile storage medium recording the data processing program.

[0225] Moreover, the present invention is not limited to the above-described embodiments, and various modifications can be made without departing from the spirit of the present invention.

[0226] Symbol Explanation

[0227] 1- Hyperspectral camera, 2A- Paper, 2B- Leaf, 2C- Insect, 4A, 6A- Paper, 4B, 4C- Soil, 6B- Peanut shell, 6C- Peanut kernel, 10-1, 10-2- Data processing unit, 12- Data acquisition unit, 14- Intensity characteristic calculation unit, 16- Data conversion unit, 18- Output unit, 20- User instruction receiving unit, 22- Wavelength combination determination unit, 24- Position extraction unit, 30- Display device, 32- Operation unit, 100- Multispectral camera, 110- Camera The optical system includes: 110A - lens; 110B - lens; 120 - filter unit; 122 - polarizing filter unit; 122A - first polarizing filter; 122B - second polarizing filter; 124 - bandpass filter unit; 124A - first bandpass filter; 124B - second bandpass filter; 130 - image sensor; 140 - signal processing unit; M1, M2, M3, M4, M5 - markers; S10 to S22, S30, S40 to S46 - steps.

Claims

1. A data processing apparatus comprising a processor, wherein, The processor performs the following processing: data acquisition processing to acquire spectral data of multiple subjects; calculation processing to calculate the intensity characteristics at the first wavelength and the second wavelength selected from the wavelength region of the acquired spectral data of the multiple subjects based on the relationship between the first wavelength and the second wavelength. The data conversion process converts the intensity characteristics calculated in the computational process into identification data of a specific subject relative to the wavelength region. The identification data is output externally, and the intensity characteristics are intensity difference and / or intensity ratio. If the spectral data of the first subject in the spectral data of the plurality of subjects is set as A(λ), the spectral data of the second subject is set as B(λ), the selected first wavelength is set as λ1, and the second wavelength is set as λ2, then the intensity difference and / or intensity ratio is calculated by intensity difference = |[A(λ1)-A(λ2)]-[B(λ1)-B(λ2)]| and intensity ratio = |[A(λ1)-A(λ2)] / [B(λ1)-B(λ2)]|.

2. A data processing apparatus comprising a processor, wherein, The processor performs the following processing: data acquisition processing to acquire spectral data of multiple subjects; calculation processing to calculate the intensity characteristics at the first wavelength and the second wavelength selected from the wavelength region of the acquired spectral data of the multiple subjects based on the relationship between the first wavelength and the second wavelength. The data conversion process converts the intensity characteristics calculated in the computational process into identification data of a specific subject relative to the wavelength region. The identification data is output externally, and the intensity characteristics are intensity difference and / or intensity ratio. If the spectral data of the first subject in the spectral data of the plurality of subjects is set as A(λ), the spectral data of the second subject is set as B(λ), the selected first wavelength is set as λ1, and the second wavelength is set as λ2, then the intensity difference and / or intensity ratio are calculated by the following [Equation 1] and / or [Equation 2]: [Equation 1] [Formula 2] 。 3. The data processing apparatus according to claim 1 or 2, wherein, The identification data is a first distribution map representing the variation of the intensity characteristic with wavelength as the variable.

4. The data processing apparatus according to claim 1 or 2, wherein, The plurality of subjects includes a first subject, a second subject, and a third subject. The data acquisition process acquires the spectral data of the first subject, the second subject, and the third subject. The calculation process calculates two or more intensity characteristics from the intensity characteristics at the first and second wavelengths of the spectral data of the first and second subjects, the intensity characteristics at the first and second wavelengths of the spectral data of the second and third subjects, and the intensity characteristics at the first and second wavelengths of the spectral data of the first and third subjects. The data conversion process converts the two or more intensity characteristics into two or more recognition data.

5. A data processing apparatus comprising a processor, wherein, The processor performs the following processing: data acquisition processing to acquire spectral data of multiple subjects; calculation processing to calculate the intensity characteristics at the first wavelength and the second wavelength selected from the wavelength region of the acquired spectral data of the multiple subjects based on the relationship between the first wavelength and the second wavelength. The data conversion process converts the intensity characteristics calculated in the computational process into identification data of a specific subject relative to the wavelength region. The identification data is output externally as a first distribution map representing the change of the intensity characteristic with wavelength as the variable. The first distribution map is a two-dimensional distribution map, and the coordinate axes of the two-dimensional distribution map are the first wavelength and the second wavelength.

6. The data processing apparatus according to claim 5, wherein, The destination of the external output of the identification data is a display device, and the processor performs the following processing: receiving a specific position displayed on the first distribution map on the display device by means of a user instruction; and determining a wavelength combination of the first wavelength and the second wavelength for detecting the object in the plurality of subjects based on the specific position.

7. The data processing apparatus according to claim 5, wherein, The processor performs the following processing: extracting one or more locations in the first distribution map where the intensity characteristic exceeds a threshold; and determining one or more wavelength combinations of the first wavelength and the second wavelength for detection of the detection object in the plurality of subjects based on the extracted locations.

8. The data processing apparatus according to claim 7, wherein, The destination of the external output of the identification data is a display device, and the processor performs the following processing: overlapping the candidates of a specific position in one or more wavelength combinations of the first wavelength and the second wavelength on the first distribution map displayed in the display device; receiving a specific position from the candidates of the specific position by user instruction; and determining the wavelength combination of the first wavelength and the second wavelength based on the received specific position.

9. The data processing apparatus according to any one of claims 5 to 8, wherein, The plurality of subjects includes a first subject, a second subject, and a third subject. The data acquisition process acquires the spectral data of the first subject, the second subject, and the third subject. The calculation process calculates two or more intensity characteristics from the intensity characteristics at the first and second wavelengths of the spectral data of the first and second subjects, the intensity characteristics at the first and second wavelengths of the spectral data of the second and third subjects, and the intensity characteristics at the first and second wavelengths of the spectral data of the first and third subjects. The data conversion process converts the two or more intensity characteristics into two or more recognition data.

10. An optical element having a first wavelength selection element and a second wavelength selection element, wherein, The first wavelength selection element allows transmission of the band of the first wavelength determined by the data processing apparatus according to any one of claims 6 to 8, and the second wavelength selection element allows transmission of the band of the second wavelength determined by the data processing apparatus according to any one of claims 6 to 8.

11. A photographic optical system which arranges the optical element of claim 10 at or near the pupil position.

12. A photographic apparatus comprising: the photographic optical system of claim 11; and an imaging element for capturing images of a first optical image transmitted through the first wavelength selection element and a second optical image transmitted through the second wavelength selection element, both imaged by the photographic optical system.

13. A data processing method, the data processing method comprising: The data acquisition steps involve acquiring spectral data from multiple subjects. The calculation steps involve calculating the intensity characteristics at the first and second wavelengths selected from the wavelength regions of the acquired spectral data of the multiple subjects, based on the relationship between the first and second wavelengths. The data conversion step converts the intensity characteristics calculated in the calculation step into identification data of a specific subject relative to the wavelength region. The identification data is output externally, and the processor executes each step of the process. The intensity characteristic is the intensity difference and / or intensity ratio. If the spectral data of the first subject in the spectral data of the plurality of subjects is set as A(λ), the spectral data of the second subject is set as B(λ), the selected first wavelength is set as λ1, and the second wavelength is set as λ2, then the intensity difference and / or intensity ratio is calculated by intensity difference = |[A(λ1)-A(λ2)]-[B(λ1)-B(λ2)]| and intensity ratio = |[A(λ1)-A(λ2)] / [B(λ1)-B(λ2)]|.

14. A data processing method, the data processing method comprising: The data acquisition steps involve acquiring spectral data from multiple subjects. The calculation steps involve calculating the intensity characteristics at the first and second wavelengths selected from the wavelength regions of the acquired spectral data of the multiple subjects, based on the relationship between the first and second wavelengths. The data conversion step converts the intensity characteristics calculated in the calculation step into identification data of a specific subject relative to the wavelength region. The process includes an output step, where the identified data is externally output and processed by a processor. The intensity characteristic is the intensity difference and / or intensity ratio. If the spectral data of the first subject in the spectral data of the plurality of subjects is set as A(λ), the spectral data of the second subject is set as B(λ), the selected first wavelength is set as λ1, and the second wavelength is set as λ2, then the intensity difference and / or intensity ratio is calculated using the following [Equation 1] and / or [Equation 2]: [Equation 1] [Formula 2] 。 15. The data processing method according to claim 13 or 14, wherein, The identification data is a first distribution map representing the variation of the intensity characteristic with wavelength as the variable.

16. The data processing method according to claim 13 or 14, wherein, The plurality of subjects includes a first subject, a second subject, and a third subject. In the data acquisition step, spectral data of the first subject, the second subject, and the third subject are acquired. In the calculation step, at least two intensity characteristics are calculated from the intensity characteristics at the first and second wavelengths of the spectral data of the first and second subjects, the intensity characteristics at the first and second wavelengths of the spectral data of the second and third subjects, and the intensity characteristics at the first and second wavelengths of the spectral data of the first and third subjects. In the data conversion step, the two or more intensity characteristics are converted into two or more identification data.

17. A recording medium that is non-transitory and computer-readable, and which records a program that causes a computer to perform the data processing method of any one of claims 13 to 16.

18. A data processing method, wherein, The data processing method includes: a data acquisition step, acquiring spectral data of multiple subjects; a calculation step, calculating the intensity characteristics at the first wavelength and the second wavelength selected from the wavelength region of the acquired spectral data of the multiple subjects based on the relationship between the first wavelength and the second wavelength; a data conversion step, converting the intensity characteristics calculated in the calculation step into identification data of a specific subject relative to the wavelength region; and an output step, externally outputting the identification data, and having the processor execute the processing of each step, wherein the identification data is a first distribution map representing the change of the intensity characteristics with wavelength as the variable, the first distribution map being a two-dimensional distribution map, and the coordinate axes of the two-dimensional distribution map being the first wavelength and the second wavelength.

19. The data processing method according to claim 18, wherein, The destination of the external output of the identification data is a display device, and the data processing method includes: a step of receiving a specific position displayed on the first distribution map on the display device by a user instruction; and a determination step of determining a wavelength combination of the first wavelength and the second wavelength for detecting an object among the plurality of subjects based on the specific position.

20. The data processing method according to claim 18, wherein, The data processing method includes: a step of extracting one or more locations in the first distribution map where the intensity characteristic exceeds a threshold; and a determination step of determining one or more wavelength combinations of the first wavelength and the second wavelength for detecting a detection object in the plurality of subjects based on the extracted locations.

21. The data processing method according to claim 20, wherein, The destination of the external output of the identification data is a display device, and the data processing method includes the following steps: displaying candidates of specific positions in one or more wavelength combinations of the first wavelength and the second wavelength overlapping on the first distribution map displayed in the display device; receiving specific positions from the candidates of the specific positions by user instruction; and determining the wavelength combination of the first wavelength and the second wavelength based on the received specific positions.

22. The data processing method according to any one of claims 18 to 21, wherein, The plurality of subjects includes a first subject, a second subject, and a third subject. In the data acquisition step, spectral data of the first subject, the second subject, and the third subject are acquired. In the calculation step, at least two intensity characteristics are calculated from the intensity characteristics at the first and second wavelengths of the spectral data of the first and second subjects, the intensity characteristics at the first and second wavelengths of the spectral data of the second and third subjects, and the intensity characteristics at the first and second wavelengths of the spectral data of the first and third subjects. In the data conversion step, the two or more intensity characteristics are converted into two or more identification data.

23. The data processing method according to any one of claims 19 to 21, wherein, The data processing method includes: an image acquisition step, which acquires a first image containing the first wavelength band of the determined wavelength combination and a second image containing the second wavelength band; a step of calculating the difference or ratio between the acquired first image and the second image; and a second distribution map production step, which produces a second distribution map representing the calculated difference or ratio.

24. The data processing method according to claim 23, wherein, In the determination step, two or more wavelength combinations of the first wavelength and the second wavelength for detecting the object in the plurality of subjects are determined according to the first distribution map. In the image acquisition step, a first image containing the first wavelength band of each of the two or more wavelength combinations and a second image containing the second wavelength band are acquired respectively. In the second distribution map creation step, the second distribution map is created for each of the two or more wavelength combinations. The data processing method includes the step of combining the created two or more second distribution maps to create one second distribution map.

25. The data processing method according to claim 24, wherein, The data processing method includes the step of detecting the target object based on the second distribution map that has been created.

26. A recording medium that is non-transitory and computer-readable, and which records a program that causes a computer to perform the data processing method of any one of claims 18 to 25.

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