Data processing device, method, program and recording medium

Abstract

The present invention provides: a data processing device, method, and program whereby it is possible to easily specify a wavelength combination of a first wavelength and a second wavelength that are suitable for detecting an object to be detected from a plurality of subjects that include the object to be detected; an optical element; an imaging optical system; and an imaging device. A data processing device comprising a processor, wherein the processor comprises: data acquisition processing for acquiring spectral data of a plurality of subjects; calculation processing for calculating, on the basis of a relationship between two wavelengths consisting of a first wavelength and a second wavelength selected from the wavelength region of the spectral data of the acquired plurality of subjects, an intensity characteristic at the first wavelength and the second wavelength; data conversion processing for converting the intensity characteristic calculated by the calculation processing into identification data of a specific subject with respect to the wavelength region; and output processing for outputting the identification data to the outside.

Description

[Technical Field]

[0001] The present invention relates to a data processing device, a method, a program, an optical element, an imaging optical system, and an imaging device, and more particularly to the selection of a wavelength suitable for detecting an object to be detected. [Background technology]

[0002] Conventionally, there are hyperspectral cameras that can perform spectral sensing using 100 or more wavelengths.

[0003] This type of hyperspectral camera measures many wavelengths, so it is common to sense the target object by searching for wavelengths where reflection or absorption changes suddenly (for example, by taking the second derivative of the spectral reflectance in the wavelength direction and searching for the peak wavelength).

[0004] Furthermore, a foreign matter contamination inspection device has been proposed that inspects for foreign matter such as hair contained in an object to be inspected, such as food, cosmetics, or medicine (Patent Document 1).

[0005] This foreign matter contamination inspection device irradiates the object to be inspected with light of a first wavelength band and light of a second wavelength band, which have mutually different relative relationships between the luminance values ​​of the foreign matter and the luminance values ​​of the object to be inspected, and acquires a first spectral image based on the luminance values ​​of the first wavelength band and a second spectral image based on the luminance values ​​of the second wavelength band, and performs an operation between the acquired first spectral image and second spectral image to acquire a foreign matter extracted image that can be identified as the object to be inspected and that contains foreign matter contained in the object to be inspected. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Laid-Open No. 2007-178407 Summary of the Invention [Problem to be solved by the invention]

[0007] One embodiment of the technology of the present disclosure provides a data processing device, a method, a program, an optical element, an imaging optical system, and an imaging device that can easily identify a wavelength combination of a first wavelength and a second wavelength that is suitable for detecting a detection target from multiple subjects including the detection target. [Means for solving the problem]

[0008] A first aspect of the invention is a data processing device including a processor, the processor including a data acquisition process for acquiring spectral data of a plurality of subjects, a calculation process for calculating intensity characteristics at a first wavelength and a second wavelength selected from the wavelength ranges of the acquired spectral data of the plurality of subjects based on the relationship between the two wavelengths, the first wavelength and the second wavelength, a data conversion process for converting the intensity characteristics calculated in the calculation process into identification data of a specific subject for the wavelength range, and an output process for externally outputting the identification data.

[0009] In the data processing device according to the second aspect of the present invention, the intensity characteristic is preferably an intensity difference and / or an intensity ratio.

[0010] In the data processing device according to the third aspect of the present invention, the intensity difference and / or the intensity ratio can be calculated by the following formula 1 and / or formula 2, where A(λ) is the spectral data of a first object, B(λ) is the spectral data of a second object, and λ1 is the selected first wavelength and λ2 is the selected second wavelength:

[0011]

number

[0012]

number

[0013] It is preferable to calculate it by the following formula.

[0014] In the data processing device according to the fourth aspect of the present invention, the identification data is preferably a first map that represents a change in intensity characteristics with wavelength as a variable.

[0015] In the data processing device according to the fifth aspect of the present invention, it is preferable that the first map is a two-dimensional map, and the coordinate axes of the two-dimensional map are the first wavelength and the second wavelength.

[0016] In a data processing device according to a sixth aspect of the present invention, it is preferable that the identification data is externally output to a display device, and the processor performs a process of accepting a specific position on a first map displayed on the display device by a user instruction, and a process of identifying a wavelength combination of a first wavelength and a second wavelength to be used for detecting a detection target among a plurality of subjects based on the specific position.

[0017] In the data processing device according to the seventh aspect of the present invention, it is preferable that the processor performs the steps of extracting one or more positions in the first map where the intensity characteristic exceeds a threshold value from the first map, and identifying one or more wavelength combinations of a first wavelength and a second wavelength to be used for detecting a detection target among the multiple subjects based on the extracted positions.

[0018] In a data processing device according to an eighth aspect of the present invention, it is preferable that a destination of an external output of the identification data is a display device, and the processor performs a process of superimposing candidates for specific positions for one or more wavelength combinations of a first wavelength and a second wavelength on a first map displayed on the display device, a process of accepting a specific position from the candidates for specific positions in response to a user instruction, and a process of identifying the wavelength combination of the first wavelength and the second wavelength based on the accepted specific position.

[0019] In a data processing device according to a ninth aspect of the present invention, it is preferable that the multiple subjects include a first subject, a second subject, and a third subject, the data acquisition process acquires spectral data of the first subject, spectral data of the second subject, and spectral data of the third subject, the calculation process calculates two or more intensity characteristics from among intensity characteristics at a first wavelength and a second wavelength of the spectral data of the first subject and the spectral data of the second subject, intensity characteristics at the first wavelength and the second wavelength of each of the spectral data of the second subject and the third subject, and intensity characteristics at the first wavelength and the second wavelength of the spectral data of the first subject and the spectral data of the third subject, and the intensity characteristics at the first wavelength and the second wavelength of the spectral data of the first subject and the spectral data of the third subject, and the data conversion process converts the two or more intensity characteristics into two or more identification data.

[0020] A tenth aspect of the invention is an optical element having a first wavelength selection element and a second wavelength selection element, wherein the first wavelength selection element transmits a wavelength band of a first wavelength identified by a data processing device of any one of the sixth to eighth aspects, and the second wavelength selection element transmits a wavelength band of a second wavelength identified by the data processing device of any one of the sixth to eighth aspects.

[0021] An eleventh aspect of the invention is a photographic optical system in which the optical element of the tenth aspect is disposed at or near the pupil position.

[0022] A twelfth aspect of the invention is an imaging device including the imaging optical system of the eleventh aspect and an imaging element that captures a first optical image that has been transmitted through a first wavelength selecting element and a second optical image that has been transmitted through a second wavelength selecting element, both of which are formed by the imaging optical system.

[0023] The invention according to a thirteenth aspect is a data processing program including a data acquisition step of acquiring spectral data of a plurality of subjects; a calculation step of calculating intensity characteristics at a first wavelength and a second wavelength selected from the wavelength ranges of the acquired spectral data of the plurality of subjects based on the relationship between the two wavelengths, the first wavelength and the second wavelength; a data conversion step of converting the intensity characteristics calculated in the calculation step into identification data of a specific subject for the wavelength range; and an output step of externally outputting the identification data, wherein a processor executes processing of each step.

[0024] In the data processing method according to the fourteenth aspect of the present invention, the intensity characteristic is preferably an intensity difference and / or an intensity ratio.

[0025] In the data processing method according to the fifteenth aspect of the present invention, the intensity difference and / or the intensity ratio can be calculated by the following formula 1 and / or formula 2, where A(λ) is the spectral data of a first object, B(λ) is the spectral data of a second object, λ1 is the selected first wavelength, and λ2 is the selected second wavelength:

[0026]

number

[0027]

number

[0028] It is preferable to calculate it by the following formula.

[0029] In the data processing method according to the sixteenth aspect of the present invention, the identification data is preferably a first map representing a change in intensity characteristics with wavelength as a variable.

[0030] In the data processing method according to the seventeenth aspect of the present invention, it is preferable that the first map is a two-dimensional map, and the coordinate axes of the two-dimensional map are the first wavelength and the second wavelength.

[0031] In the data processing method according to the 18th aspect of the present invention, it is preferable that the destination of the external output of the identification data is a display device, and the method includes the steps of: accepting a specific position on a first map displayed on the display device by user instruction; and specifying a wavelength combination of a first wavelength and a second wavelength to be used for detecting a detection target among a plurality of subjects based on the specific position.

[0032] In the data processing method according to the 19th aspect of the present invention, it is preferable to include the steps of extracting from the first map one or more positions in the first map where the intensity characteristic exceeds a threshold value, and identifying, based on the extracted positions, one or more wavelength combinations of first wavelengths and second wavelengths to be used for detecting a detection target among a plurality of subjects.

[0033] In the data processing method according to the twentieth aspect of the present invention, it is preferable that the identification data is externally output to a display device, and the method includes the steps of superimposing candidates for specific positions for one or more wavelength combinations of a first wavelength and a second wavelength on a first map displayed on the display device, accepting a specific position from the candidates for specific positions in response to a user instruction, and identifying the wavelength combination of the first wavelength and the second wavelength based on the accepted specific position.

[0034] In a data processing method according to a 21st aspect of the present invention, it is preferable that the multiple subjects include a first subject, a second subject, and a third subject, the data acquiring step acquires spectral data of the first subject, spectral data of the second subject, and spectral data of the third subject, the calculating step calculates two or more intensity characteristics from among intensity characteristics at a first wavelength and a second wavelength of the spectral data of the first subject and the spectral data of the second subject, intensity characteristics at the first wavelength and a second wavelength of each of the spectral data of the second subject and the third subject, and intensity characteristics at the first wavelength and a second wavelength of the spectral data of the first subject and the spectral data of the third subject, and the intensity characteristics at the first wavelength and a second wavelength of the spectral data of the first subject and the spectral data of the third subject, and

[0035] In the data processing method according to the 22nd aspect of the present invention, it is preferable to include an image acquisition step of acquiring a first image of a wavelength band including a first wavelength in the identified wavelength combination and a second image of a wavelength band including a second wavelength, a step of calculating a difference or ratio between the acquired first image and second image, and a second map creation step of creating a second map indicating the calculated difference or ratio.

[0036] In the data processing method according to the 23rd aspect of the present invention, it is preferable that the identification step includes a step of identifying two or more wavelength combinations of a first wavelength and a second wavelength to be used for detecting a detection target among a plurality of subjects based on the first map, the image acquisition step of acquiring a first image in a wavelength band including the first wavelength and a second image in a wavelength band including the second wavelength for each of the two or more wavelength combinations, and the second map creation step of creating a second map for each of the two or more wavelength combinations and combining the two or more created second maps to create a single second map.

[0037] The data processing method according to the 24th aspect of the present invention preferably includes a step of detecting the detection target based on the created second map.

[0038] The invention according to a 25th aspect is a data processing program that causes a computer to realize the following functions: acquiring spectral data of a plurality of subjects; calculating intensity characteristics at a first wavelength and a second wavelength selected from the wavelength ranges of the acquired spectral data of the plurality of subjects based on the relationship between the two wavelengths, the first wavelength and the second wavelength; converting the calculated intensity characteristics into identification data of a specific subject for the wavelength range; and externally outputting the identification data. [Brief explanation of the drawings]

[0039] [Figure 1] FIG. 1 is a functional block diagram showing a first embodiment of a data processing device according to the present invention. [Figure 2] FIG. 2 is a diagram showing a plurality of subjects including a detection target photographed by a hyperspectral camera. [Figure 3] FIG. 3 is a graph showing the spectral data for paper, a leaf, and an insect, respectively. [Figure 4] FIG. 4 is a diagram showing an example of a first map showing the intensity distribution of the intensity characteristics calculated from the spectral data of paper and leaves. [Figure 5] FIG. 5 is a diagram showing an example of a first map showing the intensity distribution of the intensity characteristics calculated from the spectral data of the leaf and the insect. [Figure 6] FIG. 6 is a diagram showing an example of a second map indicating the difference or ratio between the first image and the second image acquired from the multispectral camera that captured the subject shown in FIG. [Figure 7] FIG. 7 is a diagram showing a plurality of subjects including other detection targets photographed by a hyperspectral camera. [Figure 8] FIG. 8 is a graph showing the spectral data of two types of soil, one in paper form and one in powder form. [Figure 9] FIG. 9 is a diagram showing an example of a first map showing the intensity distribution of the strength characteristics calculated from the spectral data of two types of soil. [Figure 10] FIG. 10 is a diagram showing an example of a second map indicating the difference or ratio between the first image and the second image acquired by the multispectral camera that captured the subject shown in FIG. [Figure 11] FIG. 11 is a diagram showing a plurality of subjects including other detection targets photographed by a hyperspectral camera. [Figure 12] FIG. 12 is a graph showing the spectral data of paper, peanut skins, and peanut kernels, respectively. [Figure 13] FIG. 13 is a diagram showing an example of a first map showing the intensity distribution of the intensity characteristics calculated from the spectral data of the skin and kernel of a peanut. [Figure 14] FIG. 14 is a diagram showing an example of one second map obtained by combining three second maps created from the first image, the second image, and the third image acquired from a multispectral camera that captured the subject shown in FIG. 11. [Figure 15]FIG. 15 is a diagram showing how one second map is created by combining three second maps. [Figure 16] FIG. 16 is a functional block diagram showing a second embodiment of a data processing device according to the present invention. [Figure 17] FIG. 17 is a schematic diagram illustrating an example of a multispectral camera. [Figure 18] FIG. 18 is a flowchart showing a first embodiment of the data processing method according to the present invention. [Figure 19] FIG. 19 is a flowchart showing a second embodiment of the data processing method according to the present invention. [Figure 20] FIG. 20 is a flowchart showing a third embodiment of the data processing method according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0040] Preferred embodiments of a data processing device, a method, a program, an optical element, a photographing optical system, and a photographing device according to the present invention will be described below with reference to the accompanying drawings.

[0041] [First embodiment of data processing device] FIG. 1 is a functional block diagram showing a first embodiment of a data processing device according to the present invention.

[0042] The data processing device 10-1 of the first embodiment can be configured by a personal computer, a workstation, or the like equipped with hardware such as a processor, a memory, an input / output interface, and the like.

[0043] The processor is composed of a CPU (Central Processing Unit) and the like, and controls each part of the data processing device 10-1 in an integrated manner, and also functions as, for example, the data acquisition part 12, the intensity characteristic calculation part 14, the data conversion part 16, the output part 18, the user instruction reception part 20, and the wavelength combination identification part 22 shown in FIG. 1.

[0044] The data acquisition unit 12 performs a data acquisition process to acquire spectral data of multiple subjects. In this example, the data acquisition unit 12 acquires the spectral data of multiple subjects directly from the hyperspectral camera 1 that captures images of multiple subjects with different spectral reflectances, or indirectly via a recording medium, a network, or the like.

[0045] FIG. 2 is a diagram showing a plurality of subjects including a detection target photographed by a hyperspectral camera.

[0046] The multiple subjects shown in FIG. 2 are a first subject, paper 2A, a second subject, leaf 2B, and a third subject, insect 2C, with leaf 2B placed on paper 2A and insect 2C on leaf 2B.

[0047] When acquiring spectral data of multiple subjects from the hyperspectral camera 1, for example, the multiple subjects shown in Fig. 2 are photographed by the hyperspectral camera 1, and the photographed images are displayed on the monitor screen of the hyperspectral camera 1. Then, when a user designates an area of ​​paper 2A, an area of ​​leaf 2B, and an area of ​​insect 2C on the monitor screen of the hyperspectral camera 1, spectral data indicating the spectral reflectance of paper 2A, spectral data indicating the spectral reflectance of leaf 2B, and spectral data indicating the spectral reflectance of insect 2C are acquired from the hyperspectral camera 1.

[0048] In this example, the object to be detected is an insect 2C, and the present invention relates to a technology for searching for a wavelength combination of two wavelengths that have a large change in reflection or absorption relative to the background subject (paper 2A or leaf 2B) in order to detect the object to be detected (insect 2C).

[0049] FIG. 3 is a graph showing the spectral data for paper, a leaf, and an insect, respectively.

[0050] In Fig. 3, the vertical axis of the graph indicates the spectral reflectance when the reflectance of the reference plate is set to 1. The spectral reflectance is the average or median value of the reflectance of the entire object or a partial region thereof.

[0051] 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 Figure 3. Note that the spectral data of each subject does not necessarily have to be acquired from the hyperspectral camera 1. For example, if the spectral data of the subject (including some of the subjects) is known, the spectral data may be acquired.

[0052] The intensity characteristic calculation unit 14 performs a calculation process to calculate intensity characteristics at a first wavelength and a second wavelength selected from the wavelength ranges of the spectral data of multiple subjects acquired by the data acquisition unit 12, based on the relationship between the two wavelengths. The intensity characteristics at the first wavelength and the second wavelength are used to evaluate the sensing performance of the subject, and the larger the intensity characteristics, the easier it is to identify the subject. Note that the first wavelength and the second wavelength may have a width.

[0053] The intensity characteristics at the first wavelength and the second wavelength are the intensity difference and / or intensity ratio of the light beams at the first wavelength and the second wavelength.

[0054] If the spectral data of the first subject (paper 2A) is A(λ) and the spectral data of the second subject (leaf 2B) is B(λ), and the first wavelength to be combined is λ1 and the second wavelength is λ2, 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.

[0055] When calculating the intensity difference as the intensity characteristic, the intensity characteristic calculation unit 14 calculates the absolute value of the difference between the differential value of the spectral reflectance data A(λ1), A(λ2) of the paper 2A at the first wavelength λ1 and the second wavelength λ2 and the differential value of the spectral reflectance data B(λ1), B(λ2) of the leaf 2B at the first wavelength λ1 and the second wavelength λ2 (the difference between the relative spectral reflectance data of the paper 2A and the leaf 2B at the two first wavelengths λ1 and λ2). In this case, to cancel the effects of uneven lighting, shadows, etc., it is preferable to divide the differential value by the sum of the spectral reflectance data at the first wavelength λ1 and the second wavelength λ2.

[0056] Specifically, the intensity characteristic calculation unit 14 calculates the intensity difference using the following formula (1) based on the spectral reflectance data A(λ1), A(λ2) of the paper 2A at the first wavelength λ1 and the second wavelength λ2, and the spectral reflectance data B(λ1), B(λ2) of the leaf 2B.

[0057]

number

[0058] When calculating the intensity ratio as the intensity characteristic, the intensity characteristic calculation unit 14 calculates the intensity ratio by the following equation (2): ratio can be calculated.

[0059]

number

[0060] Furthermore, the intensity characteristic calculation unit 14 calculates the intensity difference and / or intensity ratio for each possible wavelength combination of the two first wavelengths λ1 and the second wavelengths λ2.

[0061] In the above example, the intensity difference and / or intensity ratio for evaluating the sensing performance of the two subjects, the paper 2A and the leaf 2B, is calculated, but the intensity characteristic calculation unit 14 similarly calculates the intensity difference and / or intensity ratio for evaluating the sensing performance of the two subjects, the leaf 2B and the insect 2C. Furthermore, when it is necessary to distinguish between the paper 2A and the insect 2C, the intensity difference and / or intensity ratio for evaluating the sensing performance of the two subjects, the paper 2A and the insect 2C, is also calculated.

[0062] The strength characteristics calculated by the strength characteristic calculation unit 14 are output to the data conversion unit 16.

[0063] The data conversion unit 16 performs a data conversion process to convert the calculated intensity characteristics into identification data of the specific subject for the wavelength range. The identification data is a map (first map) that represents changes in the intensity characteristics with wavelength as a variable.

[0064] The first map is a two-dimensional map, and the coordinate axes of the two-dimensional map are the first wavelength and the second wavelength.

[0065] 4A and 4B are diagrams showing examples of first maps showing the intensity distribution of the intensity characteristics calculated from the spectral data of paper and leaves. The first map shown in Fig. 4A is a heat map in which colors and densities are assigned according to the magnitude of the intensity characteristics, and the first map shown in Fig. 4B is a contour map according to the magnitude of the intensity characteristics.

[0066] In the first map (heat map) shown in Figure 4(A), the intensity characteristics of areas with high brightness (white) are large, and in the first map (contour map) shown in Figure 4(B), the intensity characteristics of areas with large contour lines are large.

[0067] 5A and 5B are diagrams showing examples of first maps showing the intensity distribution of the intensity characteristics calculated from the spectral data of the leaf and the insect. The first map shown in Fig. 5A is a heat map in which colors and densities are assigned according to the magnitude of the intensity characteristics, and the first map shown in Fig. 5B is a contour map according to the magnitude of the intensity characteristics.

[0068] The two first maps (the first maps shown in FIGS. 4 and 5), which are the identification data converted by the data conversion unit 16, are applied to the output unit 18.

[0069] The output unit 18 is an output processing unit that outputs the identification data (first map) input from the data conversion unit 16 to the outside. In this example, the destination of the external output is a display device 30.

[0070] As a result, in this example, the first map shown in FIGS. 4 and 5 is displayed on the display device 30.

[0071] The user instruction receiving unit 20 receives a specific position on the first map that is specified by the user using an operation unit 32 of a pointing device such as a mouse operated by the user.

[0072] As mentioned above, in this example, the insect 2C on the leaf 2B is the object to be detected, so on the first map shown in Figure 4, a position that does not distinguish between the paper 2A and the leaf 2B (small intensity characteristics) is preferred, while on the first map shown in Figure 5, a position that distinguishes between the leaf 2B and the insect 2C (large intensity characteristics) is preferred.

[0073] The user finds a specific position from the two first maps displayed on the display device 30 where the paper 2A and the leaf 2B are not distinguished from each other and the leaf 2B and the insect 2C are distinguished from each other, and indicates the specific position using the operation unit 32.

[0074] The user instruction receiving unit 20 receives the specific position on the first map specified by the user in this manner.

[0075] The wavelength combination determination unit 22 has wavelength information for each coordinate axis of the first map, and when a specific position accepted by the user instruction acceptance unit 20 is input, the wavelength combination determination unit 22 determines the wavelength combination of the first wavelength λ1 and the second wavelength λ2, which is the wavelength information for each coordinate axis indicated by the specific position.

[0076] In this example, the user designates a specific position indicated by a star marker M1 in the first map shown in Figures 4 and 5, and as a result, the wavelength combination identification unit 22 identifies the wavelength combination of a first wavelength λ1 (=750 nm) and a second wavelength λ2 (=950 nm).

[0077] The first wavelength λ1 (=750 nm) and the second wavelength λ2 (=950 nm) of the wavelength combination thus identified are wavelengths suitable for detecting the insect 2C that is the detection target.

[0078] The first wavelength λ1 (=750 nm) and the second wavelength λ2 (=950 nm) identified by the wavelength combination identifying unit 22 are output to and displayed on the display device 30, and can also be output to a recording device or other external device.

[0079] Once a wavelength combination of the first wavelength λ1 and the second wavelength λ2 suitable for detecting the detection target has been identified as described above, a multispectral camera (described below) having a first wavelength selection element (first bandpass filter) that transmits light in a wavelength band including the first wavelength λ1, and a second wavelength selection element (second bandpass filter) that transmits light in a wavelength band including the second wavelength λ2, is used to photograph multiple subjects including the detection target, and a first image in a wavelength band including the first wavelength λ1 and a second image in a wavelength band including the second wavelength λ2 are simultaneously obtained.

[0080] In addition, in the above example, the wavelength combination of the first wavelength λ1 and the second wavelength λ2 suitable for finding an insect 2C on a leaf 2B placed on a piece of paper 2A as shown in Figure 2 is the first wavelength λ1 (= 750 nm) and the second wavelength λ2 (= 950 nm).

[0081] FIG. 6 is a diagram showing an example of a second map indicating the difference or ratio between the first image and the second image acquired from the multispectral camera that captured the subject shown in FIG.

[0082] In the second map shown in FIG. 6, the contrast between the insect 2C and the background paper 2A and leaf 2B is clear, and the use of the second map allows the insect 2C to be detected with high accuracy.

[0083] <Other detection targets> FIG. 7 is a diagram showing a plurality of subjects including other detection targets photographed by a hyperspectral camera.

[0084] The multiple subjects shown in Figure 7 are two types of powdered soil 4B and 4C placed on a piece of paper 4A in the background. Soil 4B is soil of OK quality, and soil 4C is soil of NG quality.

[0085] The data acquisition unit 12 (FIG. 1) acquires spectral data of the two types of soil 4B and 4C from the hyperspectral camera 1.

[0086] FIG. 8 is a graph showing the spectral data of two types of soil, one in paper form and one in powder form.

[0087] In Fig. 8, the vertical axis of the graph indicates the spectral reflectance when the reflectance of the reference plate is set to 1. The spectral reflectance is the average or median value of the reflectance in the entire or partial region of the detection object.

[0088] In FIG. 8, D(λ) represents the spectral data of paper 4A, E(λ) represents the spectral data of soil 4B, and F(λ) represents the spectral data of soil 4C.

[0089] As shown in FIG. 8, the spectral data E(λ) and F(λ) of the two types of soil 4B and 4C are almost identical, making it difficult to distinguish between the two types of soil 4B and 4C with the naked eye.

[0090] The intensity characteristic calculation unit 14 calculates the intensity characteristics at the first wavelength λ1 and the second wavelength λ2 selected from the wavelength ranges of the spectral data E(λ) and F(λ) of the spectral data ...

[0091] 9A and 9B are diagrams showing examples of first maps showing the intensity distribution of strength characteristics calculated from the spectral data of two types of soil. The first map shown in Fig. 9A is a heat map in which colors and densities are assigned according to the magnitude of the strength characteristics, and the first map shown in Fig. 9B is a contour map according to the magnitude of the strength characteristics.

[0092] In the first map shown in FIG. 9, the user specifies a specific position where the strength characteristic is large. 9 , the specific position designated by the user is the position marked with a star marker M2.

[0093] The wavelength combination specifying unit 22 specifies the wavelength combination of the first wavelength λ1 (=515 nm) and the second wavelength λ2 (=730 nm) from the specified position indicated by the marker M2.

[0094] A multispectral camera having a first bandpass filter that transmits light in a wavelength band (e.g., 490 nm to 540 nm) including the identified first wavelength λ1 (= 515 nm) and a second bandpass filter that transmits light in a wavelength band (e.g., 700 nm to 760 nm) including the second wavelength λ2 (= 730 nm) is used to capture images of multiple subjects including the detection target shown in Figure 7, and simultaneously obtain a first image in the wavelength band including the first wavelength λ1 and a second image in the wavelength band including the second wavelength λ2.

[0095] The difference or ratio between the first image and the second image in each wavelength band acquired by the multispectral camera is calculated, and a second map indicating the calculated difference or ratio is created.

[0097] FIG. 10 is a diagram showing an example of a second map indicating the difference or ratio between the first image and the second image acquired by the multispectral camera that captured the subject shown in FIG.

[0098] As an example of the second map, the following is a method for expressing the difference between the first image and the second image.

[0099] If the first image is a wavelength image (λ1) and the second image is a wavelength image (λ2), the difference image is calculated by (wavelength image (λ1) - wavelength image (λ2)) ÷ (wavelength image (λ1) + wavelength image (λ2)), and this difference image is displayed as a heat map.

[0100] Furthermore, the R channel of a color image of R (red), G (green), and B (blue) is assigned to the wavelength image (λ1), and the G channel is assigned to the wavelength image (λ2), resulting in a pseudocolor image. Note that the assignment of the wavelength image (λ1) and the wavelength image (λ2) to the R channel, G channel, or B channel of the color image is not limited to the above example.

[0101] In this way, the heat map or pseudo-color image generated from the wavelength image (λ1) and the wavelength image (λ2) makes it easy to distinguish between the subject of the detection target and other subjects.

[0102] The second map shown in Figure 10 clearly shows the contrast between soil 4B, which has OK quality, and soil 4C, which has NG quality, and by using the second map, it is easy to distinguish between soil 4B, which has OK quality, and soil 4C, which has NG quality.

[0103] FIG. 11 is a diagram showing a plurality of subjects including other detection targets photographed by a hyperspectral camera.

[0104] The multiple subjects shown in FIG. 11 are peanut shells 6B and peanut kernels 6C placed on a background piece of paper 6A.

[0105] The data acquisition unit 12 (FIG. 1) acquires spectral data of the peanut skin 6B and the peanut kernel 6C from the hyperspectral camera 1.

[0106] FIG. 12 is a graph showing the spectral data of paper, peanut skins, and peanut kernels, respectively.

[0107] 12, the vertical axis of the graph indicates the spectral reflectance when the reflectance of the reference plate is set to 1. The spectral reflectance is the average or median value of the reflectance in the entire or partial region of the detection object.

[0108] In Figure 12, G(λ) represents the spectral data of paper 6A, H(λ) represents the spectral data of peanut skins 6B, and I(λ) represents the spectral data of peanuts 6C. As shown in Figure 12, peanut skins 6B and peanuts 6C are almost the same color, making them difficult to distinguish with the naked eye.

[0109] The intensity characteristic calculation unit 14 calculates the intensity characteristics at a first wavelength λ1 and a second wavelength λ2 selected from the wavelength ranges of the spectral data H(λ) and I(λ) of the peanut skin 6B and the peanut kernel 6C based on the relationship between the two wavelengths, the first wavelength λ1 and the second wavelength λ2, and the data conversion unit 16 converts the calculated intensity characteristics into a first map.

[0110] 13A and 13B are diagrams showing examples of first maps showing the intensity distribution of the intensity characteristics calculated from the spectral data of the skins and kernels of peanuts. The first map shown in Fig. 13A is a heat map in which colors and densities are assigned according to the magnitude of the intensity characteristics, and the first map shown in Fig. 13B is a contour map according to the magnitude of the intensity characteristics.

[0111] In order to enhance sensing performance, the user specifies multiple specific positions in the first map shown in Fig. 13. Note that in Fig. 13, the multiple (three) specific positions specified by the user are the positions marked with star markers M3, M4, and M5.

[0112] The wavelength combination determination unit 22 determines the wavelength combination of a first wavelength λ1 (= 570 nm) and a second wavelength λ2 (= 690 nm) from the specific position indicated by marker M3, determines the wavelength combination of a first wavelength λ1 (= 570 nm) and a second wavelength λ2 (= 930 nm) from the specific position indicated by marker M4, and determines the wavelength combination of a first wavelength λ1 (= 690 nm) and a second wavelength λ2 (= 930 nm) from the specific position indicated by marker M5.

[0113] In the above case, the wavelengths identified by the wavelength combination are three wavelengths (570 nm, 690 nm, 930 nm). Note that, in the case of a wavelength combination corresponding to three specific positions, six wavelengths (two wavelengths x three) are usually identified, but in this example, the wavelengths in the wavelength combination corresponding to the three specific positions include overlapping wavelengths, so three wavelengths (570 nm, 690 nm, 930 nm) are identified.

[0114] A multispectral camera having a first bandpass filter that transmits light in the wavelength band including the wavelength (570 nm) identified in this manner, a second bandpass filter that transmits light in the wavelength band including the wavelength (690 nm), and a third bandpass filter that transmits light in the wavelength band including the wavelength (930 nm) is used to photograph multiple subjects including the detection target shown in Figure 11, and a first image in the wavelength band including the wavelength (570 nm), a second image in the wavelength band including the wavelength (690 nm), and a third image in the wavelength band including the wavelength (930 nm) are simultaneously obtained.

[0115] 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 are calculated from the first image, the second image, and the third image of each wavelength band acquired by the multispectral camera, and three second maps showing these calculated differences or ratios are created.

[0116] FIG. 14 is a diagram showing an example of one second map obtained by combining three second maps created from the first image, the second image, and the third image acquired from a multispectral camera that captured the subject shown in FIG. 11.

[0117] FIG. 15 is a diagram showing how one second map is created by combining three second maps.

[0118] In this way, by combining multiple second maps and finally creating one second map, it is possible to create a second map with an expanded dynamic range and enhanced sensing performance.

[0119] [Second embodiment of data processing device] FIG. 16 is a functional block diagram showing a second embodiment of a data processing device according to the present invention.

[0120] In FIG. 16, parts common to the data processing device 10-1 of the first embodiment shown in FIG. 1 are given the same reference numerals, and detailed description thereof will be omitted.

[0121] The data processing device 10-2 of the second embodiment shown in Figure 16 differs from the data processing device 10-1 of the first embodiment in that it has a position extraction unit 24 instead of the user instruction receiving unit 20 of the data processing device 10-1 of the first embodiment.

[0122] The position extraction unit 24 receives a first map indicating the intensity distribution of the intensity characteristics from the data conversion unit 16, and performs a process of extracting one or more positions in the first map where the intensity characteristics exceed a threshold value.

[0123] For example, the position extraction unit 24 can extract one or more positions in the first map by detecting one or more areas in the first map where the intensity characteristic exceeds a threshold and determining the center of gravity of the detected areas. Alternatively, within one or more areas where the intensity characteristic exceeds the threshold, the position with the highest intensity characteristic may be determined as the position in that area.

[0124] The information on the positions extracted by the position extracting unit 24 (coordinate information on the first map) is output to the wavelength combination identifying unit 22.

[0125] The wavelength combination identification unit 22 has wavelength information for each coordinate axis of the first map, and when a position (specific position) extracted by the position extraction unit 24 is input, it identifies the wavelength combination of the first wavelength λ1 and the second wavelength λ2, which is the wavelength information for each coordinate axis indicated by the specific position.

[0126] The wavelength combination (first wavelength λ1, second wavelength λ2) identified by the wavelength combination identifying unit 22 is output and displayed on the display device 30, and can also be output to a recording device or other external device. When two or more wavelength combinations are identified, each identified wavelength combination is output.

[0127] Furthermore, as a modified example of the data processing device 10-1 of the first embodiment and the data processing device 10-2 of the second embodiment, the data processing device 10-2 of the second embodiment may identify multiple candidates for specific positions (multiple wavelength combinations), superimpose the candidate specific positions on the first map, and specify one or more specific positions from the candidate specific positions superimposed on the first map in response to a user instruction.

[0128] [Multispectral camera] FIG. 17 is a schematic diagram illustrating an example of a multispectral camera.

[0129] The multispectral camera (imaging device) 100 shown in FIG. 17 is made up of an imaging optical system 110 including lenses 110A and 110B and a filter unit 120, an image sensor 130, and a signal processing unit 140.

[0130] The filter unit 120 is composed of a polarizing filter unit 122 and a bandpass filter unit 124, and is preferably disposed at or near the pupil position of the photographing optical system 110.

[0131] The polarizing filter unit 122 consists of a first polarizing filter 122A and a second polarizing filter 122B that linearly polarize the light passing through the first pupil region and the second pupil region of the photographing optical system 110, respectively, and the polarization directions of the first polarizing filter 122A and the second polarizing filter 122B differ from each other by 90°.

[0132] 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 that respectively select the wavelength bands of light that pass through the first pupil region and the second pupil region of the imaging optical system 110, and the first bandpass filter 124A selects a wavelength band that includes one wavelength (first wavelength) of the identified wavelength combination, and the second bandpass filter 124B selects a wavelength band that includes the other wavelength (second wavelength) of the identified wavelength combination.

[0133] Therefore, light passing through the first pupil region of the imaging optical system 110 is linearly polarized by the first polarizing filter 122A, and only light in a wavelength band including the first wavelength is transmitted by the first band-pass filter 124A. On the other hand, light passing through the second pupil region of the imaging optical system 110 is linearly polarized by the second polarizing filter 122B (linearly polarized in a direction 90° different from that of the first polarizing filter 122A), and only light in a wavelength band including the second wavelength is transmitted by the second band-pass filter 124B.

[0134] Image sensor 130 is configured such that a first polarizing filter and a second polarizing filter, each having a polarization direction that differs by 90 degrees, are regularly arranged on a plurality of pixels each made up of a two-dimensionally arranged photoelectric conversion element.

[0135] The first polarizing filter 122A and the first polarizing filter of the image sensor 130 have the same polarization direction, and the second polarizing filter 122B and the second polarizing filter of the image sensor 130 have the same polarization direction.

[0136] The signal processing unit 140 obtains a first image in the wavelength band wavelength-selected by the first band-pass filter 124A by reading out pixel signals from pixels in which the first polarizing filter of the image sensor 130 is arranged, and obtains a second image in the wavelength band wavelength-selected by the second band-pass filter 124B by reading out pixel signals from pixels in which the second polarizing filter of the image sensor 130 is arranged.

[0137] The first and second images acquired by the signal processing unit 140 are used to detect the detection target, as described above.

[0138] [Optical elements] The optical element according to the present invention is an optical element manufactured according to a wavelength combination of a first wavelength and a second wavelength specified by the data processing device 10-1 of the first embodiment shown in FIG. 1 or the data processing device 10-2 of the second embodiment shown in FIG. 16.

[0139] That is, the optical element corresponds to the bandpass filter unit 124 arranged in the multispectral camera 100 shown in FIG. 17, and includes a first wavelength selection element (first bandpass filter) that transmits light in a wavelength band including a first wavelength specified by the data processing device, and a second wavelength selection element (second bandpass filter) that transmits light in a wavelength band including a second wavelength specified by the data processing device. 2 and a wavelength selection element (second bandpass filter).

[0140] The first bandpass filter and the second bandpass filter preferably have a bandwidth such that the wavelength bands of the transmission wavelengths of the first and second bandpass filters do not overlap each other, with the first wavelength and the second wavelength being the center wavelength, respectively.

[0141] [Photographing optical system] The imaging optical system according to the present invention corresponds to the imaging optical system 110 of the multispectral camera 100 shown in Fig. 17. This imaging optical system is an optical element corresponding to the bandpass filter unit 124, and includes a first wavelength selection element (first bandpass filter) that transmits light in a wavelength band including a first wavelength specified by the data processing device, and a second wavelength selection element (second bandpass filter) that transmits light in a wavelength band including a second wavelength specified by the data processing device. 2 An optical element having a wavelength selection element (second bandpass filter) is arranged at or near the pupil position of the lenses 110A and 110B.

[0142] [Photographing equipment] The imaging device according to the present invention corresponds to, for example, the multispectral camera 100 shown in FIG.

[0143] The multispectral camera 100 shown in FIG. 17 includes an imaging optical system 110 (an imaging optical system in which an optical element according to the present invention is arranged at or near the pupil position) and an image sensor (image capturing element) 130 that captures optical images (first optical image and second optical image) formed by the imaging optical system 110.

[0144] The first optical image is an optical image that has passed through a first wavelength selection element of the optical element, and the second optical image is an optical image that has passed through a second wavelength selection element of the optical element.

[0145] The first optical image and the second optical image are pupil-divided by polarizing filter unit 122 (first polarizing filter 122A and second polarizing filter 122B) functioning as a pupil division section, and by the first polarizing filter and the second polarizing filter corresponding to first polarizing filter 122A and second polarizing filter 122B on each pixel of image sensor 130, and then captured by image sensor 130. This allows multispectral camera 100 to simultaneously acquire a first image corresponding to the first optical image and a second image corresponding to the second optical image, each having a different wavelength band.

[0146] The imaging device is not limited to one having the configuration of the pupil division unit of the multispectral camera 100 shown in FIG. 17, but may be one that can capture at least a first optical image transmitted through a first wavelength selection element and a second optical image transmitted through a second wavelength selection element, and obtain a first image and a second image corresponding to the first optical image and the second optical image.

[0147] [Data processing method] The data processing method according to the present invention is a method for identifying a wavelength combination of a first wavelength and a second wavelength suitable for detecting a desired detection target, and is a method executed by a processor that is the main processor for processing each part of the data processing devices 10-1 and 10-2 shown in FIGS. 1 and 16.

[0148] First Embodiment FIG. 18 is a flowchart showing a first embodiment of the data processing method according to the present invention.

[0149] 18, the processor acquires spectral data of a plurality of objects (step S10, data acquisition step). In step S10, for example, the processor acquires the spectral data of a plurality of objects from a hyperspectral camera that captures images of the plurality of objects with different spectral reflectances.

[0150] Now, as shown in Figure 2, if there are three objects, namely, paper 2A, leaf 2B, and insect 2C, and leaf 2B is placed on paper 2A, and insect 2C is on leaf 2B, three pieces of spectral data A(λ), B(λ), and C(λ) of paper 2A, leaf 2B, and insect 2C are obtained (see Figure 3).

[0151] Next, the processor calculates the intensity characteristics at the first wavelength λ1 and the second wavelength λ2 selected from the wavelength ranges of the spectral data of the multiple subjects acquired in step S10 based on the relationship between the two wavelengths, the first wavelength λ1 and the second wavelength λ2 (step S12, calculation step).

[0152] The intensity characteristics at the first wavelength and the second wavelength are the intensity difference and / or intensity ratio of the light beams at the first wavelength and the second wavelength.

[0153] In the spectral data A(λ) of paper 2A and the spectral data B(λ) of leaf 2B, if the first wavelength to be combined is λ1 and the second wavelength is λ2, 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.

[0154] When calculating an intensity difference as an intensity characteristic, the absolute value of the difference between the difference value of the spectral reflectance data A(λ1), A(λ2) of the paper 2A at the first wavelength λ1 and the second wavelength λ2 and the difference value of the spectral reflectance data B(λ1), B(λ2) of the leaf 2B at the first wavelength λ1 and the second wavelength λ2 is calculated, and when calculating an intensity ratio as an intensity characteristic, the absolute value of the ratio between the difference value of the spectral reflectance data A(λ1), A(λ2) of the paper 2A at the first wavelength λ1 and the second wavelength λ2 and the difference value of the spectral reflectance data B(λ1), B(λ2) of the leaf 2B at the first wavelength λ1 and the second wavelength λ2 is calculated.

[0155] Specifically, it is preferable to calculate the intensity difference or intensity ratio using the above-mentioned [Equation 1] or [Equation 2].

[0156] Next, the processor converts the intensity characteristics calculated in step S12 into identification data of the specific subject for the wavelength range (step S14, data conversion step). Here, the identification data is a map (first map) that represents changes in intensity characteristics with wavelength as a variable. The first map is a two-dimensional map, and the coordinate axes of the two-dimensional map are the first wavelength and the second wavelength.

[0157] 4A and 4B are diagrams showing examples of first maps showing the intensity distribution of the intensity characteristics calculated from the spectral data of paper and leaves. The first map shown in Fig. 4A is a heat map in which colors and densities are assigned according to the magnitude of the intensity characteristics, and the first map shown in Fig. 4B is a contour map according to the magnitude of the intensity characteristics.

[0158] 5A and 5B are diagrams showing examples of first maps showing the intensity distribution of the intensity characteristics calculated from the spectral data of the leaf and the insect. The first map shown in Fig. 5A is a heat map in which colors and densities are assigned according to the magnitude of the intensity characteristics, and the first map shown in Fig. 5B is a contour map according to the magnitude of the intensity characteristics.

[0159] Next, the processor outputs the first map, which is the identification data converted in step S14, to the display device 30 (FIG. 1), which is the destination of external output (step S16, output step). As a result, in this example, the first map shown in FIGS. 4 and 5 is displayed on the display device 30.

[0160] The processor determines whether a specific position on the first map has been designated by the user (step S18). The user can designate a specific position (e.g., a position with a large intensity characteristic) using a pointing device such as a mouse while viewing the first map displayed on the display device 30.

[0161] In this example, since the insect 2C on the leaf 2B is the object to be detected, the specific position indicated by the user on the first map shown in Figure 4 is preferably a position that does not distinguish between the paper 2A and the leaf 2B (low intensity characteristics), while on the first map shown in Figure 5, a position that distinguishes between the leaf 2B and the insect 2C (high intensity characteristics) is preferred.

[0162] It is preferable that the user finds a specific location from the two first maps displayed on the display device 30 that does not distinguish between the paper 2A and the leaf 2B, but does distinguish between the leaf 2B and the insect 2C, and indicates that specific location.

[0163] When a specific position on the first map is designated by the user (if "Yes"), the processor identifies a wavelength combination of the first wavelength and the second wavelength to be used to detect the detection target among the multiple subjects based on the specific position (step S20. Identification step). In this example, the processor identifies a wavelength combination of the first wavelength λ1 (=750 nm) and the second wavelength λ2 (=950 nm) that represents the coordinates of the specific position from the specific position indicated by the star marker M1 on the first map shown in Figures 4 and 5.

[0164] The wavelength combination (750 nm, 950 nm) thus identified is suitable for detecting the insect 2C, which is the detection target, and information indicating the wavelength combination is output and displayed on the display device 30, and can also be output to a recording device or other external device (step S22).

[0165] Second Embodiment FIG. 19 is a flowchart showing a second embodiment of the data processing method according to the present invention.

[0166] In FIG. 19, steps common to those in the data processing method of the first embodiment shown in FIG. 18 are given the same step numbers, and detailed descriptions thereof will be omitted.

[0167] The data processing method of the second embodiment shown in FIG. 19 differs from the data processing method of the first embodiment shown in FIG. 18 in that the processing of step S30 is performed instead of the processing of step S18 shown in FIG.

[0168] In step S30 shown in FIG. 19, a process is performed to extract one or more positions in the first map showing the intensity distribution of the intensity characteristic where the intensity characteristic exceeds a threshold value.

[0169] For example, in step S30, one or more regions in the first map whose intensity characteristics exceed a threshold value are detected, and the centroid positions of the detected regions are calculated, thereby extracting one or more positions in the first map. Alternatively, the position with the highest intensity characteristic within one or more regions whose intensity characteristics exceed a threshold value may be determined as the position in that region.

[0170] The position automatically extracted in this way is set as the specific position on the first map. That is, the automatically extracted specific position can be used in place of the specific position designated by the user in the first embodiment.

[0171] As a modification of the data processing method of the first and second embodiments, , complexIt is also possible to automatically identify a number of candidate specific positions (multiple wavelength combinations), superimpose the candidate specific positions on the first map, and identify one or more specific positions from the candidate specific positions superimposed on the first map in response to a user instruction.

[0172] <Third embodiment> FIG. 20 is a flowchart showing a third embodiment of the data processing method according to the present invention.

[0173] The third embodiment shown in Fig. 20 shows a case where a detection target is detected using a wavelength combination of a first wavelength λ1 and a second wavelength λ2 identified by the data processing methods of the first and second embodiments shown in Fig. 18 and Fig. 19. Furthermore, when an insect 2C on a leaf 2B placed on a piece of paper 2A is the detection target as shown in Fig. 2, the wavelength combination suitable for detecting the insect 2C is the first wavelength λ1 (= 750 nm) and the second wavelength λ2 (= 950 nm).

[0174] In FIG. 20, when a wavelength combination of a first wavelength λ1 and a second wavelength λ2 (e.g., 750 nm, 950 nm) suitable for detecting an object to be detected is identified by the data processing methods of the first and second embodiments shown in FIGS. 18 and 19, multiple subjects including the object to be detected are simultaneously photographed using a multispectral camera 100 (see FIG. 17) having a first wavelength selection element (first band-pass filter) that transmits light in a wavelength band including the first wavelength λ1 and a second wavelength selection element (second band-pass filter) that transmits light in a wavelength band including the second wavelength λ2 (step S40).

[0175] The processor acquires a first image in a wavelength band including the first wavelength and a second image in a wavelength band including the second wavelength from the multispectral camera 100 (step S42, image acquisition step).

[0176] Next, the processor calculates the difference or ratio between the acquired first image and second image (step S44), and creates a map (second map) indicating the calculated difference or ratio (step S46, second map creation step).

[0177] FIG. 6 is a diagram showing an example of a second map indicating the difference or ratio between the first image and the second image acquired from the multispectral camera that captured the subject shown in FIG.

[0178] Next, the processor detects the detection target based on the created second map (step S48). In the second map shown in Fig. 6, the contrast between the insects 2C and the background paper 2A and leaves 2B is clear, so by using the second map, the positions of the insects 2C on the second map, the number of insects 2C, etc. can be detected with high accuracy.

[0179] [others] The multiple subjects including the detection target are not limited to those in this embodiment, and various other combinations are possible. The number of wavelength combinations to be identified may be two or more, in which case a multispectral camera having three or more wavelength selection elements is applied.

[0180] In this embodiment, for example, the hardware structure of a processing unit that executes various processes of a processor constituting a data processing device includes various processors as shown below. Each type of processor has software (programs) This includes CPUs (Central Processing Units), which are general-purpose processors that execute various processes and function as various processing units, programmable logic devices (PLDs), which are processors whose circuit configuration can be changed after manufacture, such as FPGAs (Field Programmable Gate Arrays), and dedicated electrical circuits, such as ASICs (Application Specific Integrated Circuits), which are processors with a circuit configuration designed specifically to execute specific processes.

[0181] A single processing unit may be configured with one of these various processors, or may be configured with two or more processors of the same or different types (for example, multiple FPGAs, or a combination of a CPU and an FPGA). Also, multiple processing units may be configured with a single processor. Examples of multiple processing units configured with a single processor include, first, a configuration in which one processor is configured with a combination of one or more CPUs and software, as typified by client or server computers, and this processor functions as multiple processing units. Second, a configuration in which a processor is used to realize the functions of an entire system including multiple processing units on a single IC (Integrated Circuit) chip, as typified by a System on Chip (SoC). In this way, the various processing units are configured with one or more of the above-mentioned various processors as a hardware structure.

[0182] Furthermore, the hardware structure of these various processors is, more specifically, an electric circuit made up of a combination of circuit elements such as semiconductor elements.

[0183] The present invention also includes a data processing program that, when installed in a computer, causes the computer to function as a data processing device according to the present invention, and a non-volatile storage medium on which this data processing program is recorded.

[0184] Furthermore, the present invention is not limited to the above-described embodiment, and it goes without saying that various modifications are possible without departing from the spirit of the present invention. [Explanation of symbols]

[0185] 1. Hyperspectral camera 2A paper 2B Leaf 2C Insect 4A, 6A paper 4B, 4C soil 6B Peanut skins 6C Peanuts 10-1, 10-2 Data processing device 12 Data Acquisition Section 14 Strength characteristic calculation section 16 Data conversion section 18 Output section 20 User instruction reception unit 22 Wavelength combination identification unit 24 Position extraction part 30 Display device 32 Operation section 100 Multispectral Camera 110 Photographing optical system 110A Lens 110B lens 120 Filter Unit 122 Polarizing filter unit 122A First Polarizing Filter 122B Second polarizing filter 124 Bandpass filter unit 124A 1st bandpass filter 124B Second bandpass filter 130 Image Sensor 140 Signal Processing Unit M1, M2, M3, M4, M5 markers Steps S10-S22, S30, S40-S46

Claims

1. In a data processing device having a processor, The processor: a data acquisition process for acquiring spectral data of a plurality of subjects; a calculation process of calculating intensity characteristics at a first wavelength and a second wavelength selected from the wavelength ranges of the acquired spectral data of the plurality of subjects based on a relationship between the first wavelength and the second wavelength; a data conversion process for converting the intensity characteristics calculated in the calculation process into identification data of a specific subject for the wavelength range; an output process for outputting the identification data to an external device; Equipped with the identification data is a first map representing a change in the intensity characteristic with wavelength as a variable; the first map is a two-dimensional map, and the coordinate axes of the two-dimensional map are the first wavelength and the second wavelength; Data processing device.

2. The strength characteristic is a strength difference and / or a strength ratio, The intensity difference and / or intensity ratio can be expressed as follows: where A(λ) is the spectral data of a first object and B(λ) is the spectral data of a second object among the spectral data of the plurality of objects, λ1 is the selected first wavelength, and λ2 is the selected second wavelength. Intensity difference = | [A (λ1) - A (λ2)] - [B (λ1) - B (λ2)] | Intensity ratio = | [A (λ1) - A (λ2)] / [B (λ1) - B (λ2)] | Calculated by 2. The data processing device according to claim 1.

3. The strength characteristic is a strength difference and / or a strength ratio, The intensity difference and / or intensity ratio is calculated by the following [Equation 1] and / or [Equation 2]: [Equation 1] [Equation 2] Calculated by 2. The data processing device according to claim 1.

4. The identification data is output to a display device, The processor performs a process of receiving a specific position on the first map displayed on the display device by a user instruction, and a process of specifying a wavelength combination of the first wavelength and the second wavelength to be used for detecting a detection target object among the plurality of subjects based on the specific position.

2. The data processing device according to claim 1.

5. extracting from the first map one or more locations where the intensity characteristic exceeds a threshold; and using the extracted position to detect a detection target among the plurality of subjects. and performing a process of identifying one or more wavelength combinations of the first wavelength and the second wavelength.

2. The data processing device according to claim 1.

6. The identification data is output to a display device, The processor performs a process of superimposing and displaying candidates for specific positions for the wavelength combination of the one or more first wavelengths and the second wavelengths on the first map displayed on the display device, a process of accepting a specific position from the candidates for specific positions in response to a user instruction, and a process of identifying the wavelength combination of the first wavelength and the second wavelength based on the accepted specific position.

6. A data processing device according to claim 5.

7. the plurality of subjects include a first subject, a second subject, and a third subject; the data acquisition process includes acquiring spectral data of the first object, spectral data of the second object, and spectral data of the third object; the calculation process calculates two or more of the intensity characteristics of the spectral data of the first object and the spectral data of the second object at a first wavelength and a second wavelength, the intensity characteristics of the spectral data of the second object and the spectral data of the third object at a first wavelength and a second wavelength, and the intensity characteristics of the spectral data of the first object and the spectral data of the third object at a first wavelength and a second wavelength, the data conversion process converts the two or more intensity characteristics into two or more pieces of identification data.

7. A data processing device according to claim 1.

8. a data acquisition step of acquiring spectral data of a plurality of subjects; a calculation step of calculating intensity characteristics at a first wavelength and a second wavelength selected from the wavelength ranges of the acquired spectral data of the plurality of subjects based on a relationship between the first wavelength and the second wavelength; a data conversion step of converting the intensity characteristics calculated in the calculation step into identification data of a specific subject for the wavelength range; an output step of outputting the identification data to an external device, The processor executes the processing for each step, the identification data is a first map representing a change in the intensity characteristic with wavelength as a variable; the first map is a two-dimensional map, and the coordinate axes of the two-dimensional map are the first wavelength and the second wavelength; Data processing methods.

9. The strength characteristic is an intensity difference and / or an intensity ratio, The intensity difference and / or intensity ratio can be expressed as follows: where A(λ) is the spectral data of a first object and B(λ) is the spectral data of a second object among the spectral data of the plurality of objects, λ1 is the selected first wavelength, and λ2 is the selected second wavelength. Intensity difference = | [A (λ1) - A (λ2)] - [B (λ1) - B (λ2)] | Intensity ratio = | [A (λ1) - A (λ2)] / [B (λ1) - B (λ2)] | Calculated by 9. The data processing method according to claim 8.

10. The strength characteristic is an intensity difference and / or an intensity ratio, The intensity difference and / or intensity ratio is calculated by the following [Equation 1] and / or [Equation 2]: [Equation 1] [Equation 2] Calculated by 9. The data processing method according to claim 8.

11. The identification data is output to a display device, receiving a specific position on the first map displayed on the display device through a user instruction; and specifying a wavelength combination of the first wavelength and the second wavelength to be used for detecting a detection target among the plurality of subjects based on the specified position.

9. The data processing method according to claim 8.

12. extracting from the first map one or more locations in the first map where the intensity characteristic exceeds a threshold; and identifying one or more wavelength combinations of the first wavelength and the second wavelength to be used for detecting a detection target among the plurality of subjects based on the extracted positions.

9. The data processing method according to claim 8.

13. The identification data is output to a display device, superimposing and displaying candidates for the specific position for the one or more wavelength combinations of the first wavelength and the second wavelength on the first map displayed on the display device; accepting a specific position from the specific position candidates in response to a user instruction; and identifying a wavelength combination of the first wavelength and the second wavelength based on the received specific position.

13. The data processing method according to claim 12.

14. the plurality of subjects include a first subject, a second subject, and a third subject; the data acquisition step acquires spectral data of the first object, spectral data of the second object, and spectral data of the third object; the calculating step calculates two or more intensity characteristics from among the intensity characteristics at a first wavelength and a second wavelength of the spectral data of the first object and the spectral data of the second object, the intensity characteristics at a first wavelength and a second wavelength of the spectral data of the second object and the spectral data of the third object, and the intensity characteristics at a first wavelength and a second wavelength of the spectral data of the first object and the spectral data of the third object; the data conversion step converts the two or more intensity characteristics into two or more pieces of identification data; 14. A data processing method according to any one of claims 8 to 13.

15. an image acquisition step of acquiring a first image in a wavelength band including the first wavelength and a second image in a wavelength band including the second wavelength in the specified wavelength combination; calculating a difference or ratio between the acquired first image and the acquired second image; a second map creation step of creating a second map indicating the calculated difference or ratio; 14. A data processing method according to any one of claims 11 to 13, comprising:

16. the identifying step identifies two or more wavelength combinations of the first wavelength and the second wavelength to be used for detecting a detection target among the plurality of subjects based on the first map; the image acquisition step acquires a first image in a wavelength band including the first wavelength and a second image in a wavelength band including the second wavelength for each of the two or more wavelength combinations, the second map creation step creates the second map for each of the two or more wavelength combinations, creating one second map by combining the two or more created second maps; 16. The data processing method of claim 15, comprising:

17. detecting the detection target based on the created second map, 17. The data processing method according to claim 16.

18. The ability to acquire spectral data for multiple subjects, a function of calculating intensity characteristics at a first wavelength and a second wavelength selected from the wavelength ranges of the acquired spectral data of the plurality of subjects based on a relationship between the first wavelength and the second wavelength; a function of converting the calculated intensity characteristics into identification data of a specific subject for the wavelength range; a function of outputting the identification data to an external device; A data processing program that causes a computer to execute the above. the identification data is a first map representing a change in the intensity characteristic with wavelength as a variable; the first map is a two-dimensional map, and the coordinate axes of the two-dimensional map are the first wavelength and the second wavelength; Data processing program.

19. The strength characteristic is an intensity difference and / or an intensity ratio, The intensity difference and / or intensity ratio can be expressed as follows: where A(λ) is the spectral data of a first object and B(λ) is the spectral data of a second object among the spectral data of the plurality of objects, λ1 is the selected first wavelength, and λ2 is the selected second wavelength. Intensity difference = | [A (λ1) - A (λ2)] - [B (λ1) - B (λ2)] | Intensity ratio = | [A (λ1) - A (λ2)] / [B (λ1) - B (λ2)] | Calculated by 19. The data processing program according to claim 18.

20. A non-transitory computer-readable recording medium on which the program according to claim 18 or 19 is recorded.

Images