Abstract: An information processing apparatus includes: a memory configured to store an image processing program having a tree structure in which a partial program is incorporated in each of a plurality of nodes; and a processor configured to performs, based on the image processing program, operations of; calculating a feature amount based on a processing result in each intermediate node excluding a terminal node among the plurality of nodes when executing image processing on a captured image which is captured by an imaging device; and calculating a performance evaluation value of the image processing program based on a variation amount of the feature amount in accordance with elapse of time.

Abstract: An information processing apparatus includes a memory that stores information indicating a first individual and a processing result that is output by each node of the first individual in a process of executing image processing based on the first individual; and a processor coupled to the memory and configured to: generate a second individual based on the first individual; specify subtrees that have the same content and include terminating nodes between a tree structure indicating the second individual and a tree structure indicating the first individual; and set a processing result corresponding to a head node of the subtree included in the first individual, which is stored in the memory, as a result of executing image processing based on the subtree included in the second individual when adaptability of the second individual is calculated based on a result of executing image processing based on the second individual.

Abstract: An information processing apparatus includes a memory; and a processor configured to determine a plurality of initial image processing programs and to add the initial image processing programs to an image processing program group, extract at least two image processing programs from the image processing program group, generate a candidate of a next-generation image processing program from the extracted image processing programs, based on genetic programming, calculate a fitness of the candidate of the next-generation image processing program, using a learning data item including an input image and a target processing result, and determine the next-generation image processing program or update the image processing program group based on the calculated fitness, wherein the processor is configured to determine, as some of the initial image processing programs, at least some of first image processing programs included in the image processing program group upon previous determination of a next-generation image proc

Abstract: An information processing apparatus includes: a memory; and a processor configured to: execute genetic processing on class classification programs of a set of class classification programs; acquire a distance between each learning data and an identification boundary regarding evaluation identifiers created by using first class learning data belonging to a first class and second class learning data belonging to a second class according to the class classification program; calculate a statistic amount of a distribution of a distance to the identification boundary for each of the first class learning data and the second class learning data; define a fitness calculation equation based on the statistic amounts of the first class learning data and the second class learning data; calculate the fitness of the class classification program; and determine whether or not to replace the class classification program with one of class classification programs of the set depending on the fitness.

Abstract: An image inspection learning method implemented by a computer, the method includes: generating non-defective region data obtained by extracting, from a learning image including a defective region, a non-defective region other than the defective region; inputting the learning image into an image processing program for image inspection that detects the defective region in an input image as a detection target for the defective region, and acquiring an output image; extracting a feature quantity for a predetermined region of the output image; classifying, based on the non-defective region data, the feature quantity for the predetermined region into a non-defective feature quantity corresponding to the non-defective region and a defective feature quantity corresponding to the defective region; and using the non-defective feature quantity to learn a discriminator that discriminates a region of the output image outputted from the image processing program.

Abstract: An information processing apparatus is configured to execute generation processing for generating candidates of second image processing program based on an image processing program, the second image processing program being a next generation image processing program in genetic programming, (b): execute first evaluation processing for evaluating fitness of the candidates of the second image processing program by using low-resolution learning data including an input image and a target processing result, the low-resolution learning data being obtained by reducing the resolution of at least the input image, (c): execute second evaluation processing for narrowing down the candidates of the second image processing program based on an evaluation result in the first evaluation processing, and evaluating the fitness of the narrowed-down candidates by using the learning data, and (d): execute determination processing for determining the second image processing program based on an evaluation result in the second evaluat

Abstract: In a program generation apparatus, an operating unit repeatedly performs setting process of calculating the increasing rate of the maximum fitness level among the fitness levels of individuals included in a population, setting a setting value indicating the number of child individuals to be generated to a first value if the increasing rate is higher than or equal to a first threshold and setting the setting value to a second value greater than the first value if the increasing rate is lower than the first threshold, an evolution process of generating as many child individuals as specified by the setting value, based on a parent individual selected from the individuals included in the population, and a survival selection process of selecting an individual with the highest fitness level from the parent and child individuals and replacing one of the individuals in the population with the selected individual.

Abstract: There is provided a program generating apparatus including a generating unit and a genetic processing unit. The generating unit is configured to generate tree structures each representing an image classification program. Each of the tree structures has a first level group and a second level group. Elements of nodes in the first level group are selected from amongst image filters each used to apply preprocessing to an input image. An element of a node in the second level group is selected from amongst setting programs each used to set a different value as a control parameter for generating a classifier based on information obtained by execution of the elements selected for the nodes in the first level group. The genetic processing unit is configured to output, using genetic programming, a tree structure with a fitness score exceeding a predetermined threshold based on the tree structures.

Abstract: An information processing device includes: a memory; and a processor coupled to the memory and configured to: accept specification of a feature region that includes a characteristic portion in a learning image; create first data that indicates a degree of overlap between the feature region and each of divided regions obtained by dividing the learning image; perform image conversion on the learning image by an image classification program in which element filters are combined; calculate an image feature value of each of the divided regions from an image obtained; create second data that indicates the image feature value corresponding to each of the divided regions; calculate a similarity between the first data and the second data; and use a result of evaluating the image classification program on the basis of the similarity, for execution of a genetic programming.

Abstract: An apparatus includes a processor that matches of local-feature-amounts in a state where an image of a measurement target captured by an image-sensor and a projective-transformed image of three-dimensional design data of the measurement target substantially overlap each other on a display to search the captured image and a virtual image generated from the projective-transformed image for a plurality of feature-point pairs with similar local-feature-amounts of an image, estimates a temporary-external-parameter related to a position and orientation of the image-sensor, compares an initial-external-parameter and the temporary-external-parameter to diagnose reliability of the temporary-external-parameter, and selects, among the feature-point pairs, a specified number of feature-point pairs with a score value indicating similarity between two feature-points forming each feature-point pair equal to or higher than a threshold value, estimate a final-external-parameter using the selected feature-point pairs, and disp

Abstract: An information processing device includes: a memory; and a processor coupled to the memory and configured to: automatically generate an image classification program that classifies a first learning image group classified into two classes in advance into the two classes on a basis of the genetic programming; and generate a multi-class image classifier that gives a second learning image group classified into three or more classes in advance to the image classification program automatically and classifies the second learning image group into the three or more classes.

Abstract: An information processing device includes: a memory; and a processor coupled to the memory and configured to: calculate an image feature amount from a plurality of original images for learning; classify the plurality of original images into each class by performing clustering on the plurality of original images by using the image feature amount; calculate an evaluation value of an image processing program with respect to the plurality of original images for each class; generate an image processing program by genetic programming on a basis of the evaluation value calculated for each class; and store an image processing program of which the evaluation value satisfies a predetermined condition in association with each class.

Abstract: An information processing apparatus includes: a memory; and a processor configured to: execute genetic processing on class classification programs of a set of class classification programs; acquire a distance between each learning data and an identification boundary regarding evaluation identifiers created by using first class learning data belonging to a first class and second class learning data belonging to a second class according to the class classification program; calculate a statistic amount of a distribution of a distance to the identification boundary for each of the first class learning data and the second class learning data; define a fitness calculation equation based on the statistic amounts of the first class learning data and the second class learning data; calculate the fitness of the class classification program; and determine whether or not to replace the class classification program with one of class classification programs of the set depending on the fitness.

Abstract: An image inspection learning method implemented by a computer, the method includes: generating non-defective region data obtained by extracting, from a learning image including a defective region, a non-defective region other than the defective region; inputting the learning image into an image processing program for image inspection that detects the defective region in an input image as a detection target for the defective region, and acquiring an output image; extracting a feature quantity for a predetermined region of the output image; classifying, based on the non-defective region data, the feature quantity for the predetermined region into a non-defective feature quantity corresponding to the non-defective region and a defective feature quantity corresponding to the defective region; and using the non-defective feature quantity to learn a discriminator that discriminates a region of the output image outputted from the image processing program.

Abstract: An information processing apparatus includes a memory; and a processor configured to determine a plurality of initial image processing programs and to add the initial image processing programs to an image processing program group, extract at least two image processing programs from the image processing program group, generate a candidate of a next-generation image processing program from the extracted image processing programs, based on genetic programming, calculate a fitness of the candidate of the next-generation image processing program, using a learning data item including an input image and a target processing result, and determine the next-generation image processing program or update the image processing program group based on the calculated fitness, wherein the processor is configured to determine, as some of the initial image processing programs, at least some of first image processing programs included in the image processing program group upon previous determination of a next-generation image proc

Abstract: An abnormal detection apparatus including an imaging unit configured to image generate a first and second image frames included in a first image frame group; a pseudo work generation unit configured to generate the first and a third image frames, the third and the second image frames, or the third and a fourth image frames, included in a second image frame group, respectively, with respect to the first and second image frames included in the first image frame group; a normal space generation unit configured to generate a normal space data based on the first and second image frames included in the first image frame group, and the first and third image frames, the third and second image frames, or the third and fourth image frames, included in the second image frame group; and a comparison unit configured to detect abnormality based on the normal space data.

Abstract: An information processing device includes: a memory; and a processor coupled to the memory and configured to: accept specification of a feature region that includes a characteristic portion in a learning image; create first data that indicates a degree of overlap between the feature region and each of divided regions obtained by dividing the learning image; perform image conversion on the learning image by an image classification program in which element filters are combined; calculate an image feature value of each of the divided regions from an image obtained; create second data that indicates the image feature value corresponding to each of the divided regions; calculate a similarity between the first data and the second data; and use a result of evaluating the image classification program on the basis of the similarity, for execution of a genetic programming.

Abstract: An apparatus includes a processor that matches of local-feature-amounts in a state where an image of a measurement target captured by an image-sensor and a projective-transformed image of three-dimensional design data of the measurement target substantially overlap each other on a display to search the captured image and a virtual image generated from the projective-transformed image for a plurality of feature-point pairs with similar local-feature-amounts of an image, estimates a temporary-external-parameter related to a position and orientation of the image-sensor, compares an initial-external-parameter and the temporary-external-parameter to diagnose reliability of the temporary-external-parameter, and selects, among the feature-point pairs, a specified number of feature-point pairs with a score value indicating similarity between two feature-points forming each feature-point pair equal to or higher than a threshold value, estimate a final-external-parameter using the selected feature-point pairs, and disp

Abstract: Each time evolution processing is performed, an information recording unit records increase-decrease information indicating a type of partial program changed in number between pre- and post-evolution individuals and difference between fitness levels calculated from the pre- and post-evolution individuals in a storage unit. A probability setting unit determines, per partial program, a contribution level indicating how much a partial program contributes to increasing a fitness level, based on the information stored in the storage unit within a predetermined period and sets a selection probability per partial program in such a manner that a partial program having a higher contribution level is given a higher selection probability. When the evolution processing is performed, a generation processing unit selects a partial program to be included in the post-evolution individual in mutation processing according to the set selection probabilities.

Abstract: An information processing apparatus is configured to execute generation processing for generating candidates of second image processing program based on an image processing program, the second image processing program being a next generation image processing program in genetic programming, (b): execute first evaluation processing for evaluating fitness of the candidates of the second image processing program by using low-resolution learning data including an input image and a target processing result, the low-resolution learning data being obtained by reducing the resolution of at least the input image, (c): execute second evaluation processing for narrowing down the candidates of the second image processing program based on an evaluation result in the first evaluation processing, and evaluating the fitness of the narrowed-down candidates by using the learning data, and (d): execute determination processing for determining the second image processing program based on an evaluation result in the second evaluat