Abstract: Provided are an inspection method, a program, and an inspection system capable of improving accuracy of inspecting a color of a surface of an object. The inspection method includes acquisition step and comparison step. Acquisition step is a step of acquiring a target image of a surface of an object obtained by an imaging system imaging the surface of the object illuminated by an illumination system. Comparison step, is a step of comparing a color of an attention region on the target image with a color of a reference region. The reference region is a region of a reference image of a surface of a reference object as a reference of a color of the object, and a region corresponding to a combination of an incident angle of light from the illumination system and a reflection angle of light to the imaging system in the attention region.

Abstract: Provided are an inspection method, a program, and an inspection system capable of improving accuracy of inspecting a color of a surface of an object. The inspection method includes acquisition step and comparison step. Acquisition step is a step of acquiring a target image of a surface of an object obtained by an imaging system imaging the surface of the object illuminated by an illumination system. Comparison step is a step of comparing a color of an attention region on the target image with a color of a reference region. The reference region is a region of a reference image of a surface of a reference object as a reference of a color of the object, and a region corresponding to a combination of an incident angle of light from the illumination system and a reflection angle of light to the imaging system in the attention region.

Abstract: An inspection system includes an acquisition unit and a determination unit. The acquisition unit acquires an image representing a surface of an object. The determination unit performs color determination processing. The color determination processing is performed to determine a color of the surface of the object based on a plurality of conditions of reflection. The plurality of conditions of reflection are obtained from the image representing the surface of the object as acquired by the acquisition unit, and have a specular reflection component and a diffuse reflection component at respectively different ratios on the surface of the object.

Abstract: An inspection system includes an acquisition unit and a determination unit. The acquisition unit acquires an image representing a surface of an object. The determination unit performs color determination processing. The color determination processing is performed to determine a color of the surface of the object based on a plurality of conditions of reflection. The plurality of conditions of reflection are obtained from the image representing the surface of the object as acquired by the acquisition unit, and have a specular reflection component and a diffuse reflection component at respectively different ratios on the surface of the object.

Abstract: Provided is an evaluation method for learning data that facilitates generation of learning data that can contribute to the improvement of the recognition rate of a model. An evaluation method for learning data includes a first evaluation step and a second evaluation step. The first evaluation step is a step of evaluating the performance of learned model machine-learned by using learning data generated by the data extension processing. The second evaluation step is a step of evaluating a parameter on the basis of the evaluation obtained in the first evaluation step and the possible range of the parameter of the data extension processing.

Abstract: Reliability regarding a class determination for an object is improved. Classification system includes first classification part, second classification part, and determination part. First classification part classifies first target data into at least one of a plurality of first classes. Second classification part classifies second target data into at least one of a plurality of second classes. Determination part decides whether to use one or both of a first classification result that is a classification result obtained by first classification part and a second classification result that is a classification result obtained by second classification part, and determines a class of object based on one or both of them. The first target data is image data of object. The second target data is manufacturing data regarding a manufacturing condition of object.

Abstract: An inspection system includes an acquisition unit and a determination unit. The acquisition unit acquires an image representing a surface of an object. The determination unit performs color determination processing. The color determination processing is performed to determine a color of the surface of the object based on a plurality of conditions of reflection. The plurality of conditions of reflection are obtained from the image representing the surface of the object as acquired by the acquisition unit, and have a specular reflection component and a diffuse reflection component at respectively different ratios on the surface of the object.

Abstract: Even when data that can belong to a new class that is not in an existing class is input, this data can be easily classified appropriately. Classification system includes input reception part, classification part, calculation part, determination part, and presentation part. Input reception part receives an input of target data. Classification part classifies the target data into any one of a plurality of classes. Calculation part calculates a feature amount of the target data. Determination part determines a possibility that the target data is classified into the new class based on a classification result in classification part and the feature amount of the target data calculated by calculation part. When determination part determines that there is a possibility that the target data is classified into the new class, presentation part presents a determination result of determination part.

Abstract: Provided are an inspection method, a program, and an inspection system capable of improving accuracy of inspecting a color of a surface of an object. The inspection method includes acquisition step and comparison step. Acquisition step is a step of acquiring a target image of a surface of an object obtained by an imaging system imaging the surface of the object illuminated by an illumination system. Comparison step is a step of comparing a color of an attention region on the target image with a color of a reference region. The reference region is a region of a reference image of a surface of a reference object as a reference of a color of the object, and a region corresponding to a combination of an incident angle of light from the illumination system and a reflection angle of light to the imaging system in the attention region.

Abstract: An inspection system includes an acquisition unit and a determination unit. The acquisition unit acquires an image representing a surface of an object. The determination unit performs color determination processing. The color determination processing is performed to determine a color of the surface of the object based on a plurality of conditions of reflection. The plurality of conditions of reflection are obtained from the image representing the surface of the object as acquired by the acquisition unit, and have a specular reflection component and a diffuse reflection component at respectively different ratios on the surface of the object.

Abstract: An inspection system includes an acquisition unit and a determination unit. The acquisition unit acquires an image representing a surface of an object. The determination unit performs color determination processing. The color determination processing is performed to determine a color of the surface of the object based on a plurality of conditions of reflection. The plurality of conditions of reflection are obtained from the image representing the surface of the object as acquired by the acquisition unit, and have a specular reflection component and a diffuse reflection component at respectively different ratios on the surface of the object.

Abstract: An information processing system receives first travel histories from vehicles that belong to vehicle type A, learns based on the first travel histories to build a first driver model that represents relation between travel situations and behaviors of the vehicles that belong to a first vehicle type, receives second travel histories from vehicles that belong to vehicle type X that is different from vehicle type A, and performs transfer learning in which the second travel histories are used for the first driver model to build a second driver model that represents relation between travel situations and behaviors of the vehicles that belong to vehicle type X.

Abstract: An electronic prepared slide includes: an image sensor that has a light receiving surface and receives, on the light receiving surface, light that has passed through a specimen disposed above the light receiving surface; and a removable nonvolatile transparent film that is disposed on the light receiving surface and seals the light receiving surface.

Abstract: An image display system includes a first processor, a second processor, and a comparator. The first processor acquires a behavior estimation result of a vehicle, and generates future position information after a predetermined time passes of the vehicle based on the behavior estimation result. The second processor acquires present information about the vehicle, and generates present position information on the vehicle and a peripheral object based on the acquired information. The comparator compares the future position information on the vehicle and the present position information on the vehicle and the peripheral object, and generates present image data indicating present positions of the vehicle and the peripheral object and future image data indicating future positions of the vehicle and the peripheral object. Further, the comparator allows a notification device to display a present image based on the present image data and a future image based on the future image data together.

Abstract: An information processing system that appropriately estimates a driving conduct includes: a detector that detects a vehicle environment state, which is at least one of surroundings of a vehicle and a driving state of the vehicle; a behavior learning unit configured to cause a neural network to learn a relationship between the vehicle environment state detected by the detector and a behavior of the vehicle implemented after the vehicle environment state; and a behavior estimation unit configured to estimate a behavior of the vehicle by inputting, into the neural network that learned, the vehicle environment state detected at a current point in time by the detector.

Abstract: An information processing system receives first travel histories from vehicles that belong to vehicle type A, learns based on the first travel histories to build a first driver model that represents relation between travel situations and behaviors of the vehicles that belong to a first vehicle type, receives second travel histories from vehicles that belong to vehicle type X that is different from vehicle type A, and performs transfer learning in which the second travel histories are used for the first driver model to build a second driver model that represents relation between travel situations and behaviors of the vehicles that belong to vehicle type X.

Abstract: A driving support device as an example of a data processor executes processing for estimating a driving behavior of a vehicle by using a driving behavior model trained based on detection results by a sensor. A detected-information input unit acquires detected information including the detection results. From the detection results included in the detected information input to the detected-information input unit, a selection unit selects a detection result that falls within predetermined selection range narrower than a range detectable by the sensor. A processing unit executes the processing, based on the detection result selected by the selection unit.

Abstract: An image display system includes a first processor, a second processor, and a comparator. The first processor acquires a behavior estimation result of a vehicle, and generates future position information after a predetermined time passes of the vehicle based on the behavior estimation result. The second processor acquires present information about the vehicle, and generates present position information on the vehicle and a peripheral object based on the acquired information. The comparator compares the future position information on the vehicle and the present position information on the vehicle and the peripheral object, and generates present image data indicating present positions of the vehicle and the peripheral object and future image data indicating future positions of the vehicle and the peripheral object. Further, the comparator allows a notification device to display a present image based on the present image data and a future image based on the future image data together.

Abstract: An information processing system includes an incorrectness risk determiner, a safe behavior determiner, and a safety determiner. The incorrectness risk determiner makes a determination on whether or not a behavior estimation result of a vehicle carries an incorrectness risk. The safe behavior determiner classifies parameter values each indicating a travel state of the vehicle into multiple ranges based on travel safety. The safe behavior determiner also determines a safe behavior for the vehicle. By the safe behavior, the travel state of the vehicle is adjusted such that the above parameter values fall under a range with high travel safety. The safety determiner determines a behavior control of the vehicle. When the safety determiner acquires a determination including the incorrectness risk from the incorrectness risk determiner, the safety determiner selects the safe behavior.

Abstract: An information processing system includes a safe behavior determiner, clustering range controller, and safety determiner. The safe behavior determiner classifies parameter values indicating a travel state of a vehicle into multiple ranges based on travel safety. The safe behavior determiner also determines a safe behavior for the vehicle by which the travel state of the vehicle is adjusted such that the parameter values fall under a safety range with high travel safety. The clustering range controller changes a position of a boundary between the ranges according to an external environment of the vehicle. The safety determiner acquires a behavior estimation result of the vehicle and the safe behavior determined by the safe behavior determiner, and determines behavior control of the vehicle on the basis of at least the acquired behavior estimation result or safe behavior.