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:
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:
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 image acquisition device includes an optical system, an illumination angle adjustment mechanism, and a stage. The optical system has a lens and a light source disposed in the focal plane of the lens, and generates a collimated illumination light. The illumination angle adjustment mechanism is configured so as to be able to change the irradiation direction of the illumination light with respect to an object. A module is detachably loaded on a stage. The module includes the object and an image sensor which are integrated such that the illumination light transmitted through the object is incident on the image sensor. The stage has a circuit for receiving an output of the image sensor in a state where the module is loaded on the stage.
Abstract:
An image output device according to the present disclosure includes: an image acquisition unit that acquires an image with a first resolution; a high-resolution image acquisition unit that acquires an image with a second resolution, being an image of higher resolution than the image with the first resolution; an enlargement input unit that accepts input of an enlargement ratio; a determination unit that determines whether or not an evaluation score determined based on the accepted enlargement ratio is higher than a certain value; and a transmission unit that transmits the image with the second resolution if the evaluation score is determined to be higher than the certain value, and does not transmit the image with the second resolution if the evaluation score is determined not to be higher than the certain value.
Abstract:
An image acquisition device according to the present disclosure includes a lighting system and an irradiation direction decision section. In a module, a subject and an imaging element are integrally formed. The lighting system sequentially irradiates the subject with illumination light in a plurality of different irradiation directions based on the subject such that the illumination light transmitted through the subject is incident on the imaging element. The module acquires a plurality of images according to the plurality of different irradiation directions. Before the plurality of images are acquired according to the plurality of different irradiation directions, the irradiation direction decision section decides the plurality of different irradiation directions based on a difference between a first preliminary image and a second preliminary image. The first preliminary image is acquired when the subject is irradiated with first illumination light in a first irradiation direction, and the second preliminary image is acquired when the subject is irradiated with second illumination light in a second irradiation direction.
Abstract:
An image processing apparatus includes a divider that generates a plurality pieces of third image information on the basis of a plurality of pieces of first image information and a plurality of pieces of second image information, a determiner that determines, on the basis of information regarding a sample, a filter to be used for the plurality of pieces of third image information, and a processor that deconvolutes each of the plurality of pieces of third image information using the determined filter. An image sensor that has received first resulting light emitted from a sample that has received first light emitted from a first angle outputs the plurality of pieces of first image information. The image sensor that has received second resulting light emitted from the sample that has received second light emitted from a second angle outputs the plurality of pieces of second image information.
Abstract:
A preparation element set including an image sensor including a sensor surface, a sensor back surface, and a board; a package including a front surface, a back surface, and terminals on the back surface, the front surface touching or facing the sensor back surface; and a transparent plate facing the sensor surface with a subject placed therebetween, wherein the board includes a board surface and a board back surface, a distance between the board surface and the sensor surface is less than a distance between the board back surface and the sensor surface, a distance between the board surface and the sensor back surface is more than a distance between the board back surface and the sensor back surface, conductive holes pierce the board from the board surface to the board back surface, and conductors on the board surface are electrically connected to terminals by using the conductive holes.
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:
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.