Panasonic Intellectual Property Management Co., Ltd.

Panasonic Corporation

Matsushita Electric Industrial Co., Ltd.

National Institute of Information and Communications Technology, Incorporated

Pansonic Corporation

Panasonic Intellectual Property Corporation of America

Hideto Motomura

Katsuhiro Kanamori

Yoshikuni Sato

Takeo Azuma

Hiroyoshi Komobuchi

Yoshihide Sawada

Yasuhiko Adachi

Satoshi Sato

Kunio Nobori

Kenji Kondo

Jeffry Fernando

Taro Imagawa

Taichi Sato

Masanaga Tsuji

Takanobu Ojima

Koichi Emura

Yoshihisa Kato

Yoshiyuki Okimoto

Hisaji Murata

Sahim Kourkouss

Tsuyoshi Tanaka

Kazuki Kozuka

Yoshinori Matsui

Osamu Yamada

Teruo Fumoto

Eriko Ohdachi

Yuya Sugasawa

Rina Akaho

Hiroshi Matsumoto

Kiminori Mizuuchi

Katsuya Watanabe

Tokuhiko Tamaki

Toshiya Mori

Yutaka Hirose

Yoshifumi Hirose

Naoki Matsubara

Toshiharu Kurosawa

Hiroaki Kotera

Toru Okino

Norimichi Tsumura

Shota Yamada

Yoichi Miyake

Tetsu Suzuki

Hiroyuki Mori

Toshiya Nakaguchi

Kenro Ohsawa

Takashi Sugaya

Rika Hashimoto

Manabu Kamoshida

Mohamed Sahim Kourkouss

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.

Inspection method and inspection system

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.

Classification system and classification method

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.

Classification system and classification method using lerned model and model including data distribution

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.

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.

Inspection system, inspection method, program, and storage medium

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.

Information processing system, information processing method, program, and vehicle for generating a first driver model and generating a second driver model using the first driver model

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.

Electronic prepared slide, solid state imaging element, and electronic prepared slide assembly method

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.

Image display system, image display method, and recording medium

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.

Information processing system, information processing method, and program

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.

Driving support method, data processor using the same, and driving support system using the same

An image generating system according to an aspect of the present disclosure includes an image obtaining device, an image generating circuit, and an image processing circuit. The image obtaining device includes an illuminating system that irradiates an object included in a module in which the object and an imaging element are integrated together, with light sequentially from a plurality of different radiation directions. The image obtaining device obtains a plurality of images corresponding to the plurality of different radiation directions. The image generating circuit generates a high-resolution image of the object having a higher resolution than each of the plurality of images by combining the plurality of images together. The image processing circuit detects noise resulting from a foreign object located farther from an imaging surface of the imaging element than the object and removes the noise.

Image generating system, image generation method, imaging element, and recording medium

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.

Image acquisition device, image forming system, and image forming method

An image forming apparatus includes an imager that is electrically connected to an image sensor disposed at a position where light that has passed through a sample slice is incident on the image sensor, and an illumination system that emits illumination light successively in different illumination directions relative to a sample slice to illuminate the sample slice with the illumination light and that emits a first light having a peak in a first wavelength range and a second light having a peak in a second wavelength range. The image forming apparatus obtains a plurality of first-color images with the image sensor while the sample slice is being illuminated with the first light serving as the illumination light successively in the different illumination directions. The image forming apparatus obtains at least one second-color image with the image sensor while the sample slice is being illuminated with the second light in at least one of the different illumination directions. The image forming apparatus generates a high-resolution image on the basis of the plurality of first-color images and the at least one second-color image.

Image forming apparatus, image forming method, image forming system, and recording medium

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.

Image output device, image transmission device, image reception device, image output method, and non-transitory recording medium

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Kyoto, Japan

Hideto Motomura