Nec Corporation

Azusa Sawada

Takashi Shibata

Shoji Yachida

Soma Shiraishi

Toshinori Hosoi

Keiko Inoue

Katsuhiko Takahashi

Hiroyoshi Miyano

Kenta Senzaki

Masato Toda

Kyota Higa

Kazuto Takizawa

A determination apparatus includes: a dividing unit configured to divide chronological image data acquired by imaging a liquid filled in a container while switching between a plurality of illumination conditions, into chronological image data corresponding to the illumination conditions; and a determining unit configured to determine foreign matter contained in the container based on each of the chronological image data obtained by division by the dividing unit.

Determination apparatus

An inspection system includes: a suspended matter detecting and tracking means that detects and tracks suspended matter present in the liquid in the container in chronological images obtained by consecutively imaging the liquid with a camera; a determining and storage controlling means that determines, based on a movement locus of the tracked suspended matter, whether the suspended matter is foreign matter or an air bubble, and stores inspection result information including a result of the determination and information of the movement locus of the suspended matter used as a basis for the determination into a storing means in association with identification information of the container; and a display controlling means that causes a displaying means to display the result of the determination and the information of the movement locus of the suspended matter used as the basis for the determination included by the inspection result information.

Inspection system

A learning apparatus includes a metric space learning unit and a case example storage unit. The metric space learning unit learns a metric space including feature vectors extracted from sets of attribute-attached image data for each combination of different attributes by using the sets of attribute-attached image data to which pieces of attribute information are added. The case example storage unit calculates feature vectors from sets of case example image data, and stores the feature vectors as case examples associated with the metric space.

Learning apparatus, learning method, inference apparatus, inference method, and recording medium

The learning device includes a metric space learning unit and a case example storage unit. The metric space learning unit learns a metric space including feature vectors extracted from attributed image data, for each combination of different attributes, using the attributed image data to which attribute information is assigned. The case example storage unit computes the feature vector from the image data for case example to store the computed feature vector as a case example associated with the metric space, and stores additional information associated with the case example.

Object recognition device, method, and computer-readable medium

An input image acquisition unit acquires a plurality of input images in which a specific detection target is captured by a plurality of different modalities. A perturbed image acquisition unit acquires a plurality of perturbed images in which at least one of the plurality of input images is perturbed. A detection processing unit detects a detection target included in the input images using each of the plurality of perturbed images and one of the plurality of input images that has not been perturbed, and acquires, for each of the plurality of perturbed images, a detection position of the detection target and a detection confidence level as detection results. An adjustment unit calculates, based on the detection positions and the confidence levels acquired for the plurality of perturbed images, an adjusted confidence level for each of the perturbed images using integrated parameters.

Object detection apparatus, object detection system, object detection method, and non-transitory computer readable medium storing program

An image processing apparatus according to the present invention includes: an extracting unit configured to extract a candidate image, which is an image of a candidate region specified in accordance with a preset criterion, from a target image to be a target for an annotation process, and also extract a corresponding image, which is an image of a corresponding region corresponding to the candidate region, from a reference image that is an image corresponding to the target image; a displaying unit configured to display the candidate image and the corresponding image so as to be able to compare the images with each other; and an input accepting unit configured to accept input of input information for the annotation process for the candidate image.

Image processing method

An information processing apparatus () according to the present disclosure includes: an object recognition unit () that outputs, by using a first modal signal and a first modal recognition model corresponding to the first modal signal, an inference result regarding the first modal signal; a training data processing unit () that generates first modal training data regarding the first modal signal by using the inference result, and updates second modal training data regarding a second modal signal that is different from the first modal signal by using the first modal training data; and a recognition model update unit () that updates a second modal recognition model corresponding to the second modal signal by using the second modal training data.

Information processing apparatus, information processing method, and non-transitory computer readable medium

An image generation meansgenerates an image using a generator. A discrimination meansdiscriminates whether an object image includes a feature of a target image, using a discriminator. A first update meansupdates the generator so as to minimize a first error representing a degree of divergence between a result of discriminating a generated image using the discriminator and a correct answer label associated with the generated image, the generated image being the image generated using the generator. A second update meansupdates the discriminator so as to minimize a second error representing a degree of divergence between each of respective results of discriminating the generated image, a first actual image including the feature of the target image, and a second actual image not including the feature of the target image using the discriminator and a correct answer label associated with a corresponding image.

Image generation device, image generation method, and image generation program

The purpose of the present invention is to detect an object in images accurately by means of image recognition without using a special device for removing the influence of the parallax between a plurality of images. An image transformation unit () transforms a plurality of images acquired by an image acquisition unit (). A reliability level calculation unit () calculates a level of reliability representing how small the misalignment between images is. A score calculation unit () calculates a total score taking into account both an object detection score based on a feature quantity calculated by a feature extraction unit (), and the level of reliability calculated by the reliability level calculation unit (). An object detection unit () detects an object in the images on the basis of the total score.

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Tokyo, Japan

Azusa Sawada