Nec Corporation

Shigeaki Namiki

Takashi Shibata

Shoji Yachida

The image recognition apparatus includes an image selection unit and a recognition unit. The image selection unit selects a feature image representing a feature portion of an object from among captured images of a time series in which the object is photographed. For example, the feature image corresponds to an image showing an abnormal portion. The recognition unit performs a recognition process of the object using the feature image. By the recognition process, an abnormality of the object is detected.

Image recognition apparatus, image recognition method, and recording medium

The learning apparatus classifies target domain data into (N-c) classes based on unique features of the target domain data, classifies source domain data into N classes based on unique features of the source domain data, and classifies the target domain data and the source domain data into the N classes based on common features of the target domain data and the source domain data. Also, the learning apparatus calculates a first distance between the common features of the target domain data and the source domain data, and calculates a second distance between the unique features of the target domain data and the source domain data. Next, the learning apparatus updates parameters of a common feature extraction unit based on the first distance, and updates parameters of a target domain feature extraction unit and a source domain feature extraction unit based on the second distance.

Learning apparatus, learning method, and recording medium

A system includes: a unit that input a series image sequence; a unit that selects a reference image; a unit that selects a proximity image; and an inference unit that recognizes the reference image and the proximity image by performing inference processing, including convolution processing and activation function processing, on the reference image and the proximity image. The inference unit generates results of performing the convolution processing and the activation function processing on the proximity image from the results of the convolution processing and the activation function processing performed on the reference image, and the results of the product of the results of the convolution processing performed on a difference image, which is an image of the difference between the reference image and the proximity image, and a derivative value of the results of the convolution processing and the activation function processing performed on the reference image.

Image recognition system, image recognition apparatus, image recognition method, and computer readable medium

A system includes: a sequential image string input unit configured to input a sequential image string having sequentiality; a reference image selection unit configured to select one or more images from the sequential image string as reference images; a variation calculation unit configured to select an adjacent reference image adjacent to the reference image from the sequential image string and calculate a variation between the reference image and the adjacent reference image; an image information regression unit configured to calculate class confidence by regression processing with the reference image as an input; a difference image information regression unit configured to calculate class confidence by regression processing with the variation as an input; a confidence integration unit configured to integrate class confidence calculated by the image information regression unit and class confidence calculated by the difference image information regression unit; and an output unit configured to output the integrated class confidence.

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Tokyo, Japan

Shigeaki Namiki