Nippon Telegraph and Telephone Corporation

Xiaomeng Wu

Kunio Kashino

Takahito Kawanishi

Go Irie

Kaoru Hiramatsu

Krishna Onkar

A search apparatus for searching media data for a target region that matches query data includes a first feature extraction unit configured to extract a first feature vector from the query data using a first trained neural network; a second feature extraction unit configured to obtain a first region from the media data and extract a second feature vector from the first region using a second trained neural network; a localization unit configured to determine a candidate for the target region using a third trained neural network, based on the first feature vector, the second feature vector, and the first region or a location of the first region; and a control unit configured to repeat the operations of the second feature extraction unit and the localization unit until a predetermined condition is satisfied, by using the determined candidate for the target region as the first region to be used by the second feature extraction unit.

Search apparatus, training apparatus, search method, training method, and program

An image adjustment device includes: an illumination component derivation unit that derives an illumination component of a grayscale image; a reflectance component derivation unit that derives a reflectance component image that is a resulting image in which the illumination component is removed from the grayscale image; a contrast component derivation unit that derives a contrast component based on a contrast value between a pixel of the reflectance component image and a peripheral area of the pixel; a histogram derivation unit that derives a luminance histogram of the grayscale image weighted according to the contrast value for each pixel of the contrast component; a conversion function derivation unit that derives a luminance conversion function for converting a luminance such that a luminance histogram of a converted grayscale image in which the grayscale image is converted by the luminance conversion function and a predetermined histogram are matched with or similar to each other; and a luminance conversion unit that generates the converted grayscale image.

Image adjustment apparatus, image adjustment method, and program

The purpose of the present invention is to enable learning of a neural network for extracting features of images having high robustness from an undiscriminating image region while minimizing the number of parameters of a pooling layer. A parameter learning unitlearns parameters of each layer in a convolutional neural network configured by including a fully convolutional layer for performing convolution of an input image to output a feature tensor of the input image, a weighting matrix estimation layer for estimating a weighting matrix indicating a weighting of each element of the feature tensor, and a pooling layer for extracting a feature vector of the input image based on the feature tensor and the weighting matrix. The parameter learning unitlearns the parameters such that a loss function value obtained by calculating a loss function expressed by using a distance between a first feature vector of a first image and a second feature vector of a second image, which are relevant images and are obtained by applying the convolutional neural network, becomes smaller.

Image feature learning device, image feature learning method, image feature extraction device, image feature extraction method, and program

An image collation device includes: a tentative matching point extraction unit configured to extract a plurality of tentative matching points that are pairs of corresponding feature points between a pair of input images; and a spatial verification unit configured to evaluate a degree of similarity between the pair of input images on the basis of all of a limiting conditions regarding consistency of a scale factor, a limiting conditions regarding consistency of a rotational angle, and a limiting conditions regarding consistency of a relative positional relationship according to a comparison of or a difference between geometric transformation parameters of the tentative matching points for each of the pairs of tentative matching points that is a combination of the tentative matching points extracted by the tentative matching point extraction unit.

Growing community of inventors

Tokyo, Japan

Xiaomeng Wu