Nippon Telegraph and Telephone Corporation

Yukito Watanabe

Jun Shimamura

Tetsuya Kinebuchi

Takashi Hosono

Atsushi Sagata

Shuhei Tarashima

Kaori Kumagai

Takayuki Umeda

The present invention makes it possible to estimate, with high precision, a candidate region indicating each of multiple target objects included in an image. A parameter determination unitdetermines parameters to be used when detecting a boundary line of an imagebased on a ratio between a density of boundary lines included in an imageand a density of boundary lines in a region indicated by region informationindicating the region including at least one of the multiple target objects included in the image. A boundary line detection unitdetects the boundary line in the imageusing the parameter. For each of the multiple target objects included in the image, the region estimation unitestimates the candidate region of the target object based on the detected boundary line.

Candidate region estimation device, candidate region estimation method, and program

An object included in a low-resolution image can be recognized with a high degree of precision. An acquisition unit acquires, from a query image, an increased-resolution image, which is acquired by increasing the resolution of the query image, by performing pre-learned acquisition processing for increasing the resolution of an image. A feature extraction unit, using the increased-resolution image as input, extracts a feature vector of the increased-resolution image by performing pre-learned extraction processing for extracting a feature vector of an image. A recognition unit recognizes an object captured on the increased-resolution image on the basis of the feature vector of the increased-resolution image and outputs the recognized object as the object captured on the query image.

Object recognition device, object recognition learning device, method, and program

A list for accurately identifying objects with different sizes that are the same attributes can be generated automatically. A classification unit classifies, from a group of images consisting of images including objects with any of a plurality of attributes, each of the images including the objects that have an identical attribute and have different real sizes into an identical cluster. An output unit outputs each of clusters into which at least two of the images are classified as a list of images with different real sizes of the objects for the identical attribute.

List generation device, photographic subject identification device, list generation method, and program

A retrieval apparatus includes a first retrieval unit, a second retrieval unit, and an integration unit that calculates an integrated similarity by integrating a first similarity calculated by the first retrieval unit and a second similarity calculated by the second retrieval unit. For similarities between a basic image, an image similar to the basic image, and an image dissimilar to the basic image of the reference images, at least the feature extraction of the first retrieval unit is learned such that a margin based on a second similarity between the basic image and the similar image and a second similarity between the basic image and the dissimilar image increases as the second similarity between the basic image and the dissimilar image increases relative to the second similarity between the basic image and the similar image.

Learning device, search device, learning method, search method, learning program, and search program

It is possible to determine a geometric transformation matrix representing geometric transformation between an input image and a template image with high precision. A geometric transformation matrix/inlier estimation sectiondetermines a corresponding point group serving as inliers, and estimates the geometric transformation matrix representing the geometric transformation between the input image and the template image. A scatter degree estimation sectionestimates scatter degree of the corresponding points based on the corresponding point group serving as inliers. A plane tracking convergence determination threshold calculation sectioncalculates a threshold used in convergence determination when iterative update of the geometric transformation matrix in a plane tracking sectionis performed based on the estimated scatter degree. The plane tracking sectioniterates update of the geometric transformation matrix so as to minimize a difference in value to pixels of a geometric transformation image obtained by transforming one of the input image and the templated image using the geometric transformation matrix and corresponding pixels until it is determined that convergence has been completed using the calculated threshold.

Template orientation estimation device, method, and program

Even if an object to be detected is not remarkable in images, and the input includes images including regions that are not the object to be detected and have a common appearance on the images, a region indicating the object to be detected is accurately detected. A local feature extraction unitextracts a local feature of a feature point from each image included in an input image set. An image-pair common pattern extraction unitextracts, from each image pair selected from images included in the image set, a common pattern constituted by a set of feature point pairs that have similar local features extracted by the local feature extraction unitin images constituting the image pair, the set of feature point pairs being geometrically similar to each other. A region detection unitdetects, as a region indicating an object to be detected in each image included in the image set, a region that is based on a common pattern that is omnipresent in the image set, of common patterns extracted by the image-pair common pattern extraction unit

Object detection device, method, and program

The present invention relates to representing image features used by a convolutional neural network (CNN) to identify concepts in an input image. The CNN includes a plurality of filters in each of a plurality of layers. The method generates the CNN based on a set of images for training with predetermined concepts in regions of the set of images. For a select layer of the CNN, the method generates integrated maps, Each integrated map is based on a set of feature maps in a cluster and relevance between the set of feature maps for the select layer and a region representing one of the features in the image data. The method provides a pair of a feature representation visualization image of a feature in the select layer and a concept information associated with the integration map.

Feature representation device, feature representation method, and program

An object is to make it possible to precisely infer a geometric transformation matrix for transformation between an image and a reference image representing a plane region even if correspondence to the reference image cannot be obtained. A first line segment group extraction unitextracts, out of a line segment group in an image, line segments that correspond to a direction that is parallel or perpendicular to a side of a rectangle included in the image, from the inside of the rectangle, takes the extracted line segments to be a first line segment group, and extracts a plurality of line segments different from the first line segment group out of the line segment group. An endpoint detection unitdetects four intersection points between ends of the image and two line segments that are selected from line segments that correspond to a direction that is parallel or perpendicular to a side of the rectangle and are extracted from a plurality of line segments obtained by transforming the different line segments using an affine transformation matrix in which an angle of the first line segment group relative to a reference direction of the image is used as a rotation angle. A homography matrix inferring unitcomputes a geometric transformation matrix based on the affine transformation matrix and a homography matrix computed based on correspondence between the four intersection points and the four vertexes of the rectangle in a reference image.

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Tokyo, Japan

Yukito Watanabe