Nippon Telegraph and Telephone Corporation

Takashi Hosono

Jun Shimamura

Tetsuya Kinebuchi

Yukito Watanabe

Shuhei Tarashima

Atsushi Sagata

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

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

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

Objectness indicating a degree of accuracy of a single object is accurately estimated. An edge detection unitdetects an edge for a depth image, an edge density/uniformity calculation unitcalculates an edge density on the periphery of a candidate region, an edge density inside the candidate region, and edge uniformity on the periphery of the candidate region. An objectness calculation unitcalculates the objectness of the candidate region based on the edge density on the periphery of the candidate region, the edge density inside the candidate region, and the edge uniformity on the periphery of the candidate region.

Object likelihood estimation device, 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

Takashi Hosono