Konica Minolta, Inc.

Taiki Sekii

A detection method of detecting a defined object from an image, includes estimating, on the image, an extreme point area including a boundary feature point that satisfies a criterion related to a boundary of the object.

Object detection method and object detection device

An object detection method includes a key point estimation step of estimating key point candidates for each object in an image; and a detection step of detecting key points for each object based on the estimated key point candidates. Considering an object model that models shape of an object, the key points are points that satisfy a defined condition among points indicating a boundary of the object model that are projected onto defined coordinate axes. The defined coordinate axes have an origin at a geometric center of the object model and each forms a defined angle relative to a polar axis in a polar coordinate system set for the object model.

Object detection method, object detection device, and program

A method for estimating a three-dimensional pose from control points of an image of the object, includes: selecting, for each pair consisting of first and second control points that define a skeleton of the object, relative positions of the first control point with respect to the second control point on the image, estimating, for each of the selected relative positions, a relative depth of the first control point with respect to the second control point over the entire image based on an assumption that the second control point exists at each position on the image; detecting two-dimensional positions of the control points on the image for the object using the image; obtaining, based on the relative depth estimated for each of the selected relative positions and the two-dimensional positions of the control points, relative three-dimensional positions of the control points; and estimating the three-dimensional pose for the object.

Three-dimensional pose estimation method, program, recording medium and three-dimensional pose estimation device

Disclosed is a learning method that may include generating learning data that may contain a composite image including a CG model of an object and may contain a training signal of the object. Further disclosed is a way of learning a recognition function of recognizing information regarding the object from the composite image by neuro computation using the learning data, the generated learning data including new learning data on the basis of a gradient of error for each of pixels of the composite image, which may be calculated from the composite image and the training signal by backpropagation. The disclosed learning may include learning the recognition function by using the new learning data.

Learning method, learning device, program, and recording medium

Image recognition device that combines first part candidates and second part candidates to generate a plurality of pairs; that calculates first estimate value for each of the plurality of pairs, this value indicating estimated possibility that these candidates are connected; that executes, for each of the plurality of pairs, correcting first estimate value by using second estimate value as a weight in a state in which a part candidate that is one among a pair of part candidates matching of which has already been performed and that differs from both first part candidate and second part candidate is connected to either first part candidate or second part candidate, second estimate value indicating estimated possibility that the pair of part candidates matching of which has already been performed are connected; and that performs matching of first part candidates and second part candidates using corrected first estimate values.

Image recognition device, image recognition method, and computer-readable recording medium having image recognition program recorded thereon

An image recognition device includes: a hardware processor that: conducts machine learning, to perform a first process of calculating a plurality of region candidates for a region showing part of an object captured in an image, and a second process of determining a size of each of the region candidates in accordance with the object captured in the image; and determines the region from among the region candidates, using a predetermined criterion.

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Takatsuki, Japan

Taiki Sekii