Provided is an inspection system for a wire electrical discharge machine, capable of automatically performing inspection of a constituent element and the like. The inspection system for a wire electrical discharge machine is provided with the wire electrical discharge machine, a robot for inspecting the constituent element of the wire electrical discharge machine, an image pickup device provided on a movable part of tee robot and configured to image the constituent element, an image processing unit configured to acquire an image of the constituent element by means of the image pickup device, and a maintenance necessity determination unit configured to determine the necessity of maintenance of the constituent element based on the image acquired by the image processing unit.

An information processing device includes a processor, and the processor is configured to collate images of plural positions on a surface of an object to be inspected with images of plural positions, which corresponds to the plural positions on the surface of the object to be inspected, on a surface of at least one reference object that is a reference, and present the number of successful collations among the plural positions and identification information on a reference object that matches the object to be inspected in a position where the collation is successful.

Disclosed is an image analysis method implemented by a computer, the method including analyzing a partial image which is a part of an image of a planar subject, generating partial-image analysis data representing a characteristic of the partial image, comparing, for each of a plurality of images, candidate-image analysis data with the partial-image analysis data, the candidate-image analysis data representing a characteristic of each of the plurality of images, and selecting a candidate image among the plurality of images, the candidate image including a part corresponding to the partial image.

Disclosed is an electronic device for a vehicle, including a processor receiving first image data from a first camera, receiving second image data from a second camera, receiving first sensing data from a first lidar, generating a depth image based on the first image data and the second image data, and fusing the first sensing data for each of divided regions in the depth image.

A high-resolution image matching method and system are provided. The method includes performing regional fidelity down-sampling on an initial high-resolution image to obtain a multi-level low-resolution image, performing local matching on the obtained multi-level low-resolution images using a method with global probes to obtain a matching result of the low-resolution images, and performing reverse refinement on the obtained matching result of the low-resolution image using overall consistency of the image matching, to obtain the matching results of the high-resolution images at all levels until the matching results of the initial resolution images are obtained, so as to reduce the computational complexity of the image matching process and improve the accuracy of the matching result, and then the matching result of the high-resolution image is obtained through reverse refinement based on the overall consistency.

An identifier of query content is accurately determined. A content determining device 100 includes an input unit 2 that inputs query content, a storage unit 1 that stores a plurality of pieces of master content, and a content determining unit 4 that determines a region where feature values of two pieces of master content out of the plurality of pieces of master content do not match each other, calculates a matching feature count which is a count of feature values of the region that match feature values in a corresponding region of the query content, for each of the two pieces of master content, and determines an identifier of the query content on the basis of the matching feature count of each of the pieces of master content.

In accordance with an aspect of the present disclosure, there is provided a vehicle sensor correction information determining method comprising: acquiring estimated location information of a vehicle based on matching information between a camera image photographed by a camera of the vehicle and a landmark on a precise map; and determining a scale factor for a wheel speed sensor based on an estimated travel distance calculated based on the acquired estimated location information of the vehicle and a travel distance of the vehicle detected by the wheel speed sensor of the vehicle.

A method, apparatus and computer-readable storage medium are provided to define a storyline based on path probabilities for a plurality of paths through the frames of a video. Relative to a method and for a plurality of frames of a video, regions of a first frame and regions of a second, subsequent frame that have been viewed are identified. For each of at least one first-frame region of one or more regions of the first frame, the method determines a transition probability of transitioning from a respective first-frame region of the first frame to each of at least one second-frame region of a plurality of regions of the second frame. Based on the transition probabilities, the method determines a path probability for each of at least one of a plurality of paths through the frames of the video. The method additionally defines a storyline based on the path probabilities.

Embodiments of the present application disclose a method and apparatus for detecting a body. A particular embodiment of the method comprises: acquiring a set of candidate body image region in a target image; for a candidate body image region in the set of candidate body image region: acquiring position information and confidences of candidate body key points in the candidate body image region; determining the candidate body key points within a body contour according to body contour information in the candidate body image region and the acquired position information; and determining a confidence score of the candidate body image region according to a sum of the confidences of the candidate body key points within the body contour; and determining a body image region from the set of candidate body image regions according to the confidence scores of the candidate body image regions in set of the candidate body image regions.

A method of label detection includes: obtaining, by an imaging controller, an image depicting a shelf; increasing an intensity of a foreground subset of image pixels exceeding an upper intensity threshold, and decreasing an intensity of a background subset of pixels below a lower intensity threshold; responsive to the increasing and the decreasing, (i) determining gradients for each of the pixels and (ii) selecting a candidate set of the pixels based on the gradients; overlaying a plurality of shelf candidate lines on the image derived from the candidate set of pixels; identifying a pair of the shelf candidate lines satisfying a predetermined sequence of intensity transitions; and generating and storing a shelf edge bounding box corresponding to the pair of shelf candidate lines.