A stripe pattern image collating device according to the example embodiment includes a feature extracting unit that extracts a feature point and a skeleton from a first stripe pattern image and a second stripe pattern image in which a stripe pattern is formed of ridges, and generates feature point data and skeleton data. A skeleton collating unit that collates two sets of pieces of the feature point data and pieces of the skeleton data that are extracted from each of the first stripe pattern image and the second stripe pattern image, and calculates a collation score. An image analyzing unit that analyzes the second stripe pattern image with respect to an area in which an opposite feature point pair of the first stripe pattern image exists, calculates an image analysis score, and corrects the collation score.

The invention relates to a method for automatic segmentation of a dental arch that comprises acquiring a three-dimensional surface of the dental arch, in order to obtain a three-dimensional representation comprising a set of vertices, generating virtual views from the three-dimensional representation, projecting the three-dimensional representation onto each two-dimensional virtual view, in order to obtain an image representing each vertex on the virtual view, processing each image by means of a deep learning network, carrying out inverse projection of each image in order to assign, to each vertex of the three-dimensional representation, one or more pixels of the images in which the vertex appears and to which it corresponds, and assigning one or more probability vectors to each vertex, determining the class of dental tissue to which each vertex most probably belongs based on the probability vector or vectors.

A search information acquisition unit acquires a plurality of pieces of search pose information generated for each of a plurality of target images and indicate a pose of a person included in the target image. An exclusion information acquisition unit acquires exclusion pose information indicating a pose of a person included in an exclusion query image. The exclusion query image is an image to be a query of an image needed to be excluded from a search result, and includes at least a person. An exclusion score computation unit computes an exclusion score for each of the plurality of pieces of search pose information. The exclusion score indicates a degree of similarity of the search pose information to the exclusion pose information. An exclusion image selection unit selects, from the plurality of target images by using the exclusion score, an image needed to be excluded from a search result.

An image processing apparatus detects, from an image, a subject(s) of a first type and a subject(s) of a second type. The apparatus further detects a posture for each of the subject(s) of the first type. The apparatus then obtains, for each of the subject(s) of the first type, reliability that the subject is a main subject, based on the posture, and obtains a focus condition for each of the subject(s) of the first type and each of the subject(s) of the second type. The apparatus determines, based on the reliability and the focus condition, a main subject from the subject(s) of the first type and the subject(s) of the second type detected from the image.

A vessel displaying method and device, a computer device, and readable storage medium. The method includes: acquiring vessel information of a detected object, the vessel information including a vessel segment center line and corresponding vessel segment data of each vessel segment; receiving an edit instruction; editing an initial target vessel segment center line based on the edit instruction, to obtain a final target vessel segment center line; constructing a final target vessel segment according to the final target vessel segment center line; and constructing a target vessel based on the final target vessel segment, and displaying the target vessel.

Systems and methods for correcting motion artifacts in images are provided. The methods may include obtaining multiple preliminary images of a subject. Each of the multiple preliminary images may correspond to one of multiple phases of at least one motion cycle of the subject. The methods may also include determining an image representation of a target region of the subject in each preliminary image. The methods may also include identifying a target phase of the subject among the multiple phases of the at least one motion cycle based on the image representation of the target region in each preliminary image. The target phase and one or more neighboring phases of the target phase may correspond to a target motion status of the target region. The method may further include generating a secondary image of the subject corresponding to the target phase based on the target phase of the subject.

Systems and methods are presented for generating statistics associated with a performance of a participant in an event, wherein pose data associated with the participant, performing in the event, are processed in real time. Pose data associated with the participant may comprise positional data of a skeletal representation of the participant. Actions performed by the participant may be determined based on a comparison of segments of the participant's pose data to motion patterns associated with actions of interests.

A query acquisition unit (610) acquires pose information (hereinafter, described as query pose information) that is information to be a query and indicates a pose of a person. A search information acquisition unit (620) acquires a plurality of pieces of search pose information. The search pose information is pose information about an image (hereinafter, described as a target image) to be a search target, and is stored in a database by a plurality of target images. A selection unit (630) selects, from among the plurality of pieces of search pose information, two or more pieces of the search pose information whose degree of similarity to the query pose information satisfies a reference. The selection processing becomes substantially processing of selecting a target image similar to an image (hereinafter, described as a query image) associated with the query pose information.

The present disclosure relates to a simple, fast, and low cost method for 3D microvascular imaging, termed “scatter labeled imaging of microvasculature in excised tissue” (SLIME). The method can include perfusing a contrast agent through vasculature of a tissue sample. The contrast agent can include colloids and a dispersant. After the contrast agent is perfused through the vasculature, the vasculature of the tissue sample can be treated with a molecule that cross links with at least a portion of the dispersant to form a sticky, non-Newtonian polymer that prevents leakage of the contrast agent out of the vasculature of the tissue sample. The tissue sample can then be immersed in a solution comprising a clearing agent and subsequently imaged.

Systems, methods, and computer-readable media for evaluating a set of computed tomography data associated with a lattice structure. The lattice structure may be additively manufactured. The computed tomography data may be segmented using a filter for identifying blob-like structures to identify nodes present within the lattice structure. A three-dimensional path traversal is applied to volumetric data to identify a plurality of struts within the lattice structure that are compared to corresponding struts within a set if three-dimensional mesh data of the lattice structure to identify defective struts. Further, two-dimensional slices may be extracted from each of the computed tomography data and the mesh data and compared to identify one or more inconsistencies indicative of defects within the lattice structure.