A virtual reality or mixed reality system configured to preform object detection using a monocular camera. The system configured to make the user aware of the detected objects by showing edges or lines of the object within a virtual scene. Thus, the user the user is able to avoid injury or collision while immersed in the virtual scene. In some cases, the system may also detect and correct for drift in the six degree of freedom pose of the user using corrections based on the current motion of the users.

A virtual reality or mixed reality system configured to preform object detection using a monocular camera. The system configured to make the user aware of the detected objects by showing edges or lines of the object within a virtual scene. Thus, the user the user is able to avoid injury or collision while immersed in the virtual scene. In some cases, the system may also detect and correct for drift in the six degree of freedom pose of the user using corrections based on the current motion of the users.

A method and an apparatus for compensating image segmentation lines are provided. In the method, image segmentation is applied to a medical image captured by using a segmentation model to obtain a segmentation image including at least one segmentation line between multiple layers in the medical image. Convolution computation is then performed on the segmentation image by using a kernel of a trained classification model to predict a location of a next pixel connected to a current pixel in the respective segmentation line within the segmentation image, in which the pixel to be predicted is limited to a neighboring pixel of the current pixel in a prediction direction. The predicted pixels are connected to form a compensated segmentation line for each segmentation line.

Methods for image-based detection of the tops of sample tubes used in an automated diagnostic analysis system may be based on a convolutional neural network to pre-process images of the sample tube tops to intensify the tube top circle edges while suppressing the edge response from other objects that may appear in the image. Edge maps generated by the methods may be used for various image-based sample tube analyses, categorizations, and/or characterizations of the sample tubes to control a robot in relationship to the sample tubes. Image processing and control apparatus configured to carry out the methods are also described, as are other aspects.

The invention provides an edge detection method and a device of an object in an image, an electronic equipment, and a computer-readable storage medium. The method includes: a line drawing of a grayscale contour in the image is obtained; similar lines in the line drawing are merged to obtain initial merged lines, and a boundary matrix is determined according to the initial merged lines; similar lines in the initial merged lines are merged to obtain target lines, and unmerged initial merged lines are also used as target lines; reference boundary lines are determined from the target lines according to the boundary matrix; boundary line regions of the object in the image are obtained; a target boundary line corresponding to the boundary line region is determined from the reference boundary lines; an edge of the object in the image is determined according to the determined target boundary lines.

The invention provides an edge detection method and a device of an object in an image, an electronic equipment, and a computer-readable storage medium. The method includes: a line drawing of a grayscale contour in the image is obtained; similar lines in the line drawing are merged to obtain initial merged lines, and a boundary matrix is determined according to the initial merged lines; similar lines in the initial merged lines are merged to obtain target lines, and unmerged initial merged lines are also used as target lines; reference boundary lines are determined from the target lines according to the boundary matrix; boundary line regions of the object in the image are obtained; a target boundary line corresponding to the boundary line region is determined from the reference boundary lines; an edge of the object in the image is determined according to the determined target boundary lines.

Various embodiments relate to a computer apparatus for the bone microstructure connectivity recovery of a skeletal image reconstructed through an artificial neural network using the representations of a node-link graph-based bone microstructure and a method thereof. The computer apparatus and the method may be configured to represent a node-link graph from a bone microstructure of an input skeletal image, reinforce a connectivity of the bone microstructure in the node-link graph, and change the node-link graph into a skeletal image.

Various embodiments relate to a computer apparatus for the bone microstructure connectivity recovery of a skeletal image reconstructed through an artificial neural network using the representations of a node-link graph-based bone microstructure and a method thereof. The computer apparatus and the method may be configured to represent a node-link graph from a bone microstructure of an input skeletal image, reinforce a connectivity of the bone microstructure in the node-link graph, and change the node-link graph into a skeletal image.

Provided is a method for generating an orthodontic teeth alignment shape for the orthodontic treatment of a patient. According to the present invention, there is provided a method for generating an orthodontic teeth alignment shape, the method including the steps of: acquiring a patient's teeth shape information; extracting specific points of respective teeth within a given range of at least one of the patient's upper and lower jaws, based on the acquired teeth shape information; comparing the patient's arch form produced by connecting the specific points to one another with a plurality of standard arch forms stored in a database; selecting the standard arch form that is most similar to the patient's arch form from the plurality of standard arch forms stored in the database, based on the comparison result; and generating the orthodontic teeth alignment shape based on the selected standard teeth alignment form.

An apparatus, an electronic device, and a storage medium may implement a method for generating a dynamic video of a character. The method includes: identifying a character contour area from a first picture containing a character image; acquiring a plurality of sampling points in the first picture based on the character contour area, and dividing the first picture into a plurality of triangles by each of the sampling points; deforming at least a portion of the plurality of triangles in the first picture to obtain a second picture; and acquiring at least one intermediate picture between the first picture and the second picture, and generating the dynamic video of the character comprising the first picture, the second picture and the at least one intermediate picture.