A medical image diagnostic apparatus according to an embodiment includes processing circuitry. The medical image diagnostic apparatus performs image registration between medical image data. The processing circuitry extracts a structure of the subject included in the medical image data. The processing circuitry sets a display scale of the subject to a specified value. The processing circuitry performs image registration between the medical image data on the display scale of the specified value.

A method for generating a synthesized medical image by receiving a normal image includes generating first data based on a random selection, generating second data, and, based at least in part on the first and second data, modifying the normal image to form the synthesized medical image. Modifying the normal image comprises combining the first data and the second data. The first data characterizes an image that represents a lesion and the second data characterizes a transformation of that image as well as a location of the lesion.

Systems and techniques for improving costs and efficiency in affine motion prediction of a current block includes storing motion information of previously coded block in a line buffer or one or more local buffers of a coding unit such as coding tree unit (CTU). The line buffer can store motion information of a line of coding blocks located at a boundary of the CTU, while the local buffers can be utilized for storing motion information of coding blocks located anywhere in the CTU. The utilization of the line buffer can be improved by limiting the number of motion vectors of a neighboring block stored in the line buffer and obtaining remaining motion vectors from an additional block when available. The utilization of local buffers can be improved by restricting a size of neighboring blocks whose motion information is stored in the local buffers.

Systems and techniques for improving costs and efficiency in affine motion prediction of a current block includes storing motion information of previously coded block in a line buffer or one or more local buffers of a coding unit such as coding tree unit (CTU). The line buffer can store motion information of a line of coding blocks located at a boundary of the CTU, while the local buffers can be utilized for storing motion information of coding blocks located anywhere in the CTU. The utilization of the line buffer can be improved by limiting the number of motion vectors of a neighboring block stored in the line buffer and obtaining remaining motion vectors from an additional block when available. The utilization of local buffers can be improved by restricting a size of neighboring blocks whose motion information is stored in the local buffers.

A method for re-localization of the robot may include retrieving, for each of keyframes in a keyframe database of the robot, image features and a pose of the keyframe, the image features of the keyframe comprising a global descriptor and local descriptors of the keyframe (210); extracting image features of a current frame captured by the robot, the image features of the current frame comprising a global descriptor and local descriptors of the current frame (220); determining one or more rough matching frames from the keyframes based on comparison between the global descriptor of each keyframe and the global descriptor of the current frame (230); determining a final matching frame from the one or more rough matching frames based on comparison between the local descriptors of each rough matching frame and the local descriptors of the current frame (240); and calculating a pose of the current frame based on a pose of the current frame based on a pose of the final matching frame (250).

The present embodiments, discloses a system and method for generating an aesthetic appliance including a clinical site having a plurality of imaging devices to capture digital imagery of a patient. A computing device receives the digital imagery and transmits the imagery, via a network, to an aesthetic appliance production site. The aesthetic appliance production site includes an aesthetic appliance production device configured to produce the aesthetic appliance.

Systems and methods of the present disclosure facilitate rigid point cloud registration with characteristics including shape constraint, translation proportional to distance and spatial point-set distribution model for handling scale. The method of the present disclosure enables registration of a rigid template point cloud to a given reference point cloud. Shape-constrained gravitation, as induced by the reference point cloud, controls movement of the template point cloud such that at each iteration, the template point cloud better aligns with the reference point cloud in terms of shape. This enables alignment in difficult conditions introduced by change such as presence of outliers and/or missing parts, translation, rotation and scaling. Also, systems and methods of the present disclosure provide an automated method as against conventional methods that depended on manually adjusted parameters.

A panorama image alignment method method comprises: obtaining multipath spherical images; calculating rotation Euler angles between each spherical image and a middle portion, a left portion and a right portion of an adjacent spherical image according to a middle portion, a left portion and a right portion of each spherical image to obtain a first left portion rotation matrix and a second right portion rotation matrix; obtaining a first left panorama image, a first right panorama image, a second left panorama image and a second right panorama image; aligning the second left panorama image to the first left panorama image, obtaining a second left portion rotation matrix by means of calculation, and then obtaining a rotation matrix of a left panorama; aligning the second right panorama image to the first right panorama image, obtaining a second right portion rotation matrix by means of calculation, and obtaining a rotation matrix of a right panorama.

Provided is a method for reconstructing content image data. The method includes selecting a first point and a second point in a first image of first content, selecting a third point and a fourth point in a second image of second content (the second image is an image corresponding to the first image and the third point and the fourth point are points in an image corresponding to the first point and the second point, respectively), generating a first reference vector using the first point and the second point, generating a second reference vector using the third point and the fourth point, calculating a rotation, scale, and transformation (RST) value from the first image to the second image using the first reference vector and the second reference vector; and reconstructing the second content using the calculated RST value.

A computing device is provided having at least one processor (104) operative to facilitate motion correction in a medical image file (102). The at least one processor (104) is configured to generate at least one unified frame file (110) based on motion image data (204), depth map data (206) corresponding to the motion image data, and region of interest data (200). Further, at least one corrected image file derived from the medical image file (102) is generated by performing the motion correction based on the at least one unified frame file (110) using the processor (104). Subsequently, the at least one corrected image file is outputted for display to one or more display devices (122).