Examples described herein generally relate to systems and method for streaming a video game at a client device. The client device may transmit video game controls to a streaming server. The client device may receive a video stream encoding video images generated in response to the video game controls from the streaming server. The client device may determine that a video image of the video stream to display in a frame has not been completely received at a designated time prior to display of the frame. The client device may determine an image transformation based on a history of the video images and motion vectors for the video stream. The client device may apply the image transformation to a portion of one or more images corresponding to previous frames. The client device may display a substitute video image in the frame including the portion of the transformed image.

Examples described herein generally relate to systems and method for streaming a video game at a client device. The client device may transmit video game controls to a streaming server. The client device may receive a video stream encoding video images generated in response to the video game controls from the streaming server. The client device may determine that a video image of the video stream to display in a frame has not been completely received at a designated time prior to display of the frame. The client device may determine an image transformation based on a history of the video images and motion vectors for the video stream. The client device may apply the image transformation to a portion of one or more images corresponding to previous frames. The client device may display a substitute video image in the frame including the portion of the transformed image.

Techniques are described for identifying pixel locations using a transformation function. A transformation function is identified based on the projection space of a 2D representation, and pixel locations are generated using the transformation function.

The presently disclosed technology uses predefined handwriting characteristics to create and/or refine a stylus position interpolation function over time to provide more accurate and adaptive renderings of the user's handwriting on a touch screen. As the presently disclosed technology is performed on a specific device and uses data collected from one or more specific users, it adapts the stylus position interpolation function for any device-specific or user-specific variations. Further, as the stylus position interpolation function adapts iteratively over time, it may not converge and continues to adapt as the device ages, the user's habits change, or identify of the user changes, and environmental conditions of the device change.

Feature descriptor matching described herein may include receiving a first input image and a second input image. A feature detector may detect features from the first and second input images. A descriptor extractor may learn local feature descriptors from the features of the first and second input images based on a feature descriptor matching model trained using a ground truth data set. The descriptor extractor may determine a listwise mean average precision (mAP) rank of a pool of candidate image patches from the second input image with respect to a queried image patch from the first input image based on the feature descriptor matching model, the first set of local feature descriptors, and the second set of local feature descriptors. The descriptor matcher may generate a geometric transformation between the first input image and the second input image based on the listwise mAP and a convolutional neural network.

An apparatus comprises processing circuitry configured to: obtain medical imaging data that is representative of an anatomical region of a subject, the anatomical region comprising at least one anatomical feature of interest; obtain a template that is representative of a desired view of the at least one anatomical feature of interest; register the medical imaging data and reference anatomical data to obtain a distribution of registration probability with respect to at least one registration parameter; and perform a selection process comprising: obtaining a plurality of transforms, each having an associated registration; for each of the plurality of transforms: generating a respective view of the at least one anatomical feature of interest based on said transform; and determining a template matching probability that represents a similarity of the generated view to the template; and selecting at least one of the transforms and/or at least one of the views based on a combination of at least one of the registration probabilities with at least one of the template matching probabilities.

An image processing method is provided. The method is configured to process the color-block image output by the image sensor. The face region of the color-block image is determined. A part of the color-block image beyond the face region is converted into a first simulation image using a first interpolation algorithm. A part of the color-block image within the face region is converted into a second simulation image using a second interpolation algorithm. The complexity of the second interpolation algorithm is less than that of the first interpolation algorithm. The first simulation image and the second simulation image are merged into a simulation image corresponding to the color-block image. An image processing apparatus and an electronic device are provided. With the image processing method, the image processing apparatus and the electronic device, distinguishability and resolution of the image are improved, and time required for processing the image is reduced.

A synthesis method of Chinese printed character images and a device thereof are disclosed, which belong to the field of optical character recognition. The method includes performing at least one transformation on a standard character image to respectively generate at least one extended character image, and respectively merge the at least one extended character image with a background template to generate at least one synthesized character image. In embodiments of the present invention, a standard character image is transformed to generate an extended character image, thereby extending a Chinese printed character becomes much easier and faster; an extended character image and a background template are merged to generate a synthesized character image, thereby obtaining Chinese printed character image becomes much easier and faster.

A matching transformation is determined for matching a patient image set of images of an anatomical body structure of a patient with an atlas image set of images of a general anatomical structure including anatomical atlas elements. Atlas spatial information containing spatial information on the general anatomical structure, and element representation information are obtained. The element representation information describes representation data sets which contain information on representations of the plurality of atlas elements in the atlas images to be determined are obtained, and also describes a determination rule for determining respective representation data sets for respective atlas elements in accordance with different respective parameter sets. Patient data is acquired by acquiring the patient image set and the parameter sets which are respectively associated with the images of the patient image set. The matching transformation is determined by matching images associated with the same parameter set to each other.

Signal processing devices and methods estimate transforms between signals using a least squares technique. From a seed set of transform candidates, a direct least squares method applies a seed transform candidate to a reference signal and then measures correlation between the transformed reference signal and a suspect signal. For each candidate, update coordinates of reference signal features are identified in the suspect signal and provided as input to a least squares method to compute an update to the transform candidate. The method iterates so long as the update of the transform provides a better correlation. At the end of the process, the method identifies a transform or set of top transforms based on a further analysis of correlation, as well as other results.