Abstract:
A method performed by an electronic device is described. The method includes determining a local motion pattern by determining a set of local motion vectors within a region of interest between a previous frame and a current frame. The method also includes determining a global motion pattern by determining a set of global motion vectors between the previous frame and the current frame. The method further includes calculating a separation metric based on the local motion pattern and the global motion pattern. The separation metric indicates a motion difference between the local motion pattern and the global motion pattern. The method additionally includes tracking an object based on the separation metric.
Abstract:
A method for tracking an object by an electronic device is described. The method includes detecting an object position in an initial frame to produce a detected object position. The method also includes measuring one or more landmark positions based on the detected object position or a predicted object position. The method further includes predicting the object position in a subsequent frame based on the one or more landmark positions. The method additionally includes determining whether object tracking is lost. The method also includes avoiding performing object detection for the subsequent frame in a case that object tracking is maintained.
Abstract:
A method for detecting and tracking a target object is described. The method includes performing motion-based tracking for a current video frame by comparing a previous video frame and the current video frame. The method also includes selectively performing object detection in the current video frame based on a tracked parameter.
Abstract:
Embodiments include methods and systems which determine pixel displacement between frames based on a respective weighting-value for each pixel or a group of pixels. The weighting-values provide an indication as to which pixels are more pertinent to optical flow computations. Computational resources and effort can be focused on pixels with higher weights, which are generally more pertinent to optical flow determinations.
Abstract:
In a particular illustrative embodiment, a method of determining a viewpoint of a person based on skin color area and face area is disclosed. The method includes receiving image data corresponding to an image captured by a camera, the image including at least one object to be displayed at a device coupled to the camera. The method further includes determining a viewpoint of the person relative to a display of the device coupled to the camera. The viewpoint of the person may be determined by determining a face area of the person based on a determined skin color area of the person and tracking a face location of the person based on the face area. One or more objects displayed at the display may be moved in response to the determined viewpoint of the person.
Abstract:
A histogram modeling based technique for image contrast enhancement. In some implementations, a histogram of an image is created and then transformed. Using the physics of sound or heat propagation, the technique may develop a spreaded histogram model that may be transformed. A nonlinear mapping may be created to remap an image for contrast enhancement. The technique may be performed without threshold tuning and may be implemented on a variety of display hardware.
Abstract:
Techniques for performing mesh-based video compression/decompression with domain transformation are described. A video encoder partitions an image into meshes of pixels, processes the meshes of pixels to obtain blocks of prediction errors, and codes the blocks of prediction errors to generate coded data for the image. The meshes may have arbitrary polygonal shapes and the blocks may have a predetermined shape, e.g., square. The video encoder may process the meshes of pixels to obtain meshes of prediction errors and may then transform the meshes of prediction errors to the blocks of prediction errors. Alternatively, the video encoder may transform the meshes of pixels to blocks of pixels and may then process the blocks of pixels to obtain the blocks of prediction errors. The video encoder may also perform mesh-based motion estimation to determine reference meshes used to generate the prediction errors.
Abstract:
A graphics pipeline includes a plurality of sequentially arranged processing stages which render display pixel data from input primitive object data. The processing stages include at least a texturing stage and a depth test stage, and the depth test stage may be located earlier in the graphics pipeline than the texturing stage.