Abstract:
An electronic device is described. The electronic device includes a processor. The processor is configured to obtain a plurality of images. The processor is also configured to obtain global motion information indicating global motion between at least two of the plurality of images. The processor is further configured to obtain object tracking information indicating motion of a tracked object between the at least two of the plurality of images. The processor is additionally configured to perform automatic zoom based on the global motion information and the object tracking information. Performing automatic zoom produces a zoom region including the tracked object. The processor is configured to determine a motion response speed for the zoom region based on a location of the tracked object within the zoom region.
Abstract:
A method includes receiving a user input (e.g., a one-touch user input), performing segmentation to generate multiple candidate regions of interest (ROIs) in response to the user input, and performing ROI fusion to generate a final ROI (e.g., for a computer vision application). In some cases, the segmentation may include motion-based segmentation, color-based segmentation, or a combination thereof. Further, in some cases, the ROI fusion may include intraframe (or spatial) ROI fusion, temporal ROI fusion, or a combination thereof.
Abstract:
A particular method includes determining, based on data received from at least one motion sensor, a movement of a mobile device from a first position to a second position. The method also includes computing a three-dimensional (3D) model of an object based on a first image of the object corresponding to a first view of the object from the first position of the mobile device, a second image of the object corresponding to a second view of the object from the second position of the mobile device, and the movement of the mobile device.
Abstract:
A method for memory utilization by an electronic device is described. The method includes transferring a first portion of a first decision tree and a second portion of a second decision tree from a first memory to a cache memory. The first portion and second portion of each decision tree are stored contiguously in the first memory. The first decision tree and second decision tree are each associated with a different feature of an object detection algorithm. The method also includes reducing cache misses by traversing the first portion of the first decision tree and the second portion of the second decision tree in the cache memory based on an order of execution of the object detection algorithm.
Abstract:
A method for obtaining structural information from a digital image by an electronic device is described. The method includes determining an iris position in a region of interest based on a gradient direction transform. Determining the iris position may include determining a first dimension position and a second dimension position corresponding to a maximum value in the transform space.
Abstract:
A method includes capturing an image of a scene that includes a diagram. The method further includes applying functional block recognition rules to image data of the image to recognize functional blocks of the diagram. The functional blocks include at least a first functional block associated with a first computer operation. The method further includes determining whether the functional blocks comply with functional block syntax rules. A functional graph is computer-generated based on the functional blocks complying with the functional block syntax rules. The functional graph corresponds to the diagram, and the functional graph includes the functional blocks.
Abstract:
A method for deformable expression detection is disclosed. For each pixel in a preprocessed image, a sign of a first directional gradient component and a sign of a second directional gradient component are combined to produce a combined sign. Each combined sign is coded into a coded value. An expression in an input image is detected based on the coded values.
Abstract:
A method includes receiving first data defining a first bounding box for a first image of a sequence of images. The first bounding box corresponds to a region of interest including a tracked object. The method also includes receiving object tracking data for a second image of the sequence of images, the object tracking data defining a second bounding box. The second bounding box corresponds to the region of interest including the tracked object in the second image. The method further includes determining a similarity metric for first pixels within the first bounding box and search pixels within each of multiple search bounding boxes. Search coordinates of each of the search bounding boxes correspond to second coordinates of the second bounding box shifted in one or more directions. The method also includes determining a modified second bounding box based on the similarity metric.
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:
A particular method includes determining, based on data received from at least one motion sensor, a movement of a mobile device from a first position to a second position. The method also includes computing a three-dimensional (3D) model of an object based on a first image of the object corresponding to a first view of the object from the first position of the mobile device, a second image of the object corresponding to a second view of the object from the second position of the mobile device, and the movement of the mobile device.