Abstract: Methods and systems for implementing a traditional computer vision algorithm as a neural network. The method includes: receiving a definition of the traditional computer vision algorithm that identifies a sequence of one or more traditional computer vision algorithm operations; mapping each of the one or more traditional computer vision algorithm operations to a set of one or more neural network primitives that is mathematically equivalent to that traditional computer vision algorithm operation; linking the one or more network primitives mapped to each traditional computer vision algorithm operation according to the sequence to form a neural network representing the traditional computer vision algorithm; and configuring hardware logic capable of implementing a neural network to implement the neural network that represents the traditional computer vision algorithm.

Abstract: A method is provided. The method includes receiving a request for a frame buffer from an application; identifying a next frame generation duration based on history information of frame timings; identifying a next frame generation start time based on a sync time corresponding to a next vertical sync (VSYNC) signal and the next frame generation duration; and providing the frame buffer to the application at the next frame generation start time.

Abstract: Methods and systems for implementing a traditional computer vision algorithm as a neural network. The method includes: receiving a definition of the traditional computer vision algorithm that identifies a sequence of one or more traditional computer vision algorithm operations; mapping each of the one or more traditional computer vision algorithm operations to a set of one or more neural network primitives that is mathematically equivalent to that traditional computer vision algorithm operation; linking the one or more network primitives mapped to each traditional computer vision algorithm operation according to the sequence to form a neural network representing the traditional computer vision algorithm; and configuring hardware logic capable of implementing a neural network to implement the neural network that represents the traditional computer vision algorithm.

Abstract: A method and system for processing an image is described. For example, the method comprises detecting a plurality of objects within an in input image; identifying dimensions of a display on which the input image is to be displayed; cropping the input image to obtain a cropped image which matches the identified dimensions, wherein the cropped image includes at least one of the plurality of detected objects; obtaining a list of missing objects which are not visible in the cropped image and which were detected in the input image; outputting a representation of each missing object in the list of missing objects to be displayed together with the cropped image; generating an updated image comprising the representation of at least one missing object and which matches the identified dimensions; and outputting the updated image to be displayed on the display.

Abstract: A method of image retargeting is provided. The method includes obtaining a source image, obtaining a target size for a retargeted image based on the source image, generating a two-dimensional importance map for the source image, generating, based on the two-dimensional importance map and the target size, a warping mesh having a distortion metric below a threshold value, determining whether a size of the warping mesh corresponds to the target size, and based on the size of the warping mesh being determined to correspond to the target size, rendering the retargeted image by applying the warping mesh to the source image.

Abstract: A method is provided.

Abstract: Methods and systems for implementing a traditional computer vision algorithm as a neural network. The method includes: receiving a definition of the traditional computer vision algorithm that identifies a sequence of one or more traditional computer vision algorithm operations; mapping each of the one or more traditional computer vision algorithm operations to a set of one or more neural network primitives that is mathematically equivalent to that traditional computer vision algorithm operation; linking the one or more network primitives mapped to each traditional computer vision algorithm operation according to the sequence to form a neural network representing the traditional computer vision algorithm; and configuring hardware logic capable of implementing a neural network to implement the neural network that represents the traditional computer vision algorithm.