An application processor includes a reconfigurable hardware scaler which includes dedicated circuits configured to perform different scaling techniques, respectively and a shared circuit configured to be shared by the dedicated circuits. One of the different scaling techniques is performed by one of the dedicated circuits and the shared circuit.

A graphics processing unit (GPU) processes graphics data using a rendering space which is sub-divided into a plurality of tiles. The GPU comprises cost indication logic configured to obtain a cost indication for each of a plurality of sets of one or more tiles of the rendering space. The cost indication for a set of tile(s) is suggestive of a cost of processing the set of one or more tiles. The GPU controls a rendering complexity with which primitives are rendered in tiles based on the cost indication for those tiles. This allows tiles to be rendered in a manner that is suitable based on the complexity of the graphics data within the tiles. In turn, this allows the rendering to satisfy constraints such as timing constraints even when the complexity of different tiles may vary significantly within an image.

Disclosed are apparatuses, systems, and techniques that may perform efficient deployment of machine learning for detection and classification of moving objects in streams of images. A set of machine learning models with different input sizes may be used for parallel processing of various regions of interest in multiple streams of images. Both the machine learning models as well as the inputs into these models may be selected dynamically based on a size of the regions of interest.

A sensing and computing system and method for capturing images and data regarding an object and calculating one or more parameters regarding the object using an internal, integrated CPU/GPU. The system comprises an imaging system, including a depth imaging system, color camera, and light source, that capture images of the object and sends data or signals relating to the images to the CPU/GPU, which performs calculations based on those signals/data according to pre-programmed algorithms to determine the parameters. The CPU/GPU and imaging system are contained within a protective housing. The CPU/GPU transmits information regarding the parameters, rather than raw data/signals, to one or more external devices to perform tasks in an industrial environment related to the object imaged.

Embodiments described herein provide techniques enable a graphics processor to continue processing operations during the reset of a compute unit that has experienced a hardware fault. Threads and associated context state for a faulted compute unit can be migrated to another compute unit of the graphics processor and the faulting compute unit can be reset while processing operations continue.

Embodiments relate to a multi-mode demosaicing circuit able to receive and demosaic image data in a different raw image formats, such as Bayer raw image format and Quad Bayer raw image format. The multi-mode demosaicing circuit comprises different circuitry for demosaicing different image formats that access a shared working memory. In addition, the multi-mode demosaicing circuit shares memory with a post-processing and scaling circuit configured to perform subsequent post-processing and/or scaling of the demosaiced image data, in which the operations of the post-processing and scaling circuit are modified based on the original raw image format of the demosaiced image data to use different amounts of the shared memory, to compensate for additional memory utilized by the multi-mode demosaicing circuit when demosaicing certain types of image data.

Video or graphics, received by a render engine within a graphics processing unit, may be segmented into a region of interest such as foreground and a region of less interest such as background. In other embodiments, an object of interest may be segmented from the rest of the depiction in a case of a video game or graphics processing workload. Each of the segmented portions of a frame may themselves make up a separate surface which is sent separately from the render engine to the display engine of a graphics processing unit. In one embodiment, the display engine combines the two surfaces and sends them over a display link to a display panel. The display controller in the display panel displays the combined frame. The combined frame is stored in a buffer and refreshed periodically. In accordance with another embodiment, video or graphics may be segmented by a render engine into regions of interest or objects of interest and objects not of interest and again each of the separate regions or objects may be transferred to the display engine as a separate surface. Then the display engine may transfer the separate surfaces to a display controller of a display panel over a display link. At the display panel, a separate frame buffer may be used for each of the separate surfaces.

Methods, systems, and articles of manufacture to improve image recognition searching are disclosed. In some embodiments, a first document image of a known object is used to generate one or more other document images of the same object by applying one or more techniques for synthetically generating images. The synthetically generated images correspond to different variations in conditions under which a potential query image might be captured. Extracted features from an initial image of a known object and features extracted from the one or more synthetically generated images are stored, along with their locations, as part of a common model of the known object. In other embodiments, image recognition search effectiveness is improved by transforming the location of features of multiple images of a same known object into a common coordinate system. This can enhance the accuracy of certain aspects of existing image search/recognition techniques including, for example, geometric verification.

A system for displaying medical imaging data comprising one or more data inputs, one or more processors, and one or more displays, wherein the one or more data inputs are configured for receiving first image data generated by a first medical imaging device, wherein the first image data comprises a field of view (FOV) portion and a non-FOV portion, and the one or more processors are configured for identifying the non-FOV portion of the first image data and generating cropped first image data by removing at least a portion of the non-FOV portion of the first image data, and transmitting the cropped first image data for display in a first portion of the display and additional information for display in a second portion of the display.

A system and method allows a light colored image of an object such as a check to be detected and captured on a light colored background for uploading to a server for processing. Detection involves detecting edges of objects on the image, drawing a rectangle around the detected edges, testing for an aspect ratio of the rectangle within an approved range, testing for the rectangle being outside of a margin of the image and being a certain percentage of the image, and testing for blur within a tolerable range.