Sri International

Sri Inlernational

David Chao Zhang

Sek Meng Chai

Michael Raymond Piacentino

Aswin Nadamuni Raghavan

Mohamed R Amer

Gooitzen Sieman Van Der Wal

Timothy J Shields

David Berends

Peter Alan Levine

Bhaskar S Ramamurthy

Michael Anthony Isnardi

Peter Jeffrey Burt

Girish Acharya

John Robertson Tower

Douglas A Bercow

John Benjamin Southall

Thomas Lee Vogelsong

Aaron Spaulding

Erik Frank Matlin

Atsushi Homma

Noriyuki Kanehira

Tadahisa Tsuyama

Chiharu Yamaoka

Naohiko Fuse

Method and apparatus of processing a sequence of video frames comprising generating at least one video frame and using an analog neural network to select, within the at least one video frame, at least one patch of pixels and process the at least one pixel patch to produce a patch feature for each of the at least one pixel patches. The method digitizes the patch feature, identifies objects within the digitized patch feature, and tracks the objects to generate control information that is used by the analog neural network to select and process the pixel patches.

Video processor capable of in-pixel processing

Artificial neural network systems involve the receipt by a computing device of input data that defines a pattern to be recognized (such as faces, handwriting, and voices). The computing device may then decompose the input data into a first subband and a second subband, wherein the first and second subbands include different characterizing features of the pattern in the input data. The first and second subbands may then be fed into first and second neural networks being trained to recognize the pattern. Reductions in power expenditure, memory usage, and time taken, for example, allow resource-limited computing devices to perform functions they otherwise could not.

Low precision neural networks using subband decomposition

Operations of computing devices are managed using one or more deep neural networks (DNNs), which may receive, as DNN inputs, data from sensors, instructions executed by processors, and/or outputs of other DNNs. One or more DNNs, which may be generative, can be applied to the DNN inputs to generate DNN outputs based on relationships between DNN inputs. The DNNs may include DNN parameters learned using one or more computing workloads. The DNN outputs may be, for example, control signals for managing operations of computing devices, predictions for use in generating control signals, warnings indicating an acceptable state is predicted, and/or inputs to one or more neural networks. The signals enhance performance, efficiency, and/or security of one or more of the computing devices. DNNs can be dynamically trained to personalize operations by updating DNN weights or other parameters.

Systems and methods for optimizing operations of computing devices using deep neural networks

A method, apparatus and system for enhanced welding visualization include splitting incoming light from the welding environment into at least a first optical path and a second optical path having different light levels using at least one beam splitter. Images of the split light having different light levels are captured using a respective imaging sensor. The images from the respective imaging sensors are fused to create a left-eye fused image and a right-eye fused image. The left-eye fused image is displayed on a display at a location of a left eye of a user and the right-eye fused image is displayed on a display at a location of a right eye of the user to provide a parallax-free, high dynamic range, representation of the welding environment.

HDR image capture and display system for enhanced real-time welding visualization and assistance

In general, techniques are described for processing a set of high-resolution images of an integrated circuit, the images captured at different locations with respect to the integrated circuit, to automatically align and 'stitch' the set of high-resolution images into a larger composite image. For example, an imaging system as described herein may use sampled feature points distributed across different grid tiles within overlap regions for pairs of images to match feature points to inform the alignments of a pair with respect to each image in the pair. The system may in some cases further apply a bundle adjustment to iteratively align and refine the alignment results for each image in a set of images being processed. In some examples, the bundle adjustment is a best-fit adjustment based on minimizing the net error associated with the alignment of the set of images.

Integrated circuit image alignment and stitching

Device logic in a mobile device configures a processor to capture a series of images, such as a video, using a consumer-grade camera, and to analyze the images to determine the best-focused image, of the series of images, that captures a region of interest. The images may be of a textured surface, such as facial skin of a mobile device user. The processor sets a focal length of the camera to a fixed position for collecting the images. The processor may guide the user to position the mobile device for capturing the images, using audible cues. For each image, the processor crops the image to the region of interest, extracts luminance information, and determines one or more energy levels of the luminance via a Laplacian pyramid. The energy levels may be filtered, and then are compared to energy levels of the other images to determine the best-focused image.

Selecting optimal image from mobile device captures

A method and apparatus for optimizing image quality based on scene content comprising a sensor for generating a sequence of frames where each frame in the sequence of frames comprises content representing a scene and a digital processor, coupled to the sensor, for performing scene content analysis and for establishing a window defining a number of input frames from the sensor and processed output frames, and for aligning and combining the number of frames in the window to form an output frame, wherein sensor parameters and frame combination parameters are adjusted based on scene content.

Method and apparatus for optimizing image quality based on scene content

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Belle Mead, NJ, United States of America

David Chao Zhang