Qualcomm Incorporated

Magdi Mohamed

Yingyong Qi

Kalin Mitkov Atanassov

Michel Adib Sarkis

Xin Zhong

Hasib Siddiqui

Yang Yang

Ananthapadmanabhan Arasanipalai Kandhadai

James Wilson Nash

Shuxue Quan

Mohamed Ahmed

Binglong Xie

A device (e.g., an image sensor, camera, etc.) may identify a camera lens and color filter array (CFA) sensor used to capture an image, and may determine filter parameters (e.g., a convolutional operator) based on the identified camera lens and CFA sensor. For example, a set of kernels (e.g., including a set of horizontal filters and a set of vertical filters) may be determined based on properties of a given lens and/or q-channel CFA sensor. Each kernel or filter may correspond to a row of a convolutional operator (e.g., of a restoration bit matrix) used by an image signal processor (ISP) of the device for non-linear filtering of the captured image. The corresponding outputs from the horizontal and vertical filters (e.g., two outputs of the horizontal and vertical filters corresponding to an input channel associated with the CFA sensor) may then be combined using a non-linear classification operation.

Hardware-friendly model-based filtering system for image restoration

Systems and methods are disclosed for processing spectral imaging (SI) data. A training operation estimates reconstruction matrices based on a spectral mosaic of an SI sensor, generates directionally interpolated maximum a-priori (MAP) estimations of image data based on the estimated reconstruction matrices. The training operation may determine filter coefficients for each of a number of cross-band interpolation filters based at least in part on the MAP estimations, and may determine edge classification factors based at least in part on the determined filter coefficients. The training operation may configure a cross-band interpolation circuit based at least in part on the determined filter coefficients and the determined edge classification factors. The configured cross-band interpolation circuit captures mosaic data using the SI sensor, and recovers full-resolution spectral data from the captured mosaic data.

Directional interpolation and cross-band filtering for hyperspectral imaging

A method for obtaining structural information from a digital image by an electronic device is described. The method includes obtaining a digital image. The method also includes determining a gradient vector for each pixel in a region of interest of the digital image. The method further includes transforming each pixel in the region of interest in accordance with a transform. Transforming each pixel includes determining, for each pixel, a first set of pixels. The first set of pixels includes any pixel along a line that is collinear with or perpendicular to the gradient vector and passes through a pixel location. Transforming each pixel includes incrementing with signed integer values, for each pixel, a first set of values in a transform space corresponding to any of the first set of pixels that are in a first direction of the line.

Systems and methods for obtaining structural information from a digital image

Aspects of the disclosure are related to a touch controller for use in a device having a processor and a touch sensor panel, the touch controller being coupled to the processor and the touch sensor panel, and further comprising: an analog front-end (AFE), wherein the AFE is configured to generate raw touch image data based on electrical signals generated by the touch sensor panel in response to one or more detected touches thereto; a coarse processing element configured to, in response to the processor being set to a sleep mode, coarsely process the raw touch image data to generate sparse data; and an embedded memory configured to store at least the sparse data, wherein the touch controller is configured to transmit a signal to the processor to wake the processor up and transmit the stored sparse data and new touch image data to the processor, wherein the processor performs gesture recognition based on the sparse data and the new touch image data.

Cascaded touch to wake for split architecture

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.

Systems and methods for tracking an object

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.

Automated graph-based programming

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.

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San Diego, CA, United States of America

Magdi Mohamed