Qualcomm Incorporated

Hasib Siddiqui

Kalin Mitkov Atanassov

James Wilson Nash

Albrecht Johannes Lindner

Sergiu Radu Goma

Stephen Michael Verrall

Magdi Mohamed

Erik Visser

Lae-Hoon Kim

Evgeni Petrovich Gousev

Russell Wayne Gruhlke

Xiaoyun Jiang

Jessica Liu Strohmann

Kwokleung Chan

Hau Hwang

Sandeep Louis D'Souza

Raj Kumar

Khurshid Syed Alam

Vikas Ramachandra

John Michael Wyrwas

Nathan Felix Altman

Javier Frydman

Bence Major

Robert Mitchell Kleiman

Deepak Rajendra Karnik

Haiyin Wang

Xianbiao Shu

Adi Hendel

Advait Prasad Koparkar

Some methods may involve receiving fingerprint image data and a first set of background image data from a fingerprint sensor and determining first processed fingerprint image data via a subtraction of the first set of background image data from the fingerprint image data. Some methods may involve obtaining force data corresponding to a force applied to the fingerprint sensor when the fingerprint image data were obtained. Some methods may involve obtaining a second set of background image data corresponding to the force data. Some methods may involve determining second processed fingerprint image data based, at least in part, on the first processed fingerprint image data and the second set of background image data, and outputting the second processed fingerprint image data. In some examples, determining the second processed fingerprint image data may involve a machine learning model. Some examples may involve estimating residual noise based on the force data.

Image compensation for fingerprint sensor deployed in a flexible device

Some disclosed methods involve obtaining current A-line data corresponding to reflections of ultrasonic waves from a target object detected by a single receiver pixel, obtaining current ultrasonic fingerprint image data corresponding to reflections of ultrasonic waves from a target object surface, obtaining previously-obtained A-line data that was previously obtained from an authorized user, and obtaining previously-obtained ultrasonic fingerprint image data that was previously obtained from the authorized user. Some disclosed methods involve estimating, based at least in part on the current A-line data, the previously-obtained A-line data, the current ultrasonic fingerprint image data and the previously-obtained ultrasonic fingerprint image data, whether the target object is a finger of the authorized user. The estimation may involve an anti-spoofing process based at least in part on the current A-line data and the previously-obtained A-line data.

Ultrasonic fingerprint anti-spoofing based on A-line data

Aspects relate to processing captured images from structured light systems. An example device may include one or more processors and a memory. The memory may include instructions that, when executed by the one or more processors, cause the device to receive a captured image of a scene from a structured light receiver, analyze one or more first portions of the captured image at a first scale, and analyze one or more second portions of the captured image at a second scale finer than the first scale. The analysis of the one or more second portions may be based on the analysis of the one or more first portions. The instructions further may cause the device to determine for each of the one or more second portions a codeword from a codeword distribution and determine one or more depths in the scene based on the one or more determined codewords.

Multiple scale processing for received structured light

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

Aspects of the present disclosure relate to systems and methods for determining a resampler for resampling or converting non-Bayer patter color filter array image data to Bayer pattern image data. An example device may include a camera having an image sensor with a non-Bayer pattern color filter array configured to capture non-Bayer pattern image data for an image. The example device also may include a memory and a processor coupled to the memory. The processor may be configured to receive the non-Bayer pattern image data from the image sensor, divide the non-Bayer pattern image data into portions, determine a sampling filter corresponding to the portions, and determine, based on the determined sampling filter, a resampler for converting non-Bayer pattern image data to Bayer-pattern image data.

Systems and methods for converting non-Bayer pattern color filter array image data

Systems and methods for reconstructing an object boundary in a disparity map generated by a structured light system are disclosed. One aspect is a structured light system. The system includes an image projecting device configured to project codewords. The system further includes a receiver device including a sensor, the receiver device configured to sense the projected codewords reflected from an object. The system further includes a processing circuit configured to generate a disparity map of the object, detect a first boundary of the object in the disparity map, identify a shadow region in the disparity map adjoining the first boundary, the shadow region including pixels with codeword outages, and change a shape of the object in the disparity map based on the detected shadow region. The system further includes a memory device configured to store the disparity map.

Object reconstruction in disparity maps using displaced shadow outlines

A device includes a first camera and a processor configured to detect one or more first keypoints within a first image captured by the first camera at a first time, detect one or more second keypoints within a second image captured by a second camera at the first time, and detect the one or more first keypoints within a third image captured by the first camera at a second time. The processor is configured to determine a pose estimation based on coordinates of the one or more first keypoints of the first image relative to a common coordinate system, coordinates of the one or more second keypoints of the second image relative to the common coordinate system, and coordinates of the one or more first keypoints of the third image relative to the common coordinate system. The first coordinate system is different than the common coordinate system.

Pose estimation using multiple cameras

Systems and method for refining a depth map of a scene based upon a captured image of the scene. A captured depth map of the scene may contain outage areas or other areas of low confidence. The depth map may be aligned with a color image of the scene, and the depth values of the depth map may be adjusted based upon corresponding color values of the color image. An amount of refinement for each depth value of the aligned depth map is based upon the confidence value of the depth value and a smoothing function based upon a corresponding location of the depth value on the color image.

Refinement of structured light depth maps using RGB color data

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

Exemplary embodiments are directed to configurable demodulation of image data produced by an image sensor. In some aspects, a method includes receiving information indicating a configuration of the image sensor. In some aspects, the information may indicate a configuration of sensor elements and/or corresponding color filters for the sensor elements. A modulation function may then be generated based on the information. In some aspects, the method also includes demodulating the image data based on the generated modulation function to determine chrominance and luminance components of the image data, and generating the second image based on the determined chrominance and luminance components.

Systems and methods for configurable demodulation

An electronic device for selecting a transform is described. The electronic device includes at least one image sensor, a memory, and a processor coupled to the memory and to the at least one image sensor. The processor is configured to obtain at least two images from the at least one image sensor. The processor is also configured to characterize structural content of each of the at least two images to produce a characterization for each image that is relevant to transform performance. The processor is further configured to select at least one transform from a set of transforms based on the characterization. The processor is additionally configured to apply the at least one transform to at least one of the images to substantially align the at least two images.

Systems and methods for selecting an image transform

Aspects relate to a method of generating a high-resolution image containing depth information of an object. In one aspect, the method includes downsampling a first reference image and a second reference image from a first resolution to a second resolution, wherein the first resolution is higher than the second resolution, and wherein the first reference image and the second reference image comprising a stereo image pair. The method further includes generating a depth map at the second resolution based on global minimization techniques, using the downsampled stereo image pair. The method also includes upsampling the depth map from the second resolution to the first resolution and using a guided filter to align contours of the upsampled depth map to contours of the first reference image.

Systems and methods for stereo depth estimation using global minimization and depth interpolation

Certain aspects relate to systems and techniques for performing local intensity equalization on images in a set of images exhibiting local intensity variations. For example, the local intensity equalization can be used to perform accurate region matching and alignment of the images. The images can be partitioned into regions of pixel blocks, for instance based on location, shape, and size of identified keypoints in the images. Regions depicting the same feature in the images can be equalized with respect to intensity. Region matching based on the keypoints in the intensity-equalized regions can be performed with accuracy even in images captured by asymmetric sensors or exhibiting spatially varying intensity.

Local adaptive histogram equalization

An interactive display, including a cover glass having a front surface that includes a viewing area provides an input/output (I/O) interface for a user of an electronic device. An arrangement includes a processor, a light source, and a camera disposed outside the periphery of the viewing area coplanar with or behind the cover glass. The camera receives scattered light resulting from interaction, with an object, of light outputted from the interactive display, the outputted light being received by the cover glass from the object and directed toward the camera. The processor determines, from image data output by the camera, an azimuthal angle of the object with respect to an optical axis of the camera and/or a distance of the object from the camera.

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San Diego, CA, United States of America

Hasib Siddiqui