Qualcomm Incorporated

Venkata Ravi Kiran Dayana

Wesley James Holland

Karthikeyan Shanmugavadivelu

Micha Galor Gluskin

Narayana Karthik Sadanandam Ravirala

Shizhong Liu

Upal Mahbub

Kalin Mitkov Atanassov

James Wilson Nash

Jiafu Luo

Ying Chen Lou

Adarsh Abhay Golikeri

Hung-Hsin Wu

Chung-Chi Tsai

Meng-Lin Wu

Scott Barker

An M Chen

Sergiu Radu Goma

Xiaoyun Jiang

Hau Hwang

Fatih Murat Porikli

Shang-Chih Chuang

Leung Chun Chan

Narayana Karthik Ravirala

Sanket Krishnamurthy Sagar

Rengaraj Thirupathi

Shusil Dangi

Shubhankar Mangesh Borse

Sreevatsan Madhavan

Kuang-Jui Hsu

Systems, methods, and computer-readable media are provided for low power, variable focus. An example method can include obtaining, based on a trigger, a first image of a scene captured by an image sensor, the first image being captured with a lens of the image sensor in a first configuration of a plurality of available lens configurations; determining, based on the first image and a first detection result, whether an object of interest is present in the first image; in response to determining that the object of interest is not present in the first image, adjusting the lens to a second configuration selected from the plurality of lens configurations; obtaining, by the image sensor, a second image of the scene while the lens is in the second configuration; and determining, based on the second image and a second detection result, that the object of interest is present in the second image.

Low power object detection

Methods, systems, and apparatuses for image segmentation are provided. For example, a computing device may obtain an image, and may apply a process to the image to generate input image feature data and input image segmentation data. Further, the computing device may obtain reference image feature data and reference image classification data for a plurality of reference images. The computing device may generate reference image segmentation data based on the reference image feature data, the reference image classification data, and the input image feature data. The computing device may further blend the input image segmentation data and the reference image segmentation data to generate blended image segmentation data. The computing device may store the blended image segmentation data within a data repository. In some examples, the computing device provides the blended image segmentation data for display.

Apparatus and methods for image segmentation using machine learning processes

Systems and techniques are provided for processing one or more frames. For example, a process can include obtaining a first plurality of frames associated with a first settings domain from an image capture system, wherein the first plurality of frames is captured prior to obtaining a capture input. The process can include obtaining a reference frame associated with a second settings domain from the image capture system, wherein the reference frame is captured proximate to obtaining the capture input. The process can include obtaining a second plurality of frames associated with the second settings domain from the image capture system, wherein the second plurality of frames is captured after the reference frame. The process can include, based on the reference frame, transforming at least a portion of the first plurality of frames to generate a transformed plurality of frames associated with the second settings domain.

Low-power fusion for negative shutter lag capture

An imaging system can obtain image data, for instance from an image sensor. The imaging system can supply the image data as input data to a machine learning system, which can generate one or more maps based on the image data. Each map can identify strengths at which a certain image processing function is to be applied to each pixel of the image data. Different maps can be generated for different image processing functions, such as noise reduction, sharpening, or color saturation. The imaging system can generate a modified image based on the image data and the one or more maps, for instance by applying each of one or more image processing functions in accordance with each of the one or more maps. The imaging system can supply the image data and the one or more maps to a second machine learning system to generate the modified image.

Machine learning based image adjustment

Systems, methods, and non-transitory media are provided for predictive camera initialization. An example method can include obtain, from a first image capture device, image data depicting a scene; classify the scene based on the image data; based on the classification of the scene, predict a camera use event; and based on the predicted camera use event, adjust a power mode of at least one of the first image capture device and a second image capture device.

Camera initialization for reduced latency

Systems and techniques are described for imaging. An imaging system includes an image sensor with a plurality of photodetectors, grouped into a first group of photodetectors and a second group of photodetectors. The imaging system can reset its image sensor. The imaging system exposes its image sensor to light from a scene. The plurality of photodetectors convert the light into charge. The imaging system stores analog photodetector signals corresponding to the charge from each the photodetectors. The imaging system reads first digital pixel data from a first subset of the analog photodetector signals corresponding to the first group of photodetectors without reading second digital pixel data from a second subset of the analog photodetector signals corresponding to the second group of photodetectors. The imaging system generates an image of the scene using the first digital pixel data.

Systems and methods for controlling an image sensor

Systems and techniques are described for processing image data to generate an image with a synthetic depth of field (DoF). An imaging system receives first image data of a scene captured by a first image sensor. The imaging system receives second image data of the scene captured by a second image sensor. The first image sensor is offset from the second image sensor by an offset distance. The imaging system generates, using at least the first image data and the second image data as inputs to one or more trained machine learning systems, an image having a synthetic depth of field corresponding to a simulated aperture size. The simulated aperture size is associated with the offset distance. The imaging system outputs the image.

Systems and methods for generating synthetic depth of field effects

Systems, methods, and computer-readable media are provided for foreground image segmentation. In some examples, a method can include obtaining a first image of a target and a second image of the target, the first image having a first field-of-view (FOV) and the second image having a second FOV; determining, based on the first image, a first segmentation map that identifies a first estimated foreground region in the first image; determining, based on the second image, a second segmentation map that identifies a second estimated foreground region in the second image; generating a third segmentation map based on the first segmentation map and the second segmentation map; and generating, using the second segmentation map and the third segmentation map, a refined segmentation mask that identifies the target as a foreground region of the first and/or second image.

Segmentation for image effects

Methods and apparatus for determining a depth of an object within a scene are provided. Image data of a scene can be captured using a lens configured to project an image of the scene onto an image sensor. The lens has a known focal length and is movable between at least a first lens position and a second lens position. A first image of the scene is captured with the lens at a first lens position, and a second image of the scene is captured with the lens at a second, different position. By measuring a first dimension of the object using the first image and a second dimension of the object using the second image, a depth of the object may be determined based upon a ratio of the first and second dimensions, the focal length of the lens, and a distance between the first and second lens positions.

Method and system for depth estimation based upon object magnification

Devices and methods for providing seamless preview images for multi-camera devices having two or more asymmetric cameras. A multi-camera device may include two asymmetric cameras disposed to image a target scene. The multi-camera device further includes a processor coupled to a memory component and a display, the processor configured to retrieve an image generated by a first camera from the memory component, retrieve an image generated by a second camera from the memory component, receive input corresponding to a preview zoom level, retrieve spatial transform information and photometric transform information from memory, modify at least one image received from the first and second cameras by the spatial transform and the photometric transform, and provide on the display a preview image comprising at least a portion of the at least one modified image and a portion of either the first image or the second image based on the preview zoom level.

Systems and methods for implementing seamless zoom function using multiple cameras

A method of performing an image autofocus operation using multiple cameras includes performing, at an image processor, a first autofocus operation on a first region of interest in a scene captured by a first camera and determining a second region of interest in the scene captured by a second camera. The second region of interest is determined based on the first region of interest. The method further includes performing a second autofocus operation on the second region of interest. The method also includes fusing a first image of the scene captured by the first camera with a second image of the scene captured by the second camera to generate a fused image. The first image is based on the first autofocus operation and the second image is based on the second autofocus operation.

Multiple camera autofocus synchronization

Method and systems for autofocus triggering focusing are disclosed herein. In one example, a system may include a lens, a memory component configured to store lens parameters of the lens and regions of focus corresponding to the lens parameters, and a processor coupled to the memory and the lens. The processor may be configured to focus the lens on a target object at a first instance of time, receive information indicative of distances from an imaging device to the target object over a period of time, obtain lens parameters of the lens, and determine a region of focus, and trigger the lens to re-focus on the target object if the distance to the target object indicates the target object is outside of the region of focus and the distance to the target object is unchanged for a designated time period.

Systems and methods for autofocus trigger

Devices and methods for capturing images are described herein. A processor may be configured to capture a first image. The processor may further be configured to determine a distance between a lens and an image sensor of the image capture device for capturing the first image. The processor may further be configured to determine a portion of the image sensor to use for the first image based on the determined distance in order to maintain a constant field of view for one or more images captured by the image capture device. The processor may further be configured to adjust the portion of the image sensor used for the first image based on the determined portion of the image sensor to generate an adjusted first image.

Constant field of view for image capture

Systems, methods, and devices for power optimization in imaging devices having dual cameras are contained herein. In one aspect, a method for power optimization for a dual camera imaging device is disclosed. The method includes determining a zoom factor selection, determining whether the zoom factor selection falls within a first zoom factor range, a second zoom factor range, or a third zoom factor range, and sending a series of frames of an image captured by a first sensor or a series of frames of an image captured by a second sensor or both to a camera application based on the determined zoom factor section.

Growing community of inventors

San Diego, CA, United States of America

Venkata Ravi Kiran Dayana