Qualcomm Incorporated

State Grid Jiangsu Electric Power Company

Nari Technology Co., Ltd.

Nanjing Chscom Electrical Technology Co., Ltd.

Jianfeng Ren

Ning Bi

Feng Guo

Shizhong Liu

Ruiduo Yang

Weiliang Liu

Xiao Lu

Jijun Yin

Lin Liu

Jianyu Luo

Qing Chen

Xueming Li

Kaiming Luo

Yunsong Yan

Haifeng Xia

Ying Hai Chen

Shuxue Quan

Haifeng Li

Xuerui Zhang

Wei Zou

Zheng Wu

Shili Xu

Jincheng Huang

Sahul Madanayakanahalli Phaniraj Venkatesh

Nikhil Uchil

Lei Wang

Yang Yang

Xinzhou Wu

Babak Forutanpour

Ananthapadmanabhan Arasanipalai Kandhadai

Lei Ma

Lei Xu

Dashan Gao

Li Zhang

Mainak Biswas

Bijan Forutanpour

Xin Zhong

Chinchuan Andrew Chiu

Gang Chen

Tao Zhang

Jian Wei

Chunlei Xu

Sichao Yang

Qing Zhou

Feng Xue

Yefeng Jiang

Tao Ma

Binglong Xie

Bijun Li

Jaimin Rohitkumar Bhise

Manish Madhukar Sirdeshmukh

Kuan-Chen Pan

Krishnankutty Kolathappilly

Hengzhi Cui

Xiangdong Chen

Disruptions in the continuity of image frames output from an image capture device due to switching from one image sensor to another image sensor of the device may be reduced or eliminated through controlled timing for switching of the image sensors according to a predefined image sensor configuration. Operation of a multi-sensor image device according to the predefined image sensor configuration may include an appropriate selection of a source for image adjustment during the zoom level transition. The predefined image sensor configuration may define transition parameters for particular zoom ranges of the image capture device.

Camera sensor changes in multi-sensor device

Artefacts in a sequence of image frames may be reduced or eliminated through modification of an input image frame to match another image frame in the sequence, such as by geometrically warping to generate a corrected image frame with a field of view matched to another frame in sequence of frames with the image frame. The warping may be performed based on a model generated from data regarding the multi-sensor device. The disparity between image frames may be modeled based on image captures from the first and second image sensor for scenes at varying depths. The model may be used to predict disparity values for captured images, and those predicted disparity values used to reduce artefacts resulting from image sensor switching. The predicted disparity values may be used in image conditions resulting in erroneous actual disparity values.

Transform matrix learning for multi-sensor image capture devices

In general, techniques are described regarding initiating a recursive blending of a plurality of frames following a camera transition. One or more processors may, subsequent to a transition from a first camera mode to a second camera mode, blend one or more pixels of a first frame with one or more pixels of a second frame to produce a first blended frame, the first frame captured via a first camera, and the second frame captured via a second camera. The one or more processors may then output the first blended frame.

Camera transition blending

This disclosure provides devices, methods, computer-readable medium, and means for spatial alignment transform. In some aspects, a device may perform operations including capturing a first image of a scene using a first sensor at a first zoom ratio, capturing a second image of the scene using a second sensor at a second zoom ratio, the first image having a different field-of-view (FOV) than the second image, determining a translation matrix based on one or more spatial misalignments between the first image and the second image, determining a confidence associated with the translation matrix, in response to the confidence being greater than a confidence threshold, determining a weighting factor based on the first zoom ratio, the second zoom ratio, and a current zoom ratio of the device, applying the weighting factor to the translation matrix, and warping the first image to the second image using the weighted translation matrix.

Spatial alignment transform without FOV loss

Provided is an emergency accurate control method and system for large-scale interruptible loads. The method includes: acquiring, by a region control master station, a sheddable load sequence table; acquiring, by the region control master station, a first to-be-shed load; performing, by the region control master station, minimum under-shedding matching layer by layer according to the first to-be-shed load, and shedding a sheddable load corresponding to control substation matching with the first to-be-shed load; and sending, by the region control master station, a second to-be-shed load to the corresponding control substation for load shedding if the second to-be-shed load exists.

Emergency accurate control method and system for large-scale interruptible loads

Provided are an accurate load shedding system, and a communication method and an access apparatus thereof. The access apparatus includes: two E1 interfaces, eight optical fiber interfaces, a CPU and an FPGA. The two E1 interfaces are respectively connected to a control apparatus A and a control apparatus B of a control substation. The eight optical fiber interfaces are respectively connected to eight control terminals. The FPGA includes eight optical fiber transceivers respectively connected to the eight optical fiber interfaces through serial interfaces, and two E1 transceivers respectively connected to the two E1 interfaces through serial interfaces. Each optical fiber transceiver includes a reset submodule. Each E1 transceiver also includes a reset submodule. The CPU is connected to the FPGA through a parallel bus.

Load shedding system, communication method and access apparatus thereof

Provided are a synchronization method, a wide area system protection apparatus, a plant station and a computer readable storage medium. The method includes: sending to a second plant station a first data frame that includes a sequence number p and a sending timestamp of the first data frame; receiving a second data frame sent by the second plant station, and recording a receiving timestamp of the second data frame, the second data frame including a sequence number q of the second data frame, a sending timestamp of the second data frame and a receiving timestamp of the first data frame, and the first data frame being adjacent to the second frame on the second plant station; calculating a time phase difference and a crystal oscillator frequency deviation between the first plant station and the second plant station; and adjusting time and a clock frequency of the first plant station.

Synchronization method and wide area system protection apparatus

A method performed by an electronic device is described. The method includes obtaining a first frame of a scene. The method also includes performing object recognition of at least one object within a first bounding region of the first frame. The method further includes performing object tracking of the at least one object within the first bounding region of the first frame. The method additionally includes determining a second bounding region of a second frame based on the object tracking. The second frame is subsequent to the first frame. The method also includes determining whether the second bounding region is valid based on a predetermined object model.

Systems and methods for object tracking

Techniques and systems are provided for generating a background picture. The background picture can be used for coding one or more pictures. For example, a method of generating a background picture includes generating a long-term background model for one or more pixels of a background picture. The long-term background model includes a statistical model for detecting long-term motion of the one or more pixels in a sequence of pictures. The method further includes generating a short-term background model for the one or more pixels of the background picture. The short-term background model detects short-term motion of the one or more pixels between two or more pictures. The method further includes determining a value for the one or more pixels of the background picture using the long-term background model and the short-term background model.

Methods and systems of generating a background picture for video coding

A method performed by an electronic device is described. The method includes obtaining a combined image. The combined image includes a combination of images captured from one or more image sensors. The method also includes obtaining depth information. The depth information is based on a distance measurement between a depth sensor and at least one object in the combined image. The method further includes adjusting a combined image visualization based on the depth information.

Systems and methods for adjusting a combined image visualization based on depth information

Techniques and systems are provided for processing video data. For example, techniques and systems are provided for performing content-adaptive blob filtering. A number of blobs generated for a video frame is determined. A size of a first blob from the blobs is determined, the first blob including pixels of at least a portion of a first foreground object in the video frame. The first blob is filtered from the plurality of blobs when the size of the first blob is less than a size threshold. The size threshold is determined based on the number of the plurality of blobs generated for the video frame.

Methods and systems of performing blob filtering in video analytics

An apparatus includes an object detector configured to receive image data of a scene viewed from the apparatus and including an object. The image data is associated with multiple scale space representations of the scene. The object detector is configured to detect the object responsive to location data and a first scale space representation of the multiple scale space representations.

Object detection using location data and scale space representations of image data

A system and method of object detection are disclosed. In a particular implementation, a method of processing an image includes receiving, at a processor, image data associated with an image of a scene. The scene includes a road region. The method further includes detecting the road region based on the image data and determining a subset of the image data. The subset excludes at least a portion of the image data corresponding to the road region. The method further includes performing an object detection operation on the subset of the image data to detect an object. The object detection operation performed on the subset of the image data is exclusive of the at least a portion of the image data corresponding to the road region.

System and method of object detection

A method of processing data includes receiving, at a computing device, data representative of an image captured by an image sensor. The method also includes determining a first scene clarity score. The method further includes determining whether the first scene clarity score satisfies a threshold, and if the first scene clarity score satisfies the threshold, determining a second scene clarity score based on second data extracted from the data.

Environmental scene condition detection

A method includes accessing, at a computing device, data descriptive of a graph representing a program. The graph includes multiple nodes representing execution steps of the program and includes multiple edges representing data transfer steps. The method also includes determining at least two heterogeneous hardware resources of the computing device that are available to execute code represented by one or more of the nodes, and determining one or more paths from a source node to a sink node based on a topology of the graph. The method further includes scheduling execution of code at the at least two heterogeneous hardware resources. The code is represented by at least one of the multiple nodes, and the execution of the code is scheduled based on the one or more paths.

Efficient execution of graph-based programs

Growing community of inventors

San Diego, CA, United States of America

Jianfeng Ren