Qualcomm Incorporated

Chinchuan Andrew Chiu

Xiaoyun Jiang

Shuxue Quan

Jingqiang Li

Szepo Robert Hung

Lei Ma

Ying Noyes

Lei Wang

Haohong Wang

Milivoje M Aleksic

Jon James Anderson

Moinul H Khan

Khaled Helmi El-Maleh

Hsiang-Tsun Li

Peng Wang

Hau Hwang

Meghal Varia

Serag Gadelrab

Kyle Ernewein

Tom Longo

Simon Booth

Don Le

Ying Hai Chen

Ning Bi

Kalin Mitkov Atanassov

Ruben Manuel Velarde

Dashan Gao

Victor Hokkiu Chan

Chia-Yuan Teng

Jiafu Luo

Xin Zhong

Joseph Cheung

Jianfeng Ren

Xuerui Zhang

King-Chung Lai

Vivek Sahu

Songhe Cai

Scott Cheng

Rohan Desai

Shujuan Wang

Abhay Raut

Stephen Horton

Krishnam Indukuri

Rakesh Sanam

Kalin Atanssov

Dennis Liu

Ching-Hui Chen

Ying Xie Noyes

Milivoje Aleksic

An innovative passive cooling solution with sealed UAV enclosure system allows heat from a semiconductor chip to be dissipated to the ambient environment through evaporation/condensation phase-change cooling and air cooling a heat sink such as a fin without any additional power consumption to operate cooling solution. One example of such a solution may include a pipe with a fin and a fluid. The pipe may include a wick structure along an inner surface of the pipe configured to allow the fluid to travel within the wick structure and to allow a vapor form of the fluid to exit the wick structure towards a center of the pipe.

Systems, methods, and apparatus for passive cooling of UAVs

Techniques and systems are provided for processing image data. For example, an image signal processor can obtain (e.g., from a host processor) a first setting change indicator value indicating a change in parameter settings of the image signal processor. The image signal processor can obtain an image frame from an image sensor, and can determine a second setting change indicator value from the image frame. The second setting change indicator value can be provided to the image sensor from the host processor. The second setting change indicator value indicates a change in parameter settings of the image sensor. The image signal processor can compare the first setting change indicator value to the second setting change indicator value, and can determine whether to process the image frame or to drop the image frame based on comparing the first setting change indicator value to the second setting change indicator value.

Synchronization mechanism for image capture and processing systems

A method performed by an electronic device is described. The method includes receiving a set of images. The method also includes determining a motion region and a static region based on the set of images. The method further includes extracting, at a first rate, first features from the motion region. The method additionally includes extracting, at a second rate that is different from the first rate, second features from the static region. The method also includes caching the second features. The method further includes detecting at least one object based on at least a portion of the first features.

Systems and methods for object detection

An exemplary method for intelligent compression defines a threshold value for a temperature reading generated by a temperature sensor. Data blocks received into the compression module are compressed according to either a first mode or a second mode, the selection of which is determined based on a comparison of the active level for the temperature reading to the defined threshold value. The first compression mode may be associated with a lossless compression algorithm while the second compression mode is associated with a lossy compression algorithm. Or, both the first compression mode and the second compression mode may be associated with a lossless compression algorithm, however, for the first compression mode the received data blocks are produced at a default high quality level setting while for the second compression mode the received data blocks are produced at a reduced quality level setting.

System and method for intelligent data/frame compression in a system on a chip

An exemplary method for intelligent compression defines a threshold value for a key performance indicator. Based on the key performance indicator value, data blocks generated by a producer component may be scaled down to reduce power and/or bandwidth consumption when being compressed according to a lossless compression module. The compressed data blocks are then stored in a memory component along with metadata that signals the scaling factor used prior to compression. Consumer components later retrieving the compressed data blocks from the memory component may decompress the data blocks and upscale, if required, based on the scaling factor signaled by the metadata.

To determine real-world information about objects moving in a scene, the camera capturing the scene is typically calibrated to the scene. Automatic scene calibration can be accomplished using people that are found moving about in the scene. During a calibration period, a video content analysis system processing video frames from a camera can identify blobs that are associated with people. Using an estimated height of a typical person, the video content analysis system can use the location of the person's head and feet to determine a mapping between the person's location in the 2-D video frame and the person's location in the 3-D real world. This mapping can be used to determine a cost for estimated extrinsic parameters for the camera. Using a hierarchical global estimation mechanism, the video content analysis system can determine the estimated extrinsic parameters with the lowest cost.

Automatic scene calibration method for video analytics

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

Arrangements described herein relate to apparatuses, systems, and methods for a housing of an unmanned aerial vehicle (UAV), the housing includes but is not limited to a metallic porous material having a shape of an enclosure of the UAV, and a phase change material (PCM) provided in at least a portion of the metallic porous material. The metallic porous material and the PCM are configured to passively cool the UAV.

Enclosure cooling for thermal management of unmanned aerial vehicles

Embodiments include a method of image processing including decomposing a reflectance spectrum for a test surface into a linear combination of reflectance spectra of a set of test targets. The coefficient vector calculated in this decomposition is used to predict a response of an imaging sensor to the test surface. A plurality of such predicted responses may be used for various applications involving color detection and/or classification, including human skin tone detection.

Systems, methods, and apparatus for image processing, for color classification, and for skin color detection

Systems, methods and apparatus for image processing by classifying pixels are described. In some systems, an exposure control operation is performed according to the pixel classifications. In some cases, the pixels are classified according to a predetermined segmentation of a color space, based on predicted sensor responses.

Systems, methods, and apparatus for exposure control

To combine two or more images, an image capture device may compute motion vectors for a plurality of blocks of pixels of one of the images. The image capture device may also interpolate or extrapolate motion vectors for individual pixels or sub-blocks of pixels using the block motion vectors. The image capture device may then average the first and second images by averaging each of the pixels of the first image with pixels of the second image that correspond to a location indicated by the plurality of motion vectors. These techniques may reduce blur in image information resulting from certain movements during image capture or the use of certain image capture technologies.

Block-based image stabilization

This disclosure describes barcode scanning techniques for an image capture device. The image capture device may automatically detect a barcode within an image while the image capture device is operating in a non-barcode image capture mode, such a default image capture mode. In one aspect, the detection of the barcode within the image may be based on a combination of identified edges and low intensity regions within the image. The image capture device may configure, based on the detection of the barcode, one or more image capture properties associated with the image capture device to improve a quality at which the images are captured. The image capture device captures the image in accordance with the configured image capture properties. The techniques may effectively provide a universal and integrated front-end for producing improved quality images of barcodes without requiring significant interaction with a user via a complicated user interface.

Image capture device with integrated barcode scanning

The disclosure relates to techniques for calibration of an auto-focus process in an image capture device. The techniques may involve calibration of a lens actuator used to move a lens within a search range during an auto-focus process. For example, an image capture device may adjust reference positions for the search range based on lens positions selected for different focus conditions. The different focus conditions may include a far focus condition and a near focus condition. The focus conditions may be determined based on a detected environment in which the device is used. Detection of an indoor environment may indicate a likelihood of near object focus, while detection of an outdoor environment may indicate a likelihood of far object focus. An image capture device may detect indoor and outdoor environments based on lighting, exposure, or other conditions.

Auto-focus calibration for image capture device

Apparatus are provided including an image signal carrier, a luminance information evaluator, and a chrominance information modifier. The image signal carrier is encoded with an image signal including luminance information and chrominance information. The luminance information evaluator evaluates the luminance information in the image signal for a given region within the image to identify when the given region is one of substantially white and substantially dark. The chrominance information modifier is provided to modify the chrominance information corresponding to the given region when the given region is one of substantially white and substantially dark.

Chroma supression features

The disclosure describes adaptive filtering techniques to improve the quality of captured image information, such as video or still images. An image sensor captures image information and determines a plurality of parameter values based on a current exposure index and a current scaling factor of the image information. The adaptive spatial image filter includes both horizontal and vertical sharpening filters and configures, i.e., adapts, the horizontal sharpening filter and the vertical sharpening filter based on the plurality of parameter values determined by the image sensor. The adaptive spatial image filter applies the horizontal and vertical sharpening filters to at one channel of the image information to generate filtered image information.

Adaptive spatial image filter for filtering image information

Growing community of inventors

San Diego, CA, United States of America

Chinchuan Andrew Chiu