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    Low power data generation for iris-related detection and authentication
    1.
    发明授权
    Low power data generation for iris-related detection and authentication 有权

    公开(公告)号：US10984235B2

    公开(公告)日：2021-04-20

    申请号：US15713561

    申请日：2017-09-22

    Applicant: QUALCOMM Incorporated

    Inventor： Evgeni Gousev ,  Liang Shen ,  Victor Chan ,  Edwin Chongwoo Park ,  Xiaopeng Zhang

    IPC: G06K9/00 ,  G06F3/01 ,  G06F1/3203 ,  H04W52/02 ,  H04N5/33

    Abstract: Sensing of scene-based occurrences is disclosed. In one example, a vision sensor system comprises (1) dedicated computer vision (CV) computation hardware configured to receive sensor data from at least one sensor array and capable of computing CV features using readings from multiple neighboring sensor pixels and (2) a first processing unit communicatively coupled with the dedicated CV computation hardware. The vision sensor system is configured to, in response to processing of the one or more computed CV features indicating a presence of one or more irises in a scene captured by the at least one sensor array, generate data in support of iris-related operations to be performed by a second processing unit and send the generated data to the second processing unit.

    Computer vision sensor
    3.
    发明授权
    Computer vision sensor 有权

    公开(公告)号：US10735699B1

    公开(公告)日：2020-08-04

    申请号：US16502969

    申请日：2019-07-03

    Applicant: QUALCOMM Incorporated

    Inventor： Victor Chan ,  Edwin Chongwoo Park

    IPC: H04N9/09 ,  H04N9/04 ,  H04N5/235 ,  H04N5/225

    Abstract: Embodiments described herein can address these and other issues by providing a CV image sensor that has a repeating pattern of two pairs of color-opponent color sensors and a luminance sensor to provide a high amount of functionality while maintaining relatively low power requirements. By using color opponency, embodiments can sidestep the need for processor-intensive color conversions required of other color sensors. Embodiments may optionally provide for surface area optimization, hyperspectral sensitivity, low-light optimization, neuromorphic light sensing, and more.

    Low complexity auto-exposure control for computer vision and imaging systems
    4.
    发明授权
    Low complexity auto-exposure control for computer vision and imaging systems 有权

    公开(公告)号：US10375317B2

    公开(公告)日：2019-08-06

    申请号：US15642303

    申请日：2017-07-05

    Applicant: QUALCOMM Incorporated

    Inventor： Victor Chan

    IPC: H04N5/243 ,  G06K9/46 ,  G06K9/62 ,  H04N5/235 ,  G06K9/60

    Abstract: Methods, apparatuses, computer-readable medium, and systems are disclosed for performing automatic exposure control (AEC). In one embodiment, a first digital image is captured while applying a first set of values to one or more exposure control parameters. At least one computer vision (CV) operation is performed using image data from the first digital image, thereby generating a first set of CV features from a set of possible CV features. A mask is obtained comprising a value for each feature of the set of possible CV features. Using the mask, a first measure of abundance is obtained of relevant CV features among the first set of CV features extracted from the first digital image. Based on the first measure of abundance of relevant CV features, an updated set of values is generated for applying to the one or more exposure control parameters for capturing a subsequent digital image of the scene.

    TRAINING DATA FOR MACHINE-BASED OBJECT RECOGNITION
    8.
    发明申请
    TRAINING DATA FOR MACHINE-BASED OBJECT RECOGNITION 审中-公开

    公开(公告)号：US20180189609A1

    公开(公告)日：2018-07-05

    申请号：US15861620

    申请日：2018-01-03

    Applicant: QUALCOMM Incorporated

    Inventor： Edwin Chongwoo Park ,  Victor Chan

    IPC: G06K9/62 ,  G06F15/18

    CPC classification number: G06K9/6257 ,  G06F15/76 ,  G06K9/00664 ,  G06K9/00979 ,  G06K9/22 ,  G06K9/6254 ,  G06K9/6256 ,  G06K9/6265 ,  G06K9/6267 ,  G06K2009/4666 ,  G06N20/00 ,  H04L67/20

    Abstract: Systems and methods may enable a user who may not have any experience in machine learning to effectively train new models for use in object recognition applications of a device. Embodiments can include, for example, analyzing training data comprising a set of images to determine a set of metrics indicative of a suitability of the training data in machine-learning training for object recognition, and providing an indication of the set of metrics to a user. Additionally or alternatively, an intermediate model can be used, after a first portion of the machine-learning training is conducted, to determine the effectiveness of a remaining portion of negative samples (images without the object) in the training data or to find other negative samples outside of the training data. Identifying and utilizing effective negative samples in this manner can improve the effectiveness of the training.

    Contrast-adaptive normalized pixel difference
    9.
    发明授权
    Contrast-adaptive normalized pixel difference 有权

    公开(公告)号：US10984511B1

    公开(公告)日：2021-04-20

    申请号：US16664641

    申请日：2019-10-25

    Applicant: QUALCOMM Incorporated

    Inventor： Victor Chan

    IPC: G06K9/00 ,  G06T5/00 ,  G06T5/50 ,  G06T7/00

    Abstract: Due to complexity constraints, most computer vision (CV) features are not suitable for certain applications such as low-power always-on applications. To address such issues, a contrast-adaptive normalized pixel difference (CA-NPD) is proposed. Unlike conventional NPD techniques, the CA-NPD smooths a metric surface, optimizes contrast discriminability, and enables context sensitive normalization.

    Low-power auto-exposure control (AEC) for multi-sensor systems
    10.
    发明授权
    Low-power auto-exposure control (AEC) for multi-sensor systems 有权

    公开(公告)号：US10742897B2

    公开(公告)日：2020-08-11

    申请号：US16118232

    申请日：2018-08-30

    Applicant: QUALCOMM Incorporated

    Inventor： Victor Chan ,  Edwin Chongwoo Park

    IPC: H04N5/243 ,  H04N5/232 ,  H04N5/235 ,  H04N5/247 ,  G06T7/62 ,  G06K9/32

    Abstract: Methods, systems, and devices for exposure control are described, including capturing a first and second field of view with a first and second sensor. The techniques may include identifying a brightness difference and an exposure time difference between the first and second sensor, and capturing a first image and a second image, and outputting a third image including both the first and second image. Techniques may include determining an exposure bias, identifying a hypothesis total gain for the first sensor and a peer sensor total gain for the second sensor, and selecting a total gain for each sensor based on comparing the hypothesis total gain and the peer sensor total gain, and based on a maximum brightness difference between the two sensors. The total gain for each sensor may be adjusted to satisfy the maximum brightness difference and the exposure bias, or based on a region of interest.

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