公开(公告)号：US20200077033A1

公开(公告)日：2020-03-05

申请号：US16118232

申请日：2018-08-30

Applicant: QUALCOMM Incorporated

Inventor： Victor Chan ,  Edwin Chongwoo Park

IPC: H04N5/243 ,  H04N5/247 ,  H04N5/232 ,  H04N5/235

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.

公开(公告)号：US20180189228A1

公开(公告)日：2018-07-05

申请号：US15861617

申请日：2018-01-03

Applicant: QUALCOMM Incorporated

Inventor： Edwin Chongwoo Park ,  Victor Chan

IPC: G06F15/18 ,  H04L29/08 ,  G06K9/62

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.