DETECTING PORTIONS OF INTEREST IN IMAGES
    13.
    发明申请

    公开(公告)号:US20180315180A1

    公开(公告)日:2018-11-01

    申请号:US15901551

    申请日:2018-02-21

    Inventor: Joseph TOWNSEND

    Abstract: A computer-implemented method of automatically locating a portion of interest in image or matrix data derived from an item under consideration includes: identifying parts of the image or matrix data that satisfy a preset threshold as objects which are possibly parts of the portion of the interest; applying at least one preselected filter to the data corresponding to the objects to find a set of objects consisting of the objects most likely to be part of the portion of interest; sorting the objects of the set into clusters according to a predefined criterion; and using a known characteristic of the portion of interest to identify which one of the clusters corresponds to the portion of interest.

    Target Tracking with Inter-Supervised Convolutional Networks

    公开(公告)号:US20180285692A1

    公开(公告)日:2018-10-04

    申请号:US15486392

    申请日:2017-04-13

    Applicant: ULSee Inc.

    Inventor: Jingjing Xiao

    Abstract: We propose a tracking framework that explicitly encodes both generic features and category-based features. The tracker consists of a shared convolutional network (NetS), which feeds into two parallel networks, NetC for classification and NetT for tracking. NetS is pre-trained on ImageNet to serve as a generic feature extractor across the different object categories for NetC and NetT.NetC utilizes those features within fully connected layers to classify the object category. NetT has multiple branches, corresponding to multiple categories, to distinguish the tracked object from the background. Since each branch in NetT is trained by the videos of a specific category or groups of similar categories, NetT encodes category-based features for tracking. During online tracking, NetC and NetT jointly determine the target regions with the right category and foreground labels for target estimation.

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