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Searching objects of interest in lar...

Wang, Xiaoyu.

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Searching objects of interest in large scale data.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Searching objects of interest in large scale data./
作者:	Wang, Xiaoyu.
面頁冊數:	143 p.
附註:	Source: Dissertation Abstracts International, Volume: 74-04(E), Section: B.
Contained By:	Dissertation Abstracts International74-04B(E).
標題:	Engineering, Computer. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3534028
ISBN:	9781267794222

Searching objects of interest in large scale data.
Wang, Xiaoyu.

Searching objects of interest in large scale data. - 143 p.

Source: Dissertation Abstracts International, Volume: 74-04(E), Section: B.

Thesis (Ph.D.)--University of Missouri - Columbia, 2012.

The research on object detection/tracking and large scale visual search/recognition has recently gained substantial progress and has started to contribute to improving the quality of life worldwide: real-time face detectors have been integrated into point-and-shoot cameras, smart phones, and tablets; content-based image search is available at Google and Snaptell of Amazon; vision-based gesture recognition has been an indispensable component of the popular Kinect game console.

ISBN: 9781267794222Subjects--Topical Terms:

1669061
Engineering, Computer.

Searching objects of interest in large scale data.
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300 $a 143 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-04(E), Section: B.
500 $a Adviser: Tony X. Han.
502 $a Thesis (Ph.D.)--University of Missouri - Columbia, 2012.
520 $a The research on object detection/tracking and large scale visual search/recognition has recently gained substantial progress and has started to contribute to improving the quality of life worldwide: real-time face detectors have been integrated into point-and-shoot cameras, smart phones, and tablets; content-based image search is available at Google and Snaptell of Amazon; vision-based gesture recognition has been an indispensable component of the popular Kinect game console.
520 $a In this dissertation, we investigate computer vision problems related to object detection, adaptation, tracking and content based image retrieval, all of which are indispensable components of a video surveillance system or a robot system. Our contribution involves feature development, exploration of detection correlations, object modeling, local context information of descriptors. More specifically, we designed a feature set for object detection with occlusion handling. To improve the detection performance on a video, we proposed a non-parametric detector adaptation algorithm to improve the performance of state of the art detectors for each specific video. To effectively track the detected object, we introduce a metric learning framework to unify the appearance modeling and visual matching. Taking advantage of image descriptor appearance context as well as local spatial context, we achieved state of the art retrieval performance based on the vocabulary tree based image retrieval framework. All the proposed algorithms are validated by throughout experiments.
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773 0 $t Dissertation Abstracts International $g 74-04B(E).
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