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New change detection models for obje...

Liu, Qiang.

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New change detection models for object-based encoding of patient monitoring video.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	New change detection models for object-based encoding of patient monitoring video./
作者:	Liu, Qiang.
面頁冊數:	147 p.
附註:	Source: Dissertation Abstracts International, Volume: 66-09, Section: B, page: 4993.
Contained By:	Dissertation Abstracts International66-09B.
標題:	Engineering, Electronics and Electrical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3188966
ISBN:	9780542311468

New change detection models for object-based encoding of patient monitoring video.
Liu, Qiang.

New change detection models for object-based encoding of patient monitoring video. - 147 p.

Source: Dissertation Abstracts International, Volume: 66-09, Section: B, page: 4993.

Thesis (Ph.D.)--University of Pittsburgh, 2005.

The goal of this thesis is to find a highly efficient algorithm to compress patient monitoring video. This type of video mainly contains local motion and a large percentage of idle periods. To specifically utilize these features, we present an object-based approach, which decomposes input video into three objects representing background, slow-motion foreground and fast-motion foreground. Encoding these three video objects with different temporal scalabilities significantly improves the coding efficiency in terms of bitrate vs. visual quality.

ISBN: 9780542311468Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

New change detection models for object-based encoding of patient monitoring video.
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500 $a Source: Dissertation Abstracts International, Volume: 66-09, Section: B, page: 4993.
500 $a Adviser: Robert J. Sclabassi.
502 $a Thesis (Ph.D.)--University of Pittsburgh, 2005.
520 $a The goal of this thesis is to find a highly efficient algorithm to compress patient monitoring video. This type of video mainly contains local motion and a large percentage of idle periods. To specifically utilize these features, we present an object-based approach, which decomposes input video into three objects representing background, slow-motion foreground and fast-motion foreground. Encoding these three video objects with different temporal scalabilities significantly improves the coding efficiency in terms of bitrate vs. visual quality.
520 $a The video decomposition is built upon change detection which identifies content changes between video frames. To improve the robustness of capturing small changes, we contribute two new change detection models. The model built upon Markov random theory discriminates foreground containing the patient being monitored. The other model, called covariance test method, identifies constantly changing content by exploiting temporal correlation in multiple video frames. Both models show great effectiveness in constructing the defined video objects. We present detailed algorithms of video object construction, as well as experimental results on the object-based coding of patient monitoring video.
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