東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Low-level computer vision applicatio...

Lu, Xiaoye.

FindBook

Google Book

Amazon

博客來

Low-level computer vision applications to surveillance and robotics.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Low-level computer vision applications to surveillance and robotics./
作者:	Lu, Xiaoye.
面頁冊數:	117 p.
附註:	Adviser: Roberto Marduchi.
Contained By:	Dissertation Abstracts International68-02B.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3250107

Low-level computer vision applications to surveillance and robotics.
Lu, Xiaoye.

Low-level computer vision applications to surveillance and robotics. - 117 p.

Adviser: Roberto Marduchi.

Thesis (Ph.D.)--University of California, Santa Cruz, 2007.

Computer Vision is an important component of environment sensing systems for applications such as robotics and surveillance. Images and video sequences contain information about the scene at several different levels. Accordingly, computer vision algorithms are traditionally classified into a hierarchy of levels, from low-level (e.g., feature extraction and matching) to high-level (e.g., object recognition).Subjects--Topical Terms:

626642
Computer Science.

Low-level computer vision applications to surveillance and robotics.
LDR:03268nam 2200301 a 45 001 964294
005 20110901
008 110901s2007 eng d
035 $a (UMI)AAI3250107
035 $a AAI3250107
040 $a UMI $c UMI
100 1 $a Lu, Xiaoye. $3 1287356
245 1 0 $a Low-level computer vision applications to surveillance and robotics.
300 $a 117 p.
500 $a Adviser: Roberto Marduchi.
500 $a Source: Dissertation Abstracts International, Volume: 68-02, Section: B, page: 1078.
502 $a Thesis (Ph.D.)--University of California, Santa Cruz, 2007.
520 $a Computer Vision is an important component of environment sensing systems for applications such as robotics and surveillance. Images and video sequences contain information about the scene at several different levels. Accordingly, computer vision algorithms are traditionally classified into a hierarchy of levels, from low-level (e.g., feature extraction and matching) to high-level (e.g., object recognition).
520 $a This dissertation is developing a number of low level vision algorithms with applications to surveillance and robotics. The first part of this dissertation addresses automatic feature matching for camera pair calibration. An algorithm was proposed that uses a novel topological constraint, named "cross epipolar ordering constraint" (CEO constraint). No knowledge of camera orientation or position is needed for this constraint. It effectively reduces the search space for feature matching by ruling out matches that are proven to be unrealizable. An wide baseline feature matching algorithm is proposed using this constraint. This algorithm uses coarse-to-fine search, and reduces the search space further more.
520 $a The second part of this dissertation describes work on the analysis of stereo data for the automatic detection of "curbs" and "steps". Our real-time algorithm is able to determine the 3-D location of a curb, which uses robust techniques to combine range and brightness information. This algorithm was developed as part of a DARPA project for urban robotics, and is currently being ported to a semi-autonomous wheelchair control system.
520 $a The third part of this dissertation focus on a fast motion detection algorithm on a power-awared visual sensor network developed at UC Santa Cruz, Meerkats. This algorithm can detect moving blobs in two consecutive images and calculate the velocity of the motion using optical flow constraint. It is based on the rank analysis of the structure matrix, which is built up from local spatio-temporal gradients of a image sequence. Then a multiscale belief propagation scheme is also proposed in the dissertation. The multiscale belief propagation can deal with different sized moving blobs and large motions, which have been problems in optical flow calculation. This algorithm is designed specifically for Meerkats networks, so we give a detailed power consumption analysis of our algorithm on Meerkats.
590 $a School code: 0036.
650 4 $a Computer Science. $3 626642
650 4 $a Engineering, Robotics. $3 1018454
690 $a 0771
690 $a 0984
710 2 0 $a University of California, Santa Cruz. $3 1018764
773 0 $t Dissertation Abstracts International $g 68-02B.
790 $a 0036
790 1 0 $a Marduchi, Roberto, $e advisor
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3250107