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Dynamic based contour clustering.

Zhang, Xiao.

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Dynamic based contour clustering.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Dynamic based contour clustering./
作者:	Zhang, Xiao.
面頁冊數:	64 p.
附註:	Source: Masters Abstracts International, Volume: 53-06.
Contained By:	Masters Abstracts International53-06(E).
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1565552
ISBN:	9781321206067

Dynamic based contour clustering.
Zhang, Xiao.

Dynamic based contour clustering. - 64 p.

Source: Masters Abstracts International, Volume: 53-06.

Thesis (M.S.)--Northeastern University, 2014.

This item must not be sold to any third party vendors.

Contour provides very fundamental and important information of objects from images, which is very useful in object detection, classification, recognition and retrieval. A wide range of computer vision tasks benefit from the improvement at contour detection and clustering. And contour clustering is a necessary and crucial step of contour based object recognition.

ISBN: 9781321206067Subjects--Topical Terms:

649834
Electrical engineering.

Dynamic based contour clustering.
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100 1 $a Zhang, Xiao. $3 2101938
245 1 0 $a Dynamic based contour clustering.
300 $a 64 p.
500 $a Source: Masters Abstracts International, Volume: 53-06.
500 $a Adviser: Octavia Camps.
502 $a Thesis (M.S.)--Northeastern University, 2014.
506 $a This item must not be sold to any third party vendors.
520 $a Contour provides very fundamental and important information of objects from images, which is very useful in object detection, classification, recognition and retrieval. A wide range of computer vision tasks benefit from the improvement at contour detection and clustering. And contour clustering is a necessary and crucial step of contour based object recognition.
520 $a In this thesis, we present a novel approach of clustering contours based on comparing the dynamic distances between them. A significant portion of this work is inspired by the excellent performance of human activity recognition using this method. The main idea is to hypothesize contours extracted from images as the output trajectories of unknown linear dynamic systems with unknown initial conditions. To avoid the complex task of system identification, Hankel matrices are built to encapsulate the dynamic properties of contour trajectories in the feature space. Then we use dynamic based dissimilarity metric to compare Hankel matrices and calculate the dynamic distances between them. With a matrix consisting of dynamic distances of each possible pair of contours, Normalized Cuts is applied to classify contours into different clusters. In real application, contour trajectories are composed of a sequence of discrete pixels. Rank minimization is required to clean the data and reduce the rank of Hankel matrices. And contour trajectories are also needed to be chopped at corners into segments. The primary contribution of the thesis is proposing a robust dissimilarity metric combing the dissimilarity score function used in human activity recognition with the order information of dynamic systems, the rank of Hankel matrices. We also use cumulative angles as the feature of contours instead of velocities, the derivative of positions of contours.
590 $a School code: 0160.
650 4 $a Electrical engineering. $3 649834
650 4 $a Computer engineering. $3 621879
650 4 $a Artificial intelligence. $3 516317
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710 2 $a Northeastern University. $b Electrical and Computer Engineering. $3 1018491
773 0 $t Masters Abstracts International $g 53-06(E).
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793 $a English
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