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

Design and development of a real-tim...

Tennessee State University., Electrical & Computer Engineering.

FindBook

Google Book

Amazon

博客來

Design and development of a real-time gesture recognition system.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Design and development of a real-time gesture recognition system./
作者:	Ferdousi, Zannatul.
面頁冊數:	186 p.
附註:	Adviser: Fenghui Yao.
Contained By:	Masters Abstracts International46-05.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1453388
ISBN:	9780549559054

Design and development of a real-time gesture recognition system.
Ferdousi, Zannatul.

Design and development of a real-time gesture recognition system. - 186 p.

Adviser: Fenghui Yao.

Thesis (M.S.)--Tennessee State University, 2008.

Gesture recognition enables a hands-free or device-free interaction with a computer thereby expands the range of possibilities for computer usage. Recognition of the gesture is difficult because of spatial variation and temporal variation among gesture performed by different subjects. A gesture recognition system based on skin detection is developed and presented in this thesis. This system takes the video stream as input, extracts hand area and computes hand motion features, and then recognizes that feature. The process flow of the gesture detection algorithm is as follows. First, it identifies the hand region and calculates the motion trajectories of hand. Then, it creates a motion pattern of the average trajectories for each gesture. Finally, the system classifies pattern based on Euclidean distance and cross-correlation pattern matching techniques. To test and evaluate the algorithm, this system was built using Visual C++. This system is verified for 20 gestures prepared by six performers in different environments. The experiment results show that the average gesture recognition rate is 89% in Cross correlation method and 85.20% in Euclidean distance method. The significance of the system is that it can recognize the gesture feature and display the recognition result in less than 1/30 second. It detects the gestures independent of skin color and physical structure of the performer. The system also works in different complex background with normal intensity of light.

ISBN: 9780549559054Subjects--Topical Terms:

626642
Computer Science.

Design and development of a real-time gesture recognition system.
LDR:02436nmm 2200301 a 45 001 867537
005 20100804
008 100804s2008 ||||||||||||||||| ||eng d
020 $a 9780549559054
035 $a (UMI)AAI1453388
035 $a AAI1453388
040 $a UMI $c UMI
100 1 $a Ferdousi, Zannatul. $3 1036280
245 1 0 $a Design and development of a real-time gesture recognition system.
300 $a 186 p.
500 $a Adviser: Fenghui Yao.
500 $a Source: Masters Abstracts International, Volume: 46-05, page: 2726.
502 $a Thesis (M.S.)--Tennessee State University, 2008.
520 $a Gesture recognition enables a hands-free or device-free interaction with a computer thereby expands the range of possibilities for computer usage. Recognition of the gesture is difficult because of spatial variation and temporal variation among gesture performed by different subjects. A gesture recognition system based on skin detection is developed and presented in this thesis. This system takes the video stream as input, extracts hand area and computes hand motion features, and then recognizes that feature. The process flow of the gesture detection algorithm is as follows. First, it identifies the hand region and calculates the motion trajectories of hand. Then, it creates a motion pattern of the average trajectories for each gesture. Finally, the system classifies pattern based on Euclidean distance and cross-correlation pattern matching techniques. To test and evaluate the algorithm, this system was built using Visual C++. This system is verified for 20 gestures prepared by six performers in different environments. The experiment results show that the average gesture recognition rate is 89% in Cross correlation method and 85.20% in Euclidean distance method. The significance of the system is that it can recognize the gesture feature and display the recognition result in less than 1/30 second. It detects the gestures independent of skin color and physical structure of the performer. The system also works in different complex background with normal intensity of light.
590 $a School code: 0840.
650 4 $a Computer Science. $3 626642
650 4 $a Engineering, System Science. $3 1018128
690 $a 0790
690 $a 0984
710 2 $a Tennessee State University. $b Electrical & Computer Engineering. $3 1036279
773 0 $t Masters Abstracts International $g 46-05.
790 $a 0840
790 1 0 $a Bodruzzaman, Mohammad $e committee member
790 1 0 $a Hong, Liang $e committee member
790 1 0 $a Yao, Fenghui, $e advisor
791 $a M.S.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1453388