Shape Representation and Retrieval Using Radial Basis Probabilistic Neural Networks

被引：0
|
作者：
Shaaban, Zyad [1 ,2 ]
Arif, Thawar [1 ,3 ,4 ,5 ]
机构：
[1] Appl Sci Univ, Fac Informat Technol, Amman 11931, Jordan
[2] Appl Sci Univ, Fac Informat Technol, Comp Sci, Amman 11931, Jordan
[3] Appl Sci Univ, Dept Comp Sci, Amman 11931, Jordan
[4] IEEE Comp Soc, Washington, DC 20036 USA
[5] ACM, New York, NY 10121 USA
来源：
INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY | 2008年 / 8卷 / 12期
关键词：
Shape retrieval; distance transformation; neural networks; image contour; moment invariants;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a 2D shape retrieval system using moment invariants based on a representation of image boundary of the shape objects. The original binary image is transformed into a matrix which consists of numbers that represents the distance to the shape contour. Chessboard distance transform is used for the representation of the shape contour. Moment invariants are computed for the original binary image and the proposed transformed contour image. In this paper, we use radial basis probabilistic neural networks to recognize the object shapes based on moment invariants features extracted from the shape contour. The effectiveness of the proposed shape retrieval system using moment invariants based on image contour is reported.
引用
收藏
页码：271 / 279
页数：9
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