DocumentCode :
276129
Title :
A novel moment-based shape description and recognition technique
Author :
Sardana, H.K. ; Daemi, M.F. ; Sanders, A. ; Ibrahim, M.K.
Author_Institution :
Nottingham Univ., UK
fYear :
1992
fDate :
7-9 Apr 1992
Firstpage :
147
Lastpage :
150
Abstract :
The task of recognition of objects from their two dimensional views has been attempted in the past using different techniques. The general requirement has been to represent the original two dimensional iconic image to some compact symbolic description to facilitate matching and storage requirements. Often, the quantitative symbolic description that is needed from an image is more likely to be of the order of tens of real numbers. Another requirement is to recognise as identical two patterns which differ in location, rotation or size. A further requirement is to have description sensitive enough to take care of all the features in an object and robust and flexible enough to disregard the minor differences due to noise and image acquisition system defects. The invariance to rotation and translation is achieved with the use of global techniques such as moments, Fourier descriptors and the cyclic chain codings of polygonal approximation of the objects. The scale invariance is also reported to be achieved with some additional computational cost. Such final description is termed as an n-dimensional feature vector represented as a point in n-dimensional space. Minimum-distance object classification can efficiently be used with such a description
Keywords :
computational geometry; pattern recognition; picture processing; moment-based; moments; n-dimensional feature vector; object classification; scale invariance; shape description; shape recognition; symbolic description;
fLanguage :
English
Publisher :
iet
Conference_Titel :
Image Processing and its Applications, 1992., International Conference on
Conference_Location :
Maastricht
Print_ISBN :
0-85296-543-5
Type :
conf
Filename :
146760
Link To Document :
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