NDLI: An efficient scale and rotation invariant 2-D object recognition method

Content Provider	IEEE Xplore Digital Library
Author	Heung-Ho Lee Hee-Yong Kwon Hee-Yeung Hwang
Copyright Year	1994
Description	Author affiliation: Dept. of Electr. Eng., Chung Nam Nat. Univ., Taejon, South Korea (Heung-Ho Lee)
Abstract	This paper proposes an efficient scale and rotation invariant 2-D object recognition method using Complex-Log Mapping (CLM) and Translation Invariant Neural Network (TINN). CLM is known as very useful transform for extracting scale and rotation invariant features. However, the results are given in a wrap-around translated form, which requires subsequent wrap-translation invariant recognition steps. Recently, a new method using an augmented second order neural network (SONN) was introduced as a solution. It requires, however, a connection complexity O(n/sup 2/) for input feature extraction which is too high to be implemented. In this paper, we propose a method reducing the connection complexity to O(n*log(n)) by using TINN. Experimental results show that the recognition performance of the proposed method is almost the same as that of SONN while its network size is significantly reduced.<>
Starting Page	405
Ending Page	408
File Size	215946
Page Count	4
File Format	PDF
ISBN	078031865X
DOI	10.1109/SIPNN.1994.344882
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-04-13
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fourier transforms Neural networks Two dimensional displays Feature extraction Pattern recognition Object recognition Helium Multi-layer neural network
Content Type	Text
Resource Type	Article

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