%0 Conference Paper %1 Lee1994 %A Lee, Heung-Ho %A Kwon, Hee-Yong %A Hwang, Hee-Yeung %B Speech, Image Processing and Neural Networks, 1994. Proceedings, ISSIPNN '94., 1994 International Symposium on %D 1994 %I IEEE Computer Society %K 2D complex-log complexity, computational connection extraction, feature image invariant mapping, nets network neural object recognition, rotation scale sequences, translation %P 405--408 %R 10.1109/SIPNN.1994.344882 %T An efficient scale and rotation invariant 2-D object recognition method %V 2 %X 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(n2) 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

@inproceedings{Lee1994, 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(n2) 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}, added-at = {2011-03-27T19:35:34.000+0200}, author = {Lee, Heung-Ho and Kwon, Hee-Yong and Hwang, Hee-Yeung}, biburl = {https://www.bibsonomy.org/bibtex/296278544f0f133af2aca2254d948a7a1/cocus}, booktitle = {Speech, Image Processing and Neural Networks, 1994. Proceedings, ISSIPNN '94., 1994 International Symposium on}, booktitleaddon = {April 13--16, 1994}, doi = {10.1109/SIPNN.1994.344882}, file = {:./00344882.pdf:PDF}, interhash = {c85a82b75222428958b60be762e606ee}, intrahash = {96278544f0f133af2aca2254d948a7a1}, keywords = {2D complex-log complexity, computational connection extraction, feature image invariant mapping, nets network neural object recognition, rotation scale sequences, translation}, location = {#ieeeaddr#}, pages = {405--408}, publisher = {{IEEE} Computer Society}, timestamp = {2011-03-27T19:35:41.000+0200}, title = {An efficient scale and rotation invariant 2-D object recognition method}, volume = 2, year = 1994 }