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http://dx.doi.org/10.17661/jkiiect.2018.11.2.175

Morphological Object Recognition Algorithm  

Choi, Jong-Ho (Department of IoT Electronic Engineering, Kangnam University)
Publication Information
The Journal of Korea Institute of Information, Electronics, and Communication Technology / v.11, no.2, 2018 , pp. 175-180 More about this Journal
Abstract
In this paper, a feature extraction and object recognition algorithm using only morphological operations is proposed. The morphological operations used in feature extraction are erosion and dilation, opening and closing combining erosion and dilation, and morphological edge and skeleton detection operation. In the process of recognizing an object based on features, a pooling operation is applied to reduce the dimension. Among various structuring elements, $3{\times}3$ rhombus, $3{\times}3$ square, and $5{\times}5$ circle are arbitrarily selected in morphological operation process. It has confirmed that the proposed algorithm can be applied in object recognition fields through experiments using Internet images.
Keywords
Dilation; Erosion; Feature Extraction; Morphology; Object Recognition; Pooling;
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  • Reference
1 Serra, J., Image Analysis and Mathe- matical Morphology, Vol.1, Academic Press, New York, 1982.
2 Serra, J., "Introduction to Mathematical Morphology,"Computer Vision, Graphics, and Image Processing, Vol.35, pp. 283-305, 1986.   DOI
3 Pitas, I. and Venetsanopoulos, A. N., "Morphological Shape Decomposition," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.12, No.1, pp. 38-45, 1990.   DOI
4 Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks", Advances in Neural Information processing Systems 25, NIPS, 2012.
5 H. Lee, R. Grosse, R. Ranganath, and A.Y. Ng., "Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations", Proceedings of the 26th Annual International Conference on Machine Learning, ACM, 2009.
6 Y. LeCun, K. Kavukcuoglu, and C. Farabet, "Convolutional networks and applications in vision", International Symposium in Circuits and Systems (ISCAS), IEEE, 2010.
7 D. C. Ciresan, A. Giusti, L. M. Gam bardella, and J. Schmidhuber, "Deep neural networks segment neuronal membranes in electron microscopy images," In NIPS, 2012.
8 Maragos, P. and Schafer, R.W., "Morpho- logical Skeleton Representation and Coding of Binary Images," IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol.ASSP-34, No.5, pp. 1228-1244, 1986.