Pre-print Available via FTP

[bhuiyan@mars.elcom.nitech.ac.jp]

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FTP-filename: /pub/WCNN_95.ps.gz
URL ftp://133.68.21.193/pub/WCNN_95.ps.gz



Performance Evaluation of a Neural Network based Edge Detector 
		 for high-contrast images


                Md. Shoaib Bhuiyan and Akira Iwata
	     Dept. Electrical & Computer Engineering
	 Nagoya Institute of Technology, Nagoya, Japan 466
      

To appear in Proc. World Congress on Neural Networks, 1995

ABSTRACT

	The performance of a neural network based edge detector for
high-contrast images has been investigated both quantitatively and
qualitatively. We have compared it's performance for both synthetic
and natural images with those of four existing edge detection methods
namely, Sobel's operator, Johnson proposed Contrast based Sobel
operator, Marr-Hildreth's Laplacian-of-Gaussian (LoG) operator, and
Canny's operator. We have also investigated it's noise immunity and
compared with those of the above mentioned methods.  We have found the
performance of the neural network based edge detector to be
consistently better, especially for images where the illumination
varies widely.


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The paper can be retrieved via anonymous ftp by following these
instructions:

        unix>      ftp ftp.elcom.nitech.ac.jp (133.68.21.193)
        ftp:name>  anonymous
        Password:> your complete e-mail address
        ftp>       cd pub
        ftp>       get WCNN_95.ps.gz
        ftp>       bye
        unix>      gunzip WCNN_95.ps.gz
        unix>      lpr WCNN_95.ps

WCNN_95.ps is 5.57Mb, five pages in postscript format. The paper
presents some results of our previously proposed algorithm to extract
edges from an image with high contrast (also available by ftp from the
same location, filename: ICONIP.ps.gz) and compares them with four
existing edge detection mathods. Your feedback is very much
appreciated (bhuiyan at mars.elcom.nitech.ac.jp)


--Md. Shoaib Bhuiyan