This paper presents a novel method for automatically segmenting and detecting targets in complex environment using the improved unit linking pulse coupled neural networks (ULPCNN) combining with contour tracking. On the one hand, the typical ULPCNN model is improved including linear modulate, linear attenuation of dynamic threshold and the attenuation parameter matrix Δ , which is more suitable for segmenting and detecting the target under complex environment. On the other hand, we determine the iteration times and obtain the optimal segmentation result using contour tracking based on maximum line contour point. In order to verify the efficiency, various simulations were conducted for different images acquired from real scenes. Experimental results show, as compared to the conventional approaches, the proposed method can overcome the drawbacks of PCNN and obtain the good results for segmenting and detecting targets against complex background.

Date Published: 5 March 2008
PDF: 10 pages
Proc. SPIE 6623, International Symposium on Photoelectronic Detection and Imaging 2007: Image Processing, 66230D (5 March 2008); doi: 10.1117/12.791277

Published in SPIE Proceedings Vol. 6623:
International Symposium on Photoelectronic Detection and Imaging 2007: Image Processing
Liwei Zhou, Editor(s)