Performance study of several global thresholding techniques for segmentation
Computer Vision, Graphics, and Image Processing
The NURBS book
A Network of Globally Coupled Chaotic Maps for Adaptive Multi-Resolution Image Segmentation
SBRN '02 Proceedings of the VII Brazilian Symposium on Neural Networks (SBRN'02)
On the threshold effect in the estimation of chaotic sequences
IEEE Transactions on Information Theory
On chaotic simulated annealing
IEEE Transactions on Neural Networks
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Chaotic optimization is a new optimization technique. For image segmentation, conventional chaotic sequence is not fit to two-dimension gray histogram because it is proportional distributing in [0,1]×[0,1]. In order to generate a chaotic sequence can be used to the optimization processing of image segmentation method in two-dimension gray histogram, we propose an chaotic sequence generating method based on Arnold chaotic system and Bézier curve generating algorithm. Simulation results show that the generated sequence is pseudorandom. The most important characteristic of this chaotic sequence is that its distribution is approximately inside a disc whose center is (0.5,0.5) , this characteristic indicates that the sequence is superior to the Arnold chaotic sequence in image segmenting. Based on the extended chaotic sequence generating method, we study the two-dimension Otsu's image segmentation method using chaotic optimization. Simulation results show that the method using the extended chaotic sequence has better segmentation effect and lower computation time than the existed two-dimension Otsu's method.