KEYWORDS: Image segmentation, Image processing algorithms and systems, Particles, Image information entropy, Particle swarm optimization, Image processing, Human vision and color perception, Detection and tracking algorithms, Optimization (mathematics), Machine vision
An improved watershed image segmentation algorithm is proposed to solve the problem of over-segmentation by
classical watershed algorithm. The new algorithm combines region growing with classical watershed algorithm. The key
to region growing lies in choosing a growing threshold to reach a desired result of image segmentation. An entropy
evaluation criterion is constructed to determine the optimal threshold. Considering the entropy evaluation criterion as an
objective function, the particle swarm optimization algorithm is employed to search global optimization of the objective
function. Experimental results show that this new algorithm can solve the problem of over-segmentation effectively.
Anomalous photorefractive behaviors that cannot be explained by previous models are observed in doped KNSBN crystals. The space-charge field increases to its maximum value and then gradually decreases as the incident light intensity increases. The superlinear dependence of the response rate on intensity, i.e. (tau) -1 varies direct as Ix (x greater than 1), are obtained in a Co:KNSBN crystal. The nearly linear dependence of response rate on intensity is measured in heavy reduced Co:KNSBN crystal, in which the dark decay of space-charge field is much faster and the total amount of dark decay is also larger than another one. A new model of two types of carriers with two deep and two shallow traps is proposed to explain these results.
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