A Gradient Vector Flow Snake Model using Novel Coefficients Setting for Infrared Image Segmentation

Rui Zhang


In this paper, a novel gradient vector flow snake model is proposed for infrared image segmentation. Our model has several advantages in terms of infrared image segmentation. Infrared images are characterized by high noise and low contrast. We propose a novel generalized gradient vector flow snake model combining the advantages of several snake models. And we also use a new type of coefficient setting to improve the capacity of protecting weak edges in infrared images during segmentation process. Experimental results and comparisons against other methods show that our proposed model performs much better in terms of infrared image segmentation than other snake models.


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