Texture Segmentation Based on Neuronal Activation Degree of Visual Model

In the study of object recognition, image texture segmentation has being a hot and difficult aspect in computer vision. Feature extraction and texture segmentation algorithm are two key steps in texture segmentation. An effective texture description is the important factor of texture segmentation. In this paper, the neuronal activation degree (NAD) of visual model is exploited as the texture description of image patches. By processing the length and direction of NAD, we develop an effective segmentation strategy. First, the length of the NAD are used to partition blank area and non-blank area, then the mark index of neuron is used, which is maximally activated to identify the label of each segment unit to get an initial segmentation. Finally, region merging steps is exerted to get a desired result.

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