Plant Disease Image Segmentation based on Level Set and Visual Saliency

Abstract:

In order to improve the accuracy and efficiency of plant disease image segmentation, this paper propose a color image segmentation method based on level set and visual saliency. Firstly adopt a saliency detection algorithm based on wavelet transform to get the initial position of curve evolution in the active contour model, and construct an active contour model based on salient regions. Then design an edge detection operator of vector-valued image, and introduce it into the reconstruction of distance regularized level set evolution, to construct a new level set energy functional with a more flexible initial contour, faster evolution speed and more accurate object segmentation. Finally experimental comparisons demonstrate the proposed model has a good leaf disease segmentation effect.