Abstract Local Gaussian distribution fitting energy(LGDF) model lacks global information and is sensitive to the selection of initial contour curve. Especially in the segmentation of images with weak edges and textures, it is easy to fall into the local extremum and is poorly robust to noise. To solve the problems, an LGDF model with fractional differential is proposed. A global Grümwald-Letnikov fractional gradient fitting term is introduced into LGDF model to enhance the gradient information of the weak edge and texture regions and improve the robustness to initial contour curve and noise. The coefficients of global and local terms are determined by adaptive weighting function to improve the efficiency and accuracy of gray-scale inhomogeneous image segmentation. The adaptive fractional order function is constructed according to gradient modulus, information entropy and contrast of the image to improve the segmentation efficiency. Both theoretical analysis and experiments show that the model can be used for the segmentation of the gray-scale inhomogeneous images and the images with weak texture and weak edge. Experiments on synthetic and real images show that the proposed model improves the accuracy and efficiency of image segmentation.
Fund:Supported by National Natural Science Foundation of China(No.61663031,61661036)
Corresponding Authors:
(CHU Jun(corresponding author), Ph.D., professor. Her research interests include image processing and analysis, pattern recognition and computer vision.)
About author:: (YU Jiajia, master student. Her research interests include computer vision image segmentation.)(MIAO Jun, Ph.D., lecturer. His research interests include computer vision, pattern re-cognition and machine learning.)(ZHANG Guimei, Ph.D., professor. Her research interests include computer vision, image processing and pattern recognition.)
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2019, Vol. 32 
