Segmentation of the Wear Area of Planar Shoeprint Using Mean Shift

Abstract

In the field of forensic science, the wear area of planar shoeprint is a useful information for analysis of criminal suspects. However, the identification of shoeprint wear areas is often subjective to the expert’s personal experience. In this paper, considering the characteristics of planar shoeprint images, a segmentation algorithm based on mean-shift is proposed to process planar shoeprint image to get the wear area. Firstly, the multiplicative intrinsic component optimization method(MICO) is employed to pre-segment the image. This method combines the level set with the offset field correction method to segment the outline of the unevenly printed shoeprint image. Secondly, we use mean shift to segment the image. Finally, we compare the actual wear area with the segmented wear area. It is found that the segmentation effect is good. By pattern recognition method, the wear area of the shoeprint is segmented to provide more objective technical support for detecting cases.