Project Description:

Image segmentation aims to partition an image into meaningful regions and extract important objects therein. It is an important and yet ambiguous problem in computer visions. Common methods usually impose various constraints on the extracted regions as regularization to achieve the image segmentation goal. In practical situations, it is desirable to incorporate shape prior information about the objects into the segmentation model. In this work, we present a novel shape prior image segmentation model based on discrete conformality structure. The discrete conformality structure captures the angle structures of the meshes representing the segmented objects. Shape prior information can be prescribed into the segmentation model by imposing constraints on the conformality structures. Segmentation results with prescribed local or global shape priors can be easily obtained. We illustrate our idea on various shape prior segmentation problems such as the convexity prior segmentation problem. Experimental results demonstrate the efficacy of our proposed method.

Publication:

• C.Y. Siu, H.L. Chan, L.M. Lui, Shape-prior Image Segmentation using Vertex- and Edge-Based Conformality Structures, to be submitted (2018)