Geometric Constraints Based Selective Segmentation for Vector Valued Images
B. R. Kapuriya, Debasish Pradhan, Reena Sharma · 2020
Selective image segmentation extracts object of interest from an image based on user input. There are many variational methods which are effective in segmenting object which has uniform intensity. But many times object with multiple intensities needs to extracted. In this paper, we propose an active contour based algorithm for selective segmentation of objects in vector valued image. Here, we introduced new energy function by adding new geometric constraints based fidelity terms from local region of the object for vector valued images. The model minimizes new functional over the length of the contour along various parts of the object in different component images. Experimental results shows that the proposed method is effective in segmenting object having multi intensities. Performance of the proposed method is determined using Jaccard's similarity index for all methods.