3D Mesh segmentation has become an important research field in computer graphics during the past few decades. Many geometry based and semantic oriented approaches for 3D mesh segmentation has been presented. However, only a few algorithms based on Markov Random Field (MRF) has been presented for 3D object segmentation. In this letter, we present a definition of mesh segmentation according to the labeling problem. Inspired by the capability of MRF combining the geometric information and the topology information of a 3D mesh, we propose a novel 3D mesh segmentation model based on MRF and Graph Cuts. Experimental results show that our MRF-based schema achieves an effective segmentation.

In this paper, we present an interactive and intuitive graph-cut-based video segmentation system while taking both color and motion information into consideration with a stroke-based user interface. Recently, graph-cut-based methods become prevalent for image and video segmentation. However, most of them deal with color information only and usually failed under circumstances where there are some regions in both foreground and background with similar colors. Unfortunately, it is usually hard to avoid, especially when the objects are filmed under a natural environment. To make such methods more practical to use, we propose a graph-cut-based video segmentation method based on both color and motion information, since the foreground objects and the background usually have different motion patterns. Moreover, to make the refinement mechanism easy to use, the strokes drawn by the user are propagated to the temporal-spatial video volume according to the motion information for visualization, so that the user can draw some additional strokes to refine the segmentation result in the video volume. The experiment results show that by combining both color and motion information, our system can resolve the wrong labeling due to the color similarity, even the foreground moving object is behind an occlusion object.

This paper proposes an approach to image segmentation using Iterated Graph Cuts based on local texture features of wavelet coefficients. Using Haar Wavelet based Multiresolution Analysis, the low-frequency range (smoothed image) is used for the n-link and the high-frequency range (local texture features) is used for the t-link along with the color histogram. The proposed method can segment an object region having not only noisy edges and colors similar to the background, but also heavy texture change. Experimental results illustrate the validity of our method.

A new unsupervised classification method is proposed for polarimetric SAR images to keep the spatial coherence of pixels and edges of different kinds of targets simultaneously. We consider the label scale variability of images by combining Inhomogeneous Markov Random Field (MRF) and Bayes' theorem. After minimizing an energy function using an expansion algorithm based on Graph Cuts, we can obtain classification results that are discontinuity preserving. Using a NASA/JPL AIRSAR image, we demonstrate the effectiveness of the proposed method.

Mesh segmentation is a foundational operation for many computer graphics applications. Although various automatic segmentation schemes have been proposed, to precisely obtain the user-needed meaningful part of a mesh is a challenging issue. In this paper, we introduce a novel mesh cutout system to efficiently extract meaningful object from a triangular mesh. The algorithm in the current study extends previous min-cut based image segmentation techniques to the domain of 3D mesh. We provide a novel screen-space user interface that allows the user to indicate the meaningful object easily. The results show that our proposed method is relatively simple and effective as a powerful tool for mesh cutout.