In this paper, we have developed the theoretical framework for coherent image segmentation using stereo images. The robust segmentation is performed by combining multiple cues such as shape, intensity (color) and depth. Through image segmentation has been an active research field over last few decades, segmentation based on individual cue has several well-known drawbacks. For example, intensity-based schemes tend to generate detailed but inaccurate edges, and motion-based schemes only help segment moving objects. In addition, depth- based schemes may not yield satisfactory segmentation results because disparity estimation itself is a well-known ill-posed problem. Therefore, the main issue in segmentation is how to combine various cues to achieve robust segmentation results. In the proposed scheme, robust and consistent segmentation is achieved by properly combining several cues using MRF/GRF model. We first estimate intensity edges of the image and then re-evaluate the edges based on disparity edge information. In turn, the resulting intensity edges can help estimate an accurate disparity field. In addition, occlusion area can be segmented by properly combining intensity edges of stereo images.