This study concentrates on consistent object contour extraction method for stereo image segmentation after the object regions in the left image have been obtained. By taking advantage of the epipolar geometry, our approach introduces an energy optimization framework that incorporates both the stereo correspondence term and patch-based object contour probability term. The contour map is generated by integrating the terms of stereo correspondence and patch-based object contour probability; then, the optimal contours are obtained using geodesic distance technology. The core of the proposed method is to build upon an energy optimization framework with two key contributions: first, it incorporates the patch-based object contour probability term that introduces two search strategies to efficiently find the joint nearest neighbor patch pairs for the stereo image pair. The patch-based object contour probability term provides consistent and reliable priors for the contour extraction. Second, previous methods encounter missing pixels in the extracted contour in the occluded regions. Our approach overcomes this limit by introducing the geodesic distance technology to search the optimal contours. Experimental evaluation on Middlebury dataset and Adobe open dataset indicates that the results of our stereo image segmentation are comparable with or of higher quality than state-of-the-art methods.
Image recoloring is the process of modification and adjustment of color appearance in images. Existing methods address the recoloring of a single image. We propose a method for recoloring stereoscopic images. Naively recoloring each image independently will require a pair of strokes in the source stereoscopic image pair. However, it is difficult to require consistent strokes on both the left and right views. We show how to extend a single image recoloring to work on stereoscopic images. Our method requires only a few user strokes on the left view and automatically transfers the corresponding strokes to the right view. Then a nonlocal color linear model optimization assumption is designed. Our nonlocal color linear model inherits the advantages of global and local color propagation methods. Our approach can propagate color cues in a global manner which can propagate color relatively far from the provided color constraints, while it provides the user with good local control. The experimental results show that the recolorized image pair is geometrically consistent with the original one.
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