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We convert the motion segmentation problem to a binary labeling problem, and propose an iterative solution to group the local patches whose motions are consistent.
Assuming each moving object has its own trajectory in the video, the motion segmentation problem reduces to clustering the trajectories of each of the object [26], another subspace clustering problem.
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Moreover, we expose in depth the problem of object leakage due to occlusion and highlight that motion segmentation could be treated as a graph coloring problem.
This paper presents a novel algorithm for mobile-object segmentation from static background scenes, which is both robust and accurate under most of the common problems found in motion segmentation.
By introducing a dependency, the optical flow problem is now formulated on a Markov random field, like the image binarization problem and the image segmentation problem, and the motions of individual pixels are determined simultaneously by some optimization strategy.
In computer vision, occlusions are almost always seen as undesirable singularities that pose difficult challenges to image motion analysis problems, such as optic flow computation, motion segmentation, disparity estimation, or egomotion estimation.
Motion segmentation refers to the task of segmenting moving objects subject to their motion in order to distinguish and track them in a video.
Motion segmentation has been conducted by analyzing the degree of motion using the extracted feature vectors.
Based on a formulation of the clustering task as an optimization problem using a multi-labeled Markov Random Field, we develop a semi-supervised motion segmentation algorithm by setting up a framework for incorporating prior knowledge into the segmentation algorithm.
We present a novel method for motion segmentation by learning the motion priors from exemplar motions to guide the segmentation.
A comparison of the motion segmentation contrast was made between MODAPTS analysis and automatic motion element segmentation using PCA.
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