Department of Computer Science, University of California, Los Angeles, Boelter Hall, 405 Hilgard Ave, Los Angeles, CA 90095, USA.
IEEE Trans Pattern Anal Mach Intell. 2012 Oct;34(10):1942-51. doi: 10.1109/TPAMI.2011.271.
We describe an approach for segmenting a moving image into regions that correspond to surfaces in the scene that are partially surrounded by the medium. It integrates both appearance and motion statistics into a cost functional that is seeded with occluded regions and minimized efficiently by solving a linear programming problem. Where a short observation time is insufficient to determine whether the object is detachable, the results of the minimization can be used to seed a more costly optimization based on a longer sequence of video data. The result is an entirely unsupervised scheme to detect and segment an arbitrary and unknown number of objects. We test our scheme to highlight the potential, as well as limitations, of our approach.
我们描述了一种将运动图像分割成对应于场景中被介质部分包围的表面的区域的方法。它将外观和运动统计信息集成到一个代价函数中，该函数以遮挡区域为种子，并通过有效地求解线性规划问题来最小化。在短观测时间不足以确定对象是否可分离的情况下，可以使用最小化的结果来为基于更长视频数据序列的更昂贵的优化播种。结果是一种完全无监督的方案，用于检测和分割任意数量和未知的对象。我们测试了我们的方案，以突出我们方法的潜力和局限性。
