Object segmentation in multiple views without camera calibration

We propose a method for extracting a desired object in multi-view images without camera calibration. We match the corner points obtained automatically by the Scale-invariant feature transform (SIFT) in multiview images, and then connect multi-view images into a weighted undirected graph. Thus, multi-view object segmentation converts to a graph partitioning problem solved by Biased Normalized Cuts. Compared to the existing methods, the main advantages of our method are that: (1) it needn't camera pose and intrinsic parameters; (2) arbitrary view number images (include single image) can be processed. The promising experimental results for images reveal the effectiveness of our approach.