Bayesian object extraction from uncalibrated image pairs

This paper proposes a region-based algorithm for the extraction of foreground objects in uncalibrated images or video sequences. At the initialization step, a pair of images are overly segmented based on color and texture. Then, several dominant transforms that represent the relationship between two images are found, and each pixel is transformed by these transformations. By considering transform parameters and using the proposed area-ratio test, each pixel is assigned to a background or an object. We consider the resulting binary image as a binary random field, from which the likelihood of being foreground is computed for each initial segment. Using the likelihood model for each segment and the a priori assumption on the smoothness of labels, Bayesian approach is applied to label the segments. Experiments on various images and videos show promising results that the objects are clearly extracted.

[1]  Katsushi Ikeuchi,et al.  Separating Reflection Components of Textured Surfaces Using a Single Image , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Hyun Wook Park,et al.  Region-of-interest coding based on set partitioning in hierarchical trees , 2002, IEEE Trans. Circuits Syst. Video Technol..

[3]  H. B. Mitchell.  Markov Random Fields , 1982 .

[4]  King Ngi Ngan,et al.  Video segmentation for content-based coding , 1999, IEEE Trans. Circuits Syst. Video Technol..

[5]  Nanning Zheng,et al.  Stereo Matching Using Belief Propagation , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Andrew Blake,et al.  Probabilistic Fusion of Stereo with Color and Contrast for Bilayer Segmentation , 2006, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Sang Wook Lee,et al.  Estimation of diffuse and specular appearance , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[8]  Brendan J. Frey,et al.  Learning flexible sprites in video layers , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[9]  Steven A. Shafer,et al.  Using color to separate reflection components , 1985 .

[10]  Dorin Comaniciu,et al.  Robust analysis of feature spaces: color image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  Yoshiaki Shirai,et al.  Detecting persons on changing background , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[12]  C. Strecha,et al.  Wide-baseline stereo from multiple views: A probabilistic account , 2004, CVPR 2004.

[13]  Nam Ik Cho,et al.  Stochastic Diffusion for Correspondence Estimation and Objects Segmentation , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[14]  Edward H. Adelson,et al.  Representing moving images with layers , 1994, IEEE Trans. Image Process..

[15]  Richard Szeliski,et al.  A layered approach to stereo reconstruction , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[16]  Richard Szeliski,et al.  An Integrated Bayesian Approach to Layer Extraction from Image Sequences , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Forsyth,et al.  Computer Vision , 2007 .

[18]  Jian Wang,et al.  Error-resilient region-of-interest video coding , 2005, IEEE Transactions on Circuits and Systems for Video Technology.

[19]  Amir Averbuch,et al.  A region-based MRF model for unsupervised segmentation of moving objects in image sequences , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[20]  Stuart J. Russell,et al.  Image Segmentation in Video Sequences: A Probabilistic Approach , 1997, UAI.

[21]  W. Eric L. Grimson,et al.  Learning Patterns of Activity Using Real-Time Tracking , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[23]  Andrew Blake,et al.  Bi-layer segmentation of binocular stereo video , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[24]  Tan,et al.  Separating reflection components of textured surfaces using a single image , 2003, ICCV 2003.

[25]  L. Wixson.  Detecting Salient Motion by Accumulating Directionally-Consistent Flow , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Nanning Zheng,et al.  Stereo Matching Using Belief Propagation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Qi Tian,et al.  Statistical modeling of complex backgrounds for foreground object detection , 2004, IEEE Transactions on Image Processing.

[28]  Stan Z. Li,et al.  Markov Random Field Modeling in Image Analysis , 2001, Computer Science Workbench.

[29]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  Vladimir Kolmogorov,et al.  Multi-camera Scene Reconstruction via Graph Cuts , 2002, ECCV.