Segmentation and Interpretation of Multicolored Objects with Highlights

Abstract This paper presents a segmentation system, based on a general framework for segmentation, that returns not only regions that correspond to coherent surfaces in an image, but also low-level interpretations of those regions' physical characteristics. This system is valid for images of piecewise uniform dielectric objects with highlights, moving it beyond the capabilities of previous physics-based segmentation algorithms which assume uniformly colored objects. This paper presents a summary of the complete system and focuses on two extensions of it that demonstrate its interpretive capacity and applicability to more complex scenes. The first extension provides interpretations of a scene by reasoning about the likelihood of different physical characteristics of simple image regions. The second extension allows the system to handle highlights within the general framework for segmentation. The resulting segmentations and interpretations more closely match our perceptions of objects since the resulting regions correspond to coherent surfaces, even when those surfaces have multiple colors and highlights.

[1]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

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

[3]  Glenn Healey,et al.  Using color for geometry-insensitive segmentation , 1989 .

[4]  Takeo Kanade,et al.  Finding natural clusters through entropy minimization , 1989 .

[5]  R. Bajcsy,et al.  Color image segmentation with detection of highlights and local illumination induced by inter-reflections , 1990, [1990] Proceedings. 10th International Conference on Pattern Recognition.

[6]  Carol L. Novak,et al.  Estimating scene properties by analyzing color histograms with physics-based models , 1992 .

[7]  J. Krumm Space/frequency shape inference and segmentation of textured three-dimensional surfaces , 1993 .

[8]  Steven M. LaValle,et al.  A Bayesian Segmentation Methodology for Parametric Image Models , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Glenn Healey,et al.  Results using random field models for the segmentation of color images of natural scenes , 1995, Proceedings of IEEE International Conference on Computer Vision.

[10]  Steven A. Shafer,et al.  Physics-based segmentation: moving beyond color , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  Steven W. Zucker,et al.  Shadows and shading flow fields , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  S. Shafer,et al.  Segmentation and interpretation using multiple physical hypotheses of image formation , 1996 .

[13]  Steven A. Shafer,et al.  Physics-Based Segmentation of Complex Objects Using Multiple Hypotheses of Image Formation , 1997, Comput. Vis. Image Underst..

[14]  Jorma Rissanen,et al.  Stochastic Complexity in Statistical Inquiry , 1989, World Scientific Series in Computer Science.