Abstract
Since the early days of computer vision research, shape from contour has been one of the most challenging problems. Many researchers in the field have attempted to understand this problem and proposed different approaches to solve it. Shape from contour still remains one of the hardest problems in the field. The problem has two major difficulties. First, 2D properties of contours of viewed objects are generally not sufficient by themselves to uniquely determine 3D shape, as one dimension is lost in the projection. Second, real images produce imperfect contours that make their interpretation particularly difficult. The first problem has received some attention in the research community but in the context of perfect contours. The second one, however, has received very little.
In this work, we propose a promising methodology to address this last problem for a large class of objects: generalized cylinders. It is based on exploiting mathematical invariant properties of the contours of generalized cylinders in a perceptual grouping approach. We show that using these properties greatly helps addressing the figure-ground problem in a more rigorous way than previous (intuitive) perceptual grouping methods. Our approach exploits the interplay between local and global features by handling different levels of the feature hierarchy. We have developed and implemented a method that handles SHGCs in complex seenes with markings and occlusion.
We demonstrate the application of our method of shape description and scene segmentation on complex real images. We also demonstrate the usage of the obtained descriptions for recovery of complete 3-D object centered descriptions of viewed objects from a single intensity image.
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This research was supported by the Advanced Research Projects Agency of the Department of Defense and was monitored by the Air Force Office of Scientific Research under Contract No. F49620-90-C-0078. The United States Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright notation hereon.
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Zerroug, M., Nevatia, R. Volumetric descriptions from a single intensity image. Int J Comput Vision 20, 11–42 (1996). https://doi.org/10.1007/BF00144115
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DOI: https://doi.org/10.1007/BF00144115