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We address the inference of 3-D segmented descriptions of complex objects from a single intensity image. Our approach is based on the analysis of the projective properties of a small number of generalized cylinder primitives and their relationships in the image which make up common man-made objects. Past work on this problem has either assumed perfect contours as input or used 2-dimensional shape...
There has been much interest recently in using invariant theory in computer vision. Most work has concentrated on recognition of 3-D objects from 2-D images using algebraic or differential invariants. In this work, we address the usage of a class of projective invariants and quasi-invariants for the segmentation and 3-D recovery of generalized cylinders from a monocular image. We derive important...
We discuss the issues and challenges in the development of generic object recognition systems. We argue that high-level, volumetric part-based, descriptions are essential if we want to recognize objects which are similar but not identical to pre-stored models, under wide viewing conditions, and to automatically learn new models and add them to our knowledge base.We discuss the representation scheme...
We address the problem of scene segmentation and shape recovery from a single real intensity image. Solving this problem is central to obtaining 3-D scene descriptions in realistic applications where perfect data cannot be obtained and only one image is available. The method we propose addresses a large class of generic shapes, namely straight homogeneous generalized cylinders (SHGCs). It consists...