Segmentation through Variable-Order Surface Fitting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovery of Parametric Models from Range Images: The Case for Superquadrics with Global Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic 3D Models with Local and Global Deformations: Deformable Superquadrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Closed-Form Solutions for Physically Based Shape Modeling and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Uncertainty to Visual Exploration
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Finding the parts of objects in range images
Computer Vision and Image Understanding
Autonomous Exploration: Driven by Uncertainty
IEEE Transactions on Pattern Analysis and Machine Intelligence
Darboux Frames, Snakes, and Super-Quadrics: Geometry from the Bottom Up
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Object Recognition: Looking for Differences
International Journal of Computer Vision - Special issue: Research at McGill University
On the Sequential Accumulation of Evidence
International Journal of Computer Vision - Special issue: Research at McGill University
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Many strategies in computer vision assume the existence of general purpose models that can be used to characterize a scene or environment at various levels of abstraction. The usual assumptions are that a selected model is competent to describe a particular attribute and that the parameters of this model can be estimated by interpreting the input data in an appropriate manner (e.g., location of lines and edges, segmentation into parts or regions, etc.). This paper considers the problem of how to determine when those assumptions break down. The traditional approach is to use statistical misfit measures based on an assumed sensor noise model. The problem is that correct operation often depends critically on the correctness of the noise model. Instead, we show how this can be accomplished with a minimum of a priori knowledge and within the framework of an active approach which builds a description of environment structure and noise over several viewpoints.