Recognizing unexpected objects: a proposed approach
International Journal of Pattern Recognition and Artificial Intelligence
3D curved object recognition from multiple 2D camera views
Computer Vision, Graphics, and Image Processing
The geometry of view space of opaque objects bounded by smooth surfaces
Artificial Intelligence
Winner-take-all networks of O(N) complexity
Advances in neural information processing systems 1
Developing the aspect graph representation for use in image understanding
Proceedings of a workshop on Image understanding workshop
Visibility, occlusion, and the aspect graph
International Journal of Computer Vision
Stimulus Familiarity Determines Recognition Strategy for Novel 3D Objects
Stimulus Familiarity Determines Recognition Strategy for Novel 3D Objects
1994 Special Issue: Modeling visual recognition from neurobiological constraints
Neural Networks - Special issue: models of neurodynamics and behavior
Neural Networks - Special issue: models of neurodynamics and behavior
Pictorial Recognition of Objects Employing Affine Invariance in the Frequency Domain
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of Error in Curvature Computation on Multi-Scale Free-Form Surfaces
International Journal of Computer Vision
A Neural Network Approach to CSG-Based 3-D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Free-Form Surface Description in Multiple Scales: Extension to Incomplete Surfaces
CAIP '99 Proceedings of the 8th International Conference on Computer Analysis of Images and Patterns
Efficient Discriminant Viewpoint Selection for Active Bayesian Recognition
International Journal of Computer Vision
An Adaptive Connectionist Front-End for Automated Fettling
Integrated Computer-Aided Engineering
A diffusion wavelet approach for 3-D model matching
Computer-Aided Design
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The authors address the problem of generating representations of 3-D objects automatically from exploratory view sequences of unoccluded objects. In building the models, processed frames of a video sequence are clustered into view categories called aspects, which represent characteristic views of an object invariant to its apparent position, size, 2-D orientation, and limited foreshortening deformation. The aspects as well as the aspect transitions of a view sequence are used to build (and refine) the 3-D object representations online in the form of aspect-transition matrices. Recognition emerges as the hypothesis that has accumulated the maximum evidence at each moment. The 'winning' object continues to refine its representation until either the camera is redirected or another hypothesis accumulates greater evidence. This work concentrates on 3-D appearance modeling and succeeds under favorable viewing conditions by using simplified processes to segment objects from the scene and derive the spatial agreement of object features.