Three-dimensional object recognition
ACM Computing Surveys (CSUR) - Annals of discrete mathematics, 24
Object recognition and localization via pose clustering
Computer Vision, Graphics, and Image Processing
Object recognition by computer: the role of geometric constraints
Object recognition by computer: the role of geometric constraints
Recognizing solid objects by alignment with an image
International Journal of Computer Vision
Space and Time Bounds on Indexing 3D Models from 2D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Robot Vision Using a Feature Search Strategy Generated from a 3D Oobject Model
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Surface Parametrization and Curvature Measurement of Arbitrary 3-D Objects: Five Practical Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
A mixture model for pose clustering
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
MLESAC: a new robust estimator with application to estimating image geometry
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
Model-Based Object Recognition by Geometric Hashing
ECCV '90 Proceedings of the First European Conference on Computer Vision
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The problem of searching for a model-based scene interpretation is analyzed within a probabilistic framework. Object models are formulated as generative models for range data of the scene. A new statistical criterion, the truncated object probability, is introduced to infer an optimal sequence of object hypotheses to be evaluated for their match to the data. The truncated probability is partly determined by prior knowledge of the objects and partly learned from data. Some experiments on sequence quality and object segmentation and recognition from stereo data are presented. The article recovers classic concepts from object recognition (grouping, geometric hashing, alignment) from the probabilistic perspective and adds insight into the optimal ordering of object hypotheses for evaluation. Moreover, it introduces point-relation densities, a key component of the truncated probability, as statistical models of local surface shape.