Three-dimensional object recognition
ACM Computing Surveys (CSUR) - Annals of discrete mathematics, 24
Model-based recognition in robot vision
ACM Computing Surveys (CSUR)
The representation, recognition, and locating of 3-d objects
International Journal of Robotics Research
HYPER: A New Approach for the Recognition and Positioning of Two-Dimensional Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Localizing Overlapping Parts by Searching the Interpretation Tree
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object recognition and localization via pose clustering
Computer Vision, Graphics, and Image Processing
On the Sensitivity of the Hough Transform for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
The combinatorics of object recognition in cluttered environments using constrained search
Artificial Intelligence
Recognizing solid objects by alignment with an image
International Journal of Computer Vision
On the Verification of Hypothesized Matches in Model-Based Recognition
On the Verification of Hypothesized Matches in Model-Based Recognition
The Combinatorics of Heuristic Search Termination for Object Recognition in Cluttered Environments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Data Structures for Model-Based 3-D Object Recognition and Localization from Range Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Integrated Model for Evaluating the Amount of Data Required for Reliable Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Generic Grouping Algorithm and Its Quantitative Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Grouping-Based Nonadditive Verification
IEEE Transactions on Pattern Analysis and Machine Intelligence
VC-Dimension Analysis of Object Recognition Tasks
Journal of Mathematical Imaging and Vision
Probabilistic 3D Object Recognition
International Journal of Computer Vision
Predicting Performance of Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Termination of Voting in the Probabilistic Circular Hough Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bounds on Shape Recognition Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-based recognition of articulated objects
Pattern Recognition Letters
SoftPOSIT: Simultaneous Pose and Correspondence Determination
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Design Considerations for Generic Grouping in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
SoftPOSIT: Simultaneous Pose and Correspondence Determination
International Journal of Computer Vision
Some Tradeoffs and a New Algorithm for Geometric Hashing
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
A System for Model-Based Recognition of Articulated Objects
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
An A Contrario Decision Method for Shape Element Recognition
International Journal of Computer Vision
A Unified Framework for Detecting Groups and Application to Shape Recognition
Journal of Mathematical Imaging and Vision
Knowledge-based part correspondence
Pattern Recognition
Performance characterization in computer vision: A guide to best practices
Computer Vision and Image Understanding
Adaptive image retrieval based on the spatial organization of colors
Computer Vision and Image Understanding
Computer Vision and Image Understanding
3D Edge Detection by Selection of Level Surface Patches
Journal of Mathematical Imaging and Vision
Case-based object recognition for airborne fungi recognition
Artificial Intelligence in Medicine
CAD-based recognition of 3D objects in monocular images
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Development of fuzzy manifold and fuzzy nearest distance for pose estimation of degraded face images
ACE'10 Proceedings of the 9th WSEAS international conference on Applications of computer engineering
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Model-based recognition methods generally use ad hoc techniques to decide whether or not a model of an object matches a given scene. The most common such technique is to set an empirically determined threshold on the fraction of model features that must be matched to data features. Conditions under which to accept a match as correct are rigorously derived. The analysis is based on modeling the recognition process as a statistical occupancy problem. This model makes the assumption that pairings of object and data features can be characterized as a random process with a uniform distribution. The authors present a number of examples illustrating that real image data are well approximated by such a random process. Using a statistical occupancy model, they derive an expression for the probability that a randomly occurring match will account for a given fraction of the features of a particular object. This expression is a function of the number of model features, the number of data features, and bounds on the degree of sensor noise. It provides a means of setting a threshold such that the probability of a random match is very small.