Model-based image matching using location
Model-based image matching using location
Three-dimensional object recognition
ACM Computing Surveys (CSUR) - Annals of discrete mathematics, 24
The combinatorics of local constraints in model-based recognition and localization from sparse data
Journal of the ACM (JACM)
Congruence, similarity and symmetries of geometric objects
Discrete & Computational Geometry - ACM Symposium on Computational Geometry, Waterloo
On the Sensitivity of the Hough Transform for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learnability by fixed distributions
COLT '88 Proceedings of the first annual workshop on Computational learning theory
Object recognition by computer: the role of geometric constraints
Object recognition by computer: the role of geometric constraints
An Efficiently Computable Metric for Comparing Polygonal Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Verification of Hypothesized Matches in Model-Based Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Elements of information theory
Elements of information theory
Introduction to the Special Issue on Interpretation of 3-D Scenes-Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
A Study of Affine Matching With Bounded Sensor Error
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Limitations of Non Model-Based Recognition Schemes
Limitations of Non Model-Based Recognition Schemes
An Integrated Model for Evaluating the Amount of Data Required for Reliable Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Grouping-Based Nonadditive Verification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Localization vs. Identification of Semi-Algebraic Sets
Machine Learning
VC-Dimension Analysis of Object Recognition Tasks
Journal of Mathematical Imaging and Vision
Predicting Performance of Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information-Theoretic Bounds on Target Recognition Performance Based on Degraded Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ambiguity distance: an edge evaluation measure using fuzziness of edges
Fuzzy Sets and Systems - Information processing
Performance characterization in computer vision: A guide to best practices
Computer Vision and Image Understanding
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The localization and the recognition tasks are analyzed here relying on a probabilistic model, and independently of the recognition method used. Rigorous upper and lower bounds on the probability that a set of measurements is sufficient to localize an object within a certain precision, are derived. The bounds quantify the difficulty of the localization task regarding many of its aspects, including the number of measurements, the uncertainty in their position, the information they reveal, and the 驴ability of the objects to confuse the recognizer.驴 Similar results are obtained for the recognition task. The asymptotic difficulty of recognition/localization tasks is characterized by a single parameter, thus making it possible to compare between different tasks. The bounds provide a theoretical benchmark to which experimentally measured performance of localization/recognition methods can be compared.