Numerical recipes: the art of scientific computing
Numerical recipes: the art of scientific computing
ACM Transactions on Mathematical Software (TOMS)
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Statistical object recognition
Statistical object recognition
Geometric computation for machine vision
Geometric computation for machine vision
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Statistical Approaches to Feature-Based Object Recognition
International Journal of Computer Vision
Statistical methods for speech recognition
Statistical methods for speech recognition
Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling
International Journal of Computer Vision
Training Hidden Markov Models with Multiple Observations-A Combinatorial Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Optimization for Geometric Computation: Theory and Practice
Statistical Optimization for Geometric Computation: Theory and Practice
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Robot Vision
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
Three-Dimensional Object Recognition Systems
Three-Dimensional Object Recognition Systems
Hidden Markov Models for Speech Recognition
Hidden Markov Models for Speech Recognition
Uncertainty Minimization in the Localization of Polyhedral Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the International Workshop on Object Representation in Computer Vision II
ECCV '96 Proceedings of the International Workshop on Object Representation in Computer Vision II
Statistical Modeling of Relations for 3-D Object Recognition
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
Learning to recognize objects in images: acquiring and using probabilistic models of appearance
Learning to recognize objects in images: acquiring and using probabilistic models of appearance
Transinformation for Active Object Recognition
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Applied Pattern Recognition.
3D target recognition using cooperative feature map binding under Markov Chain Monte Carlo
Pattern Recognition Letters
Gesture recognition with a Time-Of-Flight camera
International Journal of Intelligent Systems Technologies and Applications
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This paper introduces a uniform statistical framework for both 3-D and 2-D object recognition using intensity images as input data. The theoretical part provides a mathematical tool for stochastic modeling. The algorithmic part introduces methods for automatic model generation, localization, and recognition of objects. 2-D images are used for learning the statistical appearance of 3-D objects; both the depth information and the matching between image and model features are missing for model generation. The implied incomplete data estimation problem is solved by the Expectation Maximization algorithm. This leads to a novel class of algorithms for automatic model generation from projections. The estimation of pose parameters corresponds to a non-linear maximum likelihood estimation problem which is solved by a global optimization procedure. Classification is done by the Bayesian decision rule. This work includes the experimental evaluation of the various facets of the presented approach. An empirical evaluation of learning algorithms and the comparison of different pose estimation algorithms show the feasibility of the proposed probabilistic framework.