Computer Vision, Graphics, and Image Processing
A Multiresolution Hierarchical Approach to Image Segmentation Based on Intensity Extrema
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust regression methods for computer vision: a review
International Journal of Computer Vision
Scale and segmentation of grey-level images using maximum gradient paths
Image and Vision Computing - Special issue: information processing in medical imaging 1991
Robust Parameter Estimation in Computer Vision
SIAM Review
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
Empirical Evaluation Techniques in Computer Vision
Empirical Evaluation Techniques in Computer Vision
Affine/ Photometric Invariants for Planar Intensity Patterns
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
A new point matching algorithm for non-rigid registration
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Visual and Spatial Analysis
Using Catastrophe Theory to Derive Trees from Images
Journal of Mathematical Imaging and Vision
Integral Invariants for Shape Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Methodology for Automated Vector-to-Image Registration
AIPR '07 Proceedings of the 36th Applied Imagery Pattern Recognition Workshop
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This paper analyzes the theoretical foundations of the invariant, robust, and stable methods in computer vision applications. Some studies had shown that many known invariants used in pattern recognition algorithms are not robust to small changes in images and robust parameters of these algorithms are not invariant. We provide a conceptual framework for new studies on invariance, robustness, and stability of computer vision algorithms critical for applications. Based on this theoretical analysis new invariant, robust and stable methods suited for the complexity of computer vision tasks, can be designed.