Robust regression and outlier detection
Robust regression and outlier detection
A survey of the Hough transform
Computer Vision, Graphics, and Image Processing
A new curve detection method: randomized Hough transform (RHT)
Pattern Recognition Letters
On the Sensitivity of the Hough Transform for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A probabilistic Hough transform
Pattern Recognition
Recognizing solid objects by alignment with an image
International Journal of Computer Vision
A probabilistic algorithm for computing Hough transforms
Journal of Algorithms
CVGIP: Image Understanding
A study of affine matching with bounded sensor error
International Journal of Computer Vision
Robust and Efficient Detection of Salient Convex Groups
IEEE Transactions on Pattern Analysis and Machine Intelligence
Polynomial-Time Geometric Matching for Object Recognition
International Journal of Computer Vision
Efficient Pose Clustering Using a Randomized Algorithm
International Journal of Computer Vision
An approximation algorithm for least median of squares regression
Information Processing Letters
Uncertainty Propagation in Model-Based Recognition
International Journal of Computer Vision
Constrained Hough transforms for curve detection
Computer Vision and Image Understanding
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
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Popular algorithms for feature matching and model extraction fall into two broad categories, generate-and-test and Hough transform variations. However, both methods suffer from problems in practical implementations. Generate-and-test methods are sensitive to noise in the data. They often fail when the generated model fit is poor due to error in the selected features. Hough transform variations are somewhat less sensitive the noise, but implementations for complex problems suffer from large time and space requirements and the detection of false positives. This paper describes a general method for solving problems where a model is extracted from or fit to data that draws benefits from both generate-and-test methods and those based on the Hough transform, yielding a method superior to both. An important component of the method is the subdivision of the problem into many subproblems. This allows efficient generate-and-test techniques to be used, including the use of randomization to limit the number of subproblems that must be examined. However, the subproblems are solved using pose space analysis techniques similar to the Hough transform, which lowers the sensitivity of the method to noise. This strategy is easy to implement and results in practical algorithms that are efficient and robust. We apply this method to object recognition, geometric primitive extraction, robust regression, and motion segmentation.