https://romisatriawahono.net/lecture/rm/survey/computer%20vision/Mukhopadhyay%20-%20Hough%20Transform%20-%202014.pdf

A survey of Hough Transform

Robust Detection of Lines Using the Progressive Probabilistic Hough Transform

A clustering algorithm based on the minimum volume ellipsoid (MVE) robust estimator is proposed. The MVE estimator identifies the least volume region containing h percent of the data points. The clustering algorithm iteratively partitions the space into clusters without prior information about their number. At each iteration, the MVE estimator is applied several times with values of h decreasing from 0.5. A cluster is hypothesized for each ellipsoid. The shapes of these clusters are compared with shapes corresponding to a known unimodal distribution by the Kolmogorov-Smirnov test. The best fitting cluster is then removed from the space, and a new iteration starts. Constrained random sampling keeps the computation low. The clustering algorithm was successfully applied to several computer vision problems formulated in the feature space paradigm: multithresholding of gray level images, analysis of the Hough space, and range image segmentation. >

Robust clustering with applications in computer vision

We describe an algorithm for the detection of ellipse shapes in images, using the Randomized Hough Transform. The algorithm is compared to a standard implementation of the Hough Transform, and the Probabilistic Hough Transform. Tests are performed using both noise-free and noisy images, and several real-world images. The algorithm was found to give improvements in accuracy, and a reduction in computation time, memory requirements and the number of false alarms detected.

Technical Report - Randomized Hough Transform: Improved Ellipse Detection with Comparison

Randomized Hough transform: improved ellipse detection with comparison

A probabilistic algorithm for computing Hough transforms

We present a simple combinatorial criterion for determining concept classes that cannot be learned in the sense of Valiant from a polynomial number of positive-only examples. The criterion is applied to several types of Boolean formulae in conjunctive and disjunctive normal form, to the majority function, to graphs with large connected components, and to a neural network with a single threshold unit. All are shown to be nonlearnable from positive-only examples.

/pdf/a-necessary-condition-for-learning-from-positive-examples-1eaezeqzk1.pdf

A Necessary Condition for Learning from Positive Examples

Monte Carlo Hough Transforms

A method is described for deriving rules of inference from relations between probabilities of sentences in Nilsson's probabilistic logic.

Haim Shvaytser

Papers

A probabilistic algorithm for computing Hough transforms

A Necessary Condition for Learning from Positive Examples

Monte Carlo Hough Transforms

Probabilities that imply certainties