A survey of the Hough transform
Computer Vision, Graphics, and Image Processing
On the Sensitivity of the Hough Transform for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Elements of information theory
Elements of information theory
An analysis on quantizing the Hough space
Pattern Recognition Letters
The Mathematica book (4th edition)
The Mathematica book (4th edition)
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Shape Detection in Computer Vision Using the Hough Transform
Shape Detection in Computer Vision Using the Hough Transform
Digital Image Processing
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Occlusion Models for Natural Images: A Statistical Study of a Scale-Invariant Dead Leaves Model
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
MINPRAN: A New Robust Estimator for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Fisher-Rao Metric for Projective Transformations of the Line
International Journal of Computer Vision
Application of the Fisher-Rao Metric to Structure Detection
Journal of Mathematical Imaging and Vision
Application of the Fisher-Rao Metric to Ellipse Detection
International Journal of Computer Vision
Analysis of the inclination of elongated biological objects: microtubules
Machine Graphics & Vision International Journal
Using the fisher-rao metric to compute facial similarity
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
A Fisher-Rao Metric for Paracatadioptric Images of Lines
International Journal of Computer Vision
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In many detection problems, the structures to be detected are parameterized by the points of a parameter space. If the conditional probability density function for the measurements is known, then detection can be achieved by sampling the parameter space at a finite number of points and checking each point to see if the corresponding structure is supported by the data. The number of samples and the distances between neighboring samples are calculated using the Rao metric on the parameter space. The Rao metric is obtained from the Fisher information which is, in turn, obtained from the conditional probability density function. An upper bound is obtained for the probability of a false detection. The calculations are simplified in the low noise case by making an asymptotic approximation to the Fisher information. An application to line detection is described. Expressions are obtained for the asymptotic approximation to the Fisher information, the volume of the parameter space, and the number of samples. The time complexity for line detection is estimated. An experimental comparison is made with a Hough transform-based method for detecting lines.