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The Hough transform is a feature extraction technique used in image analysis, computer vision, pattern recognition, and digital image processing. [1] [2] The purpose of the technique is to find imperfect instances of objects within a certain class of shapes by a voting procedure.
The generalized Hough transform (GHT), introduced by Dana H. Ballard in 1981, is the modification of the Hough transform using the principle of template matching. [1] The Hough transform was initially developed to detect analytically defined shapes (e.g., line, circle, ellipse etc.). In these cases, we have knowledge of the shape and aim to ...
The Hough transform [3] can be used to detect lines and the output is a parametric description of the lines in an image, for example ρ = r cos(θ) + c sin(θ). [1] If there is a line in a row and column based image space, it can be defined ρ, the distance from the origin to the line along a perpendicular to the line, and θ, the angle of the perpendicular projection from the origin to the ...
Hough transforms are techniques for object detection, a critical step in many implementations of computer vision, or data mining from images. Specifically, the Randomized Hough transform is a probabilistic variant to the classical Hough transform, and is commonly used to detect curves (straight line, circle, ellipse, etc.) [1] The basic idea of Hough transform (HT) is to implement a voting ...
Therefore, one expects that line detection algorithms should successfully detect these lines in practice. Indeed, the following figure demonstrates Hough transform-based line detection applied to a perspective-transformed chessboard image. Clearly, the Hough transform is able to accurately detect the lines induced by the board squares.
He also popularized the use of the generalised hough transform in computer vision in his paper "Generalizing the Hough Transform to Detect Arbitrary Shapes." [ 3 ] He is also known as a proponent of active vision techniques for computer vision systems [ 4 ] as well as approaches to understanding human vision.
If Hough transforms are used to detect lines and ellipses, then thinning could give much better results. If the edge happens to be the boundary of a region, then thinning could easily give the image parameters like perimeter without much algebra. There are many popular algorithms used to do this, one such is described below:
The Hough transform however has a long history of being used in image processing, image analysis and computer vision, and I would therefore not advice to smash this article into a specific instance of the Radon transform -- this would make readers from the image processing, image analysis and computer vision communities complete lost unless ...