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The circle Hough Transform (CHT) is a basic feature extraction technique used in digital image processing for detecting circles in imperfect images. The circle candidates are produced by “voting” in the Hough parameter space and then selecting local maxima in an accumulator matrix.
Example 1. For each data point, a number of lines are plotted going through it, all at different angles. These are shown here in different colours. The Hough transform accumulates contributions from all pixels in the detected edge. To each line, a support line exists which is perpendicular to it and which intersects the origin. In each case ...
The watershed transformation treats the image it operates upon like a topographic map, with the brightness of each point representing its height, and finds the lines that run along the tops of ridges. There are different technical definitions of a watershed.
The problem of finding the object (described with a model) in an image can be solved by finding the model's position in the image. With the generalized Hough transform, the problem of finding the model's position is transformed to a problem of finding the transformation's parameter that maps the model into the image.
Bresenham's line algorithm is a line drawing algorithm that determines the points of an n-dimensional raster that should be selected in order to form a close approximation to a straight line between two points.
Image rectification in GIS converts images to a standard map coordinate system. This is done by matching ground control points (GCP) in the mapping system to points in the image. These GCPs calculate necessary image transforms. [11] Primary difficulties in the process occur when the accuracy of the map points are not well known
Single color line drawing algorithms involve drawing lines in a single foreground color onto a background. They are well-suited for usage with monochromatic displays. The starting point and end point of the desired line are usually given in integer coordinates, so that they lie directly on the points considered by the algorithm.
Semi-global matching (SGM) is a computer vision algorithm for the estimation of a dense disparity map from a rectified stereo image pair, introduced in 2005 by Heiko Hirschmüller while working at the German Aerospace Center. [1]