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  2. Line detection - Wikipedia

    en.wikipedia.org/wiki/Line_detection

    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 ...

  3. Hough transform - Wikipedia

    en.wikipedia.org/wiki/Hough_transform

    Moreover, due to imperfection errors in the edge-detection step, there will usually be errors in the accumulator space, which may make it non-trivial to find the appropriate peaks, and thus the appropriate lines. The final result of the linear Hough transform is a two-dimensional array (matrix) similar to the accumulator—one dimension of this ...

  4. Randomized Hough transform - Wikipedia

    en.wikipedia.org/wiki/Randomized_Hough_Transform

    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 ...

  5. Generalised Hough transform - Wikipedia

    en.wikipedia.org/wiki/Generalised_Hough_transform

    x c = x + r cos(α) y c = y + r sin(α) (2.3) Increase counters (voting): ++A([[x c]][y c]) (3) Possible locations of the object contour are given by local maxima in A[x c][y c]. If A[x c][y c] > T, then the object contour is located at (x c, y c) General case: Suppose the object has undergone some rotation Θ and uniform scaling s: (x ′, y ...

  6. Chessboard detection - Wikipedia

    en.wikipedia.org/wiki/Chessboard_detection

    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.

  7. C string handling - Wikipedia

    en.wikipedia.org/wiki/C_string_handling

    Strings are passed to functions by passing a pointer to the first code unit. Since char * and wchar_t * are different types, the functions that process wide strings are different than the ones processing normal strings and have different names. String literals ("text" in the C source code) are converted to arrays during compilation. [2]

  8. Random sample consensus - Wikipedia

    en.wikipedia.org/wiki/Random_sample_consensus

    A simple example is fitting a line in two dimensions to a set of observations. Assuming that this set contains both inliers, i.e., points which approximately can be fitted to a line, and outliers, points which cannot be fitted to this line, a simple least squares method for line fitting will generally produce a line with a bad fit to the data including inliers and outliers.

  9. Harris corner detector - Wikipedia

    en.wikipedia.org/wiki/Harris_corner_detector

    The Harris corner detector is a corner detection operator that is commonly used in computer vision algorithms to extract corners and infer features of an image. It was first introduced by Chris Harris and Mike Stephens in 1988 upon the improvement of Moravec's corner detector. [1]