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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 ...
Free software under MIT/X11 license. Measurement Studio is a commercial integrated suite UI controls and class libraries for use in developing test and measurement applications. The analysis class libraries provide various digital signal processing, signal filtering, signal generation, peak detection, and other general mathematical functionality.
The Gupta-Sproull algorithm is based on Bresenham's line algorithm but adds antialiasing. An optimized variant of the Gupta-Sproull algorithm can be written in pseudocode as follows: DrawLine(x1, x2, y1, y2) { x = x1; y = y1; dx = x2 − x1; dy = y2 − y1; d = 2 * dy − dx; // discriminator
Notice that the points (2,1) and (2,3) are on opposite sides of the line and (,) evaluates to positive or negative. A line splits a plane into halves and the half-plane that has a negative (,) can be called the negative half-plane, and the other half can be called the positive half-plane. This observation is very important in the remainder of ...
The simpledtw Python library implements the classic O(NM) Dynamic Programming algorithm and bases on Numpy. It supports values of any dimension, as well as using custom norm functions for the distances. It is licensed under the MIT license. The tslearn Python library implements DTW in the time-series context.
In contrast to the classic SIFT approach, Wagner et al. use the FAST corner detector for feature detection. The algorithm also distinguishes between the off-line preparation phase where features are created at different scale levels and the on-line phase where features are only created at the current fixed scale level of the phone's camera image.
Template matching [1] is a technique in digital image processing for finding small parts of an image which match a template image. It can be used for quality control in manufacturing, [2] navigation of mobile robots, [3] or edge detection in images.
In optimization, line search is a basic iterative approach to find a local minimum of an objective function:. It first finds a descent direction along which the objective function f {\displaystyle f} will be reduced, and then computes a step size that determines how far x {\displaystyle \mathbf {x} } should move along that direction.