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Gradient vector flow (GVF), a computer vision framework introduced by Chenyang Xu and Jerry L. Prince, [1] [2] is the vector field that is produced by a process that smooths and diffuses an input vector field. It is usually used to create a vector field from images that points to object edges from a distance.
The pixels with the largest gradient values in the direction of the gradient become edge pixels, and edges may be traced in the direction perpendicular to the gradient direction. One example of an edge detection algorithm that uses gradients is the Canny edge detector. Image gradients can also be used for robust feature and texture matching.
OpenCV (Open Source Computer Vision Library) is a library of programming functions mainly for real-time computer vision. [2] Originally developed by Intel, it was later supported by Willow Garage, then Itseez (which was later acquired by Intel [3]). The library is cross-platform and licensed as free and open-source software under Apache License ...
For example, if the gradient angle is between 89° and 180°, interpolation between gradients at the north and north-east pixels will give one interpolated value, and interpolation between the south and south-west pixels will give the other (using the conventions of the last paragraph). The gradient magnitude at the central pixel must be ...
Sobel and Feldman presented the idea of an "Isotropic 3 × 3 Image Gradient Operator" at a talk at SAIL in 1968. [1] Technically, it is a discrete differentiation operator , computing an approximation of the gradient of the image intensity function.
XGBoost [2] (eXtreme Gradient Boosting) is an open-source software library which provides a regularizing gradient boosting framework for C++, Java, Python, [3] ...
Elon Musk looks on, in the Oval Office of the White House in Washington, D.C., U.S. - Kevin Lamarque/Reuters
The regularization parameter plays a critical role in the denoising process. When =, there is no smoothing and the result is the same as minimizing the sum of squares.As , however, the total variation term plays an increasingly strong role, which forces the result to have smaller total variation, at the expense of being less like the input (noisy) signal.