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2. Image gradient - Wikipedia

   en.wikipedia.org/wiki/Image_gradient

   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.

3. LightGBM - Wikipedia

   en.wikipedia.org/wiki/LightGBM

   LightGBM, short for Light Gradient-Boosting Machine, is a free and open-source distributed gradient-boosting framework for machine learning, originally developed by Microsoft. [ 4 ] [ 5 ] It is based on decision tree algorithms and used for ranking , classification and other machine learning tasks.

4. Perlin noise - Wikipedia

   en.wikipedia.org/wiki/Perlin_noise

   Perlin noise is a type of gradient noise developed by Ken Perlin in 1983. It has many uses, including but not limited to: procedurally generating terrain , applying pseudo-random changes to a variable, and assisting in the creation of image textures .

5. Static site generator - Wikipedia

   en.wikipedia.org/wiki/Static_site_generator

   The simple design also makes it harder for attackers to modify the website due to the smaller attack surface of these relatively simple backends. Some of the most popular static site generators are Jekyll , Hugo , Eleventy , Gatsby , and Next.js , [ 2 ] [ 3 ] SSGs are typically for rarely-changing, informative content, such as product pages ...

6. Color gradient - Wikipedia

   en.wikipedia.org/wiki/Color_gradient

   A linear, or axial, color gradient. In color science, a color gradient (also known as a color ramp or a color progression) specifies a range of position-dependent colors, usually used to fill a region. In assigning colors to a set of values, a gradient is a continuous colormap, a type of color scheme.

7. Sobel operator - Wikipedia

   en.wikipedia.org/wiki/Sobel_operator

   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.