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Edge Detection is an important process in image processing and computer vision. Edges describe the boundaries of objects, which is why they play a key role in many shape recognition algorithms. This section lists various contour detection methods that are often part of solving computer vision problems. Like gradient detection, zero crossing, etc.
The Prewitt operator is one of the first algorithms for edge detection by gradient transform. It is developed by Judith M. S. Prewitt and is a popular tool for image […]
Prewitt operator for edge detection Read More »
Harris Corner Detector is a popular computer vision algorithm used to detect key points in images and video. Corners are important features of the image, as they provide useful information
Harris Corner Detector: How to find key-points in pictures Read More »
Gradient non-maximum suppression is an edge thinning algorithm that extracts thin contours of objects in an image. The output of this image processing algorithm is contour curves that are one
Gradient Non-Maximum Suppression Read More »
The Sobel filter (also called Sobel-Feldman operator) is an edge detection algorithm, that results in image emphasizing edges. Computer vision apps often use this image processing technique to extract the
Sobel-Feldman filter: Edge Detection and Gradient Extraction Read More »
The Median filter is a nonlinear noise reduction technique that is widely used in image processing. It is very effective in cases of salt and paper noise (impulsive noise) and
Median Filter – edge preserving filter tutorial Read More »
The Hough Transform is a method to find shapes in an image. The classical transformation is initially designed to identify lines in the image. Later the transform extends to identify
Hough Transform – Basic Shape Detection Read More »
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