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  2. List of datasets in computer vision and image processing

    en.wikipedia.org/wiki/List_of_datasets_in...

    Images with pixel annotations for eight object categories: fish (vertebrates), reefs (invertebrates), aquatic plants, wrecks/ruins, human divers, robots, and sea-floor. 1,635 Images Segmentation 2020 [174] Md Jahidul Islam et al. LIACI Dataset Images have been collected during underwater ship inspections and annotated by human domain experts.

  3. Kernel (image processing) - Wikipedia

    en.wikipedia.org/wiki/Kernel_(image_processing)

    In image processing, a kernel, convolution matrix, or mask is a small matrix used for blurring, sharpening, embossing, edge detection, and more.This is accomplished by doing a convolution between the kernel and an image.

  4. OpenCV - Wikipedia

    en.wikipedia.org/wiki/OpenCV

    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]).

  5. Canny edge detector - Wikipedia

    en.wikipedia.org/wiki/Canny_edge_detector

    The Canny algorithm contains a number of adjustable parameters, which can affect the computation time and effectiveness of the algorithm. The size of the Gaussian filter: the smoothing filter used in the first stage directly affects the results of the Canny algorithm. Smaller filters cause less blurring, and allow detection of small, sharp lines.

  6. Image derivative - Wikipedia

    en.wikipedia.org/wiki/Image_derivative

    Image derivatives can be computed by using small convolution filters of size 2 × 2 or 3 × 3, such as the Laplacian, Sobel, Roberts and Prewitt operators. [1] However, a larger mask will generally give a better approximation of the derivative and examples of such filters are Gaussian derivatives [ 2 ] and Gabor filters . [ 3 ]

  7. 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]

  8. Connected-component labeling - Wikipedia

    en.wikipedia.org/wiki/Connected-component_labeling

    Multi-pass algorithms also exist, some of which run in linear time relative to the number of image pixels. [16] In the early 1990s, there was considerable interest in parallelizing connected-component algorithms in image analysis applications, due to the bottleneck of sequentially processing each pixel. [17]

  9. Box blur - Wikipedia

    en.wikipedia.org/wiki/Box_blur

    An example of an image blurred using a box blur. A box blur (also known as a box linear filter) is a spatial domain linear filter in which each pixel in the resulting image has a value equal to the average value of its neighboring pixels in the input image. It is a form of low-pass ("blurring") filter.