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  2. Histogram matching - Wikipedia

    en.wikipedia.org/wiki/Histogram_matching

    An example of histogram matching. In image processing, histogram matching or histogram specification is the transformation of an image so that its histogram matches a specified histogram. [1] The well-known histogram equalization method is a special case in which the specified histogram is uniformly distributed. [2]

  3. Bag-of-words model in computer vision - Wikipedia

    en.wikipedia.org/wiki/Bag-of-words_model_in...

    Pyramid match kernel [13] is a fast algorithm (linear complexity instead of classic one in quadratic complexity) kernel function (satisfying Mercer's condition) which maps the BoW features, or set of features in high dimension, to multi-dimensional multi-resolution histograms. An advantage of these multi-resolution histograms is their ability ...

  4. Scale-invariant feature transform - Wikipedia

    en.wikipedia.org/wiki/Scale-invariant_feature...

    The scale-invariant feature transform (SIFT) is a computer vision algorithm to detect, describe, and match local features in images, invented by David Lowe in 1999. [1] Applications include object recognition , robotic mapping and navigation, image stitching , 3D modeling , gesture recognition , video tracking , individual identification of ...

  5. Histogram equalization - Wikipedia

    en.wikipedia.org/wiki/Histogram_equalization

    Histogram equalization will work the best when applied to images with much higher color depth than palette size, like continuous data or 16-bit gray-scale images. There are two ways to think about and implement histogram equalization, either as image change or as palette change.

  6. Image color transfer - Wikipedia

    en.wikipedia.org/wiki/Image_color_transfer

    An example of an algorithm that employs the statistical properties of the images is histogram matching. This is a classic algorithm for color transfer, but it can suffer from the problem that it is too precise so that it copies very particular color quirks from the target image, rather than the general color characteristics, giving rise to ...

  7. Histogram of oriented gradients - Wikipedia

    en.wikipedia.org/wiki/Histogram_of_oriented...

    The histogram of oriented gradients (HOG) is a feature descriptor used in computer vision and image processing for the purpose of object detection. The technique counts occurrences of gradient orientation in localized portions of an image.

  8. I'm a lifelong skier. There are 6 mistakes I always see ... - AOL

    www.aol.com/im-lifelong-skier-6-mistakes...

    I'm an experienced skier who has been to more than a dozen ski resorts around the world.. I often see beginners make mistakes such as holding up lines, tailgating, and zooming down slopes. Wearing ...

  9. Local binary patterns - Wikipedia

    en.wikipedia.org/wiki/Local_binary_patterns

    OpenCV's Cascade Classifiers support LBPs as of version 2. VLFeat , an open source computer vision library in C (with bindings to multiple languages including MATLAB) has an implementation . LBPLibrary is a collection of eleven Local Binary Patterns (LBP) algorithms developed for background subtraction problem.