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Image Representation in Computer Vision

Source : https://medium.com/@sumit-kr-sharma/image-representation-in-computer-vision-364e47c4e69a

Techniques

  • Grayscale/Luminance Representation
  • Color Representation
  • Feature Extraction : Edges, corners, textures, or more complex representations like HOG (Histograms of Oriented Gradients) or deep features from CNNs.
  • Histograms : Image histograms represent the frequency distribution of pixel intensities in an image. They can provide valuable information about the image's contrast, brightness, and overall content.
  • Local Descriptors : Representations that describe local regions of an image, such as SIFT (Scale-Invariant Feature Transform) and SURF (Speeded-Up Robust Features)
  • Global Descriptors : ?
  • Deep Learning-based Representations : ?