Image processing

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This article/section is a stub — probably a pile of half-sorted notes, is not well-checked so may have incorrect bits. (Feel free to ignore, fix, or tell me)

Feature detection

Where features tend to include:

  • Points -
  • Blobs - smooth areas that won't (necessarily) be detected by point detection. Their approximate centers may also be considered interest points
  • Edges -
    • a relatively one-dimensional feature, though with a direction
  • Corners - Detects things like intersections and ends of sharp lines
    • a relatively two-dimensional kind of feature
  • Ridges

In comparisons between similar images, one should keep in mind that blob-centers can become interest points, gradients can become edges, etc., and that the difference to detectors can be and/or should be fuzzy.

  • Interest point - could be said to group the above and more
    • preferably has a clear definition
    • has a well-defined position
    • preferably quite reproducible, that is, stable under relatively minor image alterations such as scale, rotation, translation, brightness.
    • useful in their direct image context - corners, endpoints, intersections
  • Region of interest


See also:


Edge detection


Interest point / corner detection

  • Harris operator [7]
  • Shi and Tomasi [8]
  • Level curve curvature [9]
  • SUSAN [10]
  • FAST [11]


Blob detection

  • Laplacian of Gaussian (LoG)
  • Difference of Gaussians (DoG)
  • Determinant of Hessian (DoH)
  • Maximally stable extremal regions
  • PCBR


Object detection

Tends to refer to detecting anything more complex than a point, edge, blob, or corner. Regularly by example.

Unsorted

Feature extraction:

  • SIFT (Scale-Invariant Feature Transform)

Unsorted

  • Hough transform
  • Structure tensor
  • SPIN, RIFT (but SIFT usually works better(verify))


(Primarily) supporting transforms

Morphological image processing

https://www.google.com/search?hl=en&q=morphological%20image%20processing

http://www.dspguide.com/ch25/4.htm


Focusing on details or overall image

bandpass, blur, median

For color analysis we often want to focus on the larger blobs and ignore small details. (though in some cases they can fall away in statistics anyway).


Variance image

Each pixel defined by variance in nearby block of pixels


http://siddhantahuja.wordpress.com/2009/06/08/compute-variance-map-of-an-image/


Convolution

Fourier transform

Image analysis

Simpler summaries

Other useful processing

Near-duplicate detection, image similarity, image fingerprinting