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.


Feature extraction:

  • SIFT (Scale-Invariant Feature Transform)


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