Image processing

From Helpful
Revision as of 16:56, 21 June 2012 by Helpful (Talk | contribs)

Jump to: navigation, search
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

  • Canny [1]
  • Differential [2]
  • Canny-Deriche [3]
  • Prewitt [4]
  • Roberts Cross operator [5]
  • Sobel [6]
  • Scharr operator


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

Other useful processing

Near-duplicate detection, image similarity, image fingerprinting

(Near-)duplicate detection is generally defined as detecting mild variations coming from one or more of:

  • Common image/video editing operations:
    • Crops - digital crops (often mostly of the less interesting areas) often up to half of the original
    • Resizes - different resolution variations of the same image (includes resampling inaccuracies)
    • Aspect ratio changes - particularly on TV material
    • Mild color changes - contrast changes, implied changes from color space conversion

And, in some applications:

  • Camera angles - different cameras taking images of the same thing (consider TV coverage from various networks). Also images from the same camera a short time apart
  • Camera settings - such as color, brightness, exposure.
  • Added borders
  • mild noise


It can be a simple task - at least, much simpler than sub-image detection, more arbitrary image comparison, feature detection.

When you're, say, only interested in removing some almost duplicate wallpapers in your selection you deal with little more than rescales and crops, and perhaps some color changes. These can be covered with relatively simple methods.


See also:


Image analysis

Simpler summaries

Histograms

Unsorted