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Object recognition by computer

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Object recognition in computer vision is the task of finding a given object in an image or video sequence. Humans recognize a multitude of objects in images with little effort, despite the fact that the image of the objects may vary somewhat in different view points, in many different sizes / scale or even when they are translated or rotated. Objects can even be recognized when they are partially obstructed from view. This task is still a challenge for computer vision systems in general.

Approaches based on CAD-like object modelsEdit

Edge detection, primal sketch, Marr, Mohan and Nevatia, Lowe, Faugeras

Recognition by partsEdit

Binford (generalized cylinders), Biederman (geons), Dickinson, Forsyth and Ponce

Appearance-based methodsEdit

Histograms: Swain and Ballard, Schiele and Crowley, Schneiderman and Kanade, Linde and Lindeberg, Koenderink and van Doorn, Dalal and Triggs

Approaches based on interest pointsEdit

Scale-invariant feature transformEdit

David Lowe pioneered the computer vision approach to extracting and using scale-invariant SIFT features from images to perform reliable object recognition.

SURFEdit

See also: SURF

Bag of words representationsEdit

Other approachesEdit

Template matching, gradient histograms, intraclass transfer learning, explicit and implicit 3D object models, global scene representations, shading, reflectance, texture, grammars, topic models, biologically inspired object recognition[1]

Window-based detection, 3D cues, context, leveraging internet data, unsupervised learning, fast indexing[2]

ApplicationsEdit

Object recognition methods has the following applications:

  • Image panoramas[3]
  • Image watermarking[4]
  • Global robot localization[5]

SurveysEdit

Daniilides and Eklundh, Edelman

See alsoEdit

ReferencesEdit

  1. 6.870 Object Recognition and Scene Understanding
  2. CS395T: Visual Recognition and Search
  3. Brown, M., and Lowe, D.G., "Recognising Panoramas," ICCV, p. 1218, Ninth IEEE International Conference on Computer Vision (ICCV'03) - Volume 2, Nice,France, 2003
  4. Li, L., Guo, B., and Shao, K., " Geometrically robust image watermarking using scale-invariant feature transform and Zernike moments," Chinese Optics Letters, Volume 5, Issue 6, pp. 332-335, 2007.
  5. Se,S., Lowe, D.G., and Little, J.J.,"Vision-based global localization and mapping for mobile robots", IEEE Transactions on Robotics, 21, 3 (2005), pp. 364-375.


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