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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 models[edit | edit source]

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

Recognition by parts[edit | edit source]

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

Appearance-based methods[edit | edit source]

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

Approaches based on interest points[edit | edit source]

Scale-invariant feature transform[edit | edit source]

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

SURF[edit | edit source]

See also: SURF

Bag of words representations[edit | edit source]

Other approaches[edit | edit source]

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]

Applications[edit | edit source]

Object recognition methods has the following applications:

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

Surveys[edit | edit source]

Daniilides and Eklundh, Edelman

See also[edit | edit source]

References[edit | edit source]

  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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