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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.
Contents
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]
- See also: Scale-invariant feature transform
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]
- See also: Bag of words model in computer vision
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:
Surveys[edit | edit source]
Daniilides and Eklundh, Edelman
See also[edit | edit source]
- 3D single object recognition
- Scale-invariant feature transform (SIFT)
- SURF
- Histogram of oriented gradients
- Boosting methods for object categorization
- Bag of words model in computer vision
References[edit | edit source]
- ↑ 6.870 Object Recognition and Scene Understanding
- ↑ CS395T: Visual Recognition and Search
- ↑ 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
- ↑ 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.
- ↑ 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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