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| Feature detection | |
|---|---|
 Output of a typical corner detection algorithm |
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| Edge detection | |
| Canny | |
| Canny-Deriche | |
| Differential | |
| Sobel | |
| Interest point detection | |
| Corner detection | |
| Harris operator | |
| Shi and Tomasi | |
| Level curve curvature | |
| SUSAN | |
| FAST | |
| Blob detection | |
| Laplacian of Gaussian (LoG) | |
| Difference of Gaussians (DoG) | |
| Determinant of Hessian (DoH) | |
| Maximally stable extremal regions | |
| Ridge detection | |
| Affine invariant feature detection | |
| Affine shape adaptation | |
| Harris affine | |
| Hessian affine | |
| Feature description | |
| SIFT | |
| SURF | |
| GLOH | |
| LESH | |
| Scale-space | |
| Scale-space axioms | |
| Implementation details | |
| Pyramids | |
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.
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Approaches based on CAD-like object models
Edge detection, primal sketch, Marr, Mohan and Nevatia, Lowe, Faugeras
Recognition by parts
Binford (generalized cylinders), Biederman (geons), Dickinson, Forsyth and Ponce
Appearance-based methods
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
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
Bag of words representations
Other approaches
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
Object recognition methods has the following applications:
Surveys
Daniilides and Eklundh, Edelman
See also
- 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
- ^ 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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