Conclusion

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Conclusion#

This chapter has introduced the common methods of feature detection:

  • cross-correlation for detecting perfectly known patterns;

  • Canny detector for detecting lines (based on the image gradient);

  • Harris detector for detecting corners (based on the intensity variations in the pixel neighborhood);

  • Hough transform for detecting lines or circles (by transforming the image into a parameter space).

References#

  • H. Bay, T. Tuytelaars, and L. Van Gool, “SURF: Speeded up robust features”, European conference on computer vision, 2006.

  • J. Canny, “A Computational Approach To Edge Detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence}, vol. 8, 1986.

  • C. Harris and M. Stephens “A combined corner and edge detector”, Alvey Vision Conference, p. 147-151, 1988.

  • P.V.C. Hough, “Method and means for recognizing complex patterns”, US Patent 3,069,654, 1962.

  • D.G. Lowe, “Object recognition from local scale-invariant features”, IEEE International Conference on Computer Vision, 1999.

  • D.G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints”, International Journal of Computer Vision, vol. 60, 2004.

  • D. Marr and E. Hildreth, “Theory of Edge Detection”, Proceedings of the Royal Society of London, vol. 207, 1980.

  • H. Moravec, “Obstacle Avoidance and Navigation in the Real World by a Seeing Robot Rover”, technical report, Carnegie–Mellon University, Robotics Institute, 1980.

  • J.M.S. Prewitt, “Object enhancement and extraction”, Picture Processing and Psychopictorics, Academic Press, 1970.

  • I. Rey-Otero and M. Delbracio, “Anatomy of the SIFT Method”, Image Processing On Line, 2014.

  • L.G. Roberts, “Machine Perception Of Three-Dimensional Solids”, Computer Methods in Image Analysis, IEEE Press, 1965.

  • E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “ORB: an efficient alternative to SIFT or SURF”, IEEE International Conference on Computer Vision, 2011.

  • I. Sobel and G. Feldman, “A \(3\times3\) Isotropic Gradient Operator for Image Processing”, In Stanford Artificial Intelligence Project, 1968.

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