On Geometric Hashing and the Generalized Hough Transform

The generalized Hough transform and geometric hashing are two contemporary paradigms for model-based object recognition. Both schemes simultaneously find instances of objects in a scene and determine the location and orientation of these instances. The methods encode the models for the objects in a similar fashion and object recognition is achieved by image features "voting" for object models. For both schemes, the object recognition time is largely independent of the number of objects that are encoded in the object-model database. This paper puts the two schemes in perspective and examines differences and similarities. The authors also study object representation techniques and discuss how object representations are used for object recognition and position estimation. >

[1]  Richard O. Duda,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.

[2]  David J. Farber,et al.  A Parallel Mechanism for Detecting Curves in Pictures , 1975, IEEE Transactions on Computers.

[3]  Jack Sklansky,et al.  Finding circles by an array of accumulators , 1975, Commun. ACM.

[4]  Dana H. Ballard,et al.  Generalizing the Hough transform to detect arbitrary shapes , 1981, Pattern Recognit..

[5]  Dana H. Ballard,et al.  Computer Vision , 1982 .

[6]  Thomas O. Binford,et al.  Survey of Model-Based Image Analysis Systems , 1982 .

[7]  Dana H. Ballard,et al.  Viewer Independent Shape Recognition , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Ramesh C. Jain,et al.  Three-dimensional object recognition , 1985, CSUR.

[9]  G. Stockman.  THREE-DIMENSIONAL POSE COMPUTATIONS FROM MULTIPLE VIEWS , 1986 .

[10]  Charles R. Dyer,et al.  Model-based recognition in robot vision , 1986, CSUR.

[11]  Edith Schonberg,et al.  Two-Dimensional, Model-Based, Boundary Matching Using Footprints , 1986 .

[12]  Micha Sharir,et al.  Identification of Partially Obscured Objects in Two and Three Dimensions by Matching Noisy Characteristic Curves , 1987 .

[13]  Yehezkel Lamdan,et al.  Object recognition by affine invariant matching , 2011, Proceedings CVPR '88: The Computer Society Conference on Computer Vision and Pattern Recognition.

[14]  Yehezkel Lamdan,et al.  On recognition of 3-D objects from 2-D images , 2011, Proceedings. 1988 IEEE International Conference on Robotics and Automation.

[15]  Josef Kittler,et al.  A survey of the hough transform , 1988, Comput. Vis. Graph. Image Process..

[16]  Haim J. Wolfson,et al.  An improved model-based matching method using footprints , 1988, [1988 Proceedings] 9th International Conference on Pattern Recognition.

[17]  Yehezkel Lamdan,et al.  Geometric Hashing: A General And Efficient Model-based Recognition Scheme , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[18]  J. Schwartz,et al.  Object recognition by geometric hashing , 1989 .

[19]  Haim J. Wolfson.  On curve matching , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  W. Eric L. Grimson,et al.  On the sensitivity of geometric hashing , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[21]  W. Eric L. Grimson,et al.  On the Sensitivity of the Hough Transform for Object Recognition , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Robert M. Haralick,et al.  Optimal affine-invariant point matching , 1990, [1990] Proceedings. 10th International Conference on Pattern Recognition.

[23]  Ruud M. Bolle,et al.  Invariant feature matching in parameter space with application to line features , 1991, Optics & Photonics.