Virtual circles: a new set of features for fast image registration

In this paper, we propose a novel set of image features called virtual circles, and their use in an efficient image registration algorithm to find translation and scale differences. A virtual circle is a circle with maximal radius encompassing a background area that does not contain edge points. Virtual circles have a number of nice properties such as the fact that they can be extracted efficiently with the help of the distance transform from many types of images. They can also have extra information, such as their radii, which can be used for efficient registration. Furthermore, virtual circles are robust against broken or corrupt edges. On the other hand, they are vulnerable to background noise, but through the use of a heuristic called the smoothness criterion, virtual circles that are less likely to be corrupted by background noise can be selected. Another advantage of the smoothness criterion is that it reduces the number of virtual circles needed, which increases the efficiency of the algorithm.

[1]  Sidharta Gautama,et al.  Performance analysis of curvature scale space for automatic spatial registration of multisensor shorelines , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[2]  William Rucklidge,et al.  Efficient Visual Recognition Using the Hausdorff Distance , 1996, Lecture Notes in Computer Science.

[3]  William Rucklidge.  Efficient guaranteed search for gray-level patterns , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[4]  Azriel Rosenfeld,et al.  Some experiments in relaxation image matching using corner features , 1983, Pattern Recognit..

[5]  W. J. Rucklidge.  E?cient Computation of the Minimum Hausdorfi Distance for Visual Recognition , 1994 .

[6]  Mohamed S. Kamel,et al.  Multi-resolution image registration using multi-class Hausdorff fraction , 2004 .

[7]  Anil K. Jain,et al.  Registering Landsat images by point matching , 1989 .

[8]  David W. Jacobs.  Optimal matching of planar models in 3D scenes , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  Daniel P. Huttenlocher,et al.  Comparing Images Using the Hausdorff Distance , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  George C. Stockman,et al.  Matching Images to Models for Registration and Object Detection via Clustering , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Wim H. Hesselink,et al.  A General Algorithm for Computing Distance Transforms in Linear Time , 2000, ISMM.

[12]  J. Le Moigne,et al.  First evaluation of automatic image registration methods , 1998, IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174).

[13]  Fang-Hsuan Cheng,et al.  Fast algorithm for point pattern matching: Invariant to translations, rotations and scale changes , 1997, Pattern Recognit..

[14]  Guy Marchal,et al.  Multi-modality image registration by maximization of mutual information , 1996, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.

[15]  Azriel Rosenfeld,et al.  Point pattern matching by relaxation , 1980, Pattern Recognit..

[16]  S. Umeyama,et al.  Least-Squares Estimation of Transformation Parameters Between Two Point Patterns , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Paul A. Viola,et al.  Alignment by Maximization of Mutual Information , 1997, International Journal of Computer Vision.

[18]  Frederic Fol Leymarie,et al.  Fast raster scan distance propagation on the discrete rectangular lattice , 1992, CVGIP Image Underst..

[19]  B. S. Manjunath,et al.  A contour-based approach to multisensor image registration , 1995, IEEE Trans. Image Process..

[20]  P. Danielsson.  Euclidean distance mapping , 1980 .

[21]  Clark F. Olson.  Image registration by aligning entropies , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[22]  A. Ardeshir Goshtasby,et al.  Image registration by local approximation methods , 1988, Image Vis. Comput..

[23]  David M. Mount,et al.  Efficient algorithms for robust feature matching , 1999, Pattern Recognit..

[24]  John I. Goutsias,et al.  Mathematical Morphology and its Applications to Image and Signal Processing , 2000, Computational Imaging and Vision.

[25]  Michael Werman,et al.  Affine point matching , 1994, Pattern Recognit. Lett..

[26]  Xilin Yi,et al.  Line feature-based recognition using Hausdorff distance , 1995, Proceedings of International Symposium on Computer Vision - ISCV.

[27]  Gunilla Borgefors,et al.  Distance transformations in digital images , 1986, Comput. Vis. Graph. Image Process..

[28]  Thomas M. Breuel,et al.  Fast recognition using adaptive subdivisions of transformation space , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[29]  Zhebin Qian,et al.  Use of Hopfield neural network for complex image registration , 1997, Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence.

[30]  Nicholas Ayache,et al.  Rigid, affine and locally affine registration of free-form surfaces , 1996, International Journal of Computer Vision.

[31]  Shimon Ullman,et al.  Recognizing solid objects by alignment with an image , 1990, International Journal of Computer Vision.

[32]  P. Anandan,et al.  Robust multi-sensor image alignment , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[33]  Nasser Kehtarnavaz,et al.  A framework for estimation of motion parameters from range images , 1989, Comput. Vis. Graph. Image Process..

[34]  Siamak Khorram,et al.  Development of a feature-based approach to automated image registration for multitemporal and multisensor remotely sensed imagery , 1997, IGARSS'97. 1997 IEEE International Geoscience and Remote Sensing Symposium Proceedings. Remote Sensing - A Scientific Vision for Sustainable Development.

[35]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

[36]  Lisa M. Brown,et al.  A survey of image registration techniques , 1992, CSUR.