Efficient Implementation of Image Registration Based on Feature Tracking

Image registration is a process of overlaying various images of a scene. This process has four basic steps. In this work we have replaced a feature matching step with efficient and fast feature tracking. Several problems, arose due to this technique, are discussed and their solutions are proposed. The proposed algorithm is then tested on various sequences of images.

[1]  M. Helm,et al.  Towards Automatic Rectification Of Satellite Images Using Feature Based Matching , 1991, [Proceedings] IGARSS'91 Remote Sensing: Global Monitoring for Earth Management.

[2]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[3]  Robert C. Bolles,et al.  Parametric Correspondence and Chamfer Matching: Two New Techniques for Image Matching , 1977, IJCAI.

[4]  Tamim Asfour,et al.  Combining Harris interest points and the SIFT descriptor for fast scale-invariant object recognition , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Muhammad Usman Karim Khan,et al.  Real Time Object Tracking in a Video Sequence Using a Fixed Point DSP , 2008, ISVC.

[6]  Yuan C. Hsieh,et al.  Performance Evaluation of Scene Registration and Stereo Matching for Artographic Feature Extraction , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Monika Sester,et al.  DEFINITION OF GROUND-CONTROL FEATURES FOR IMAGE REGISTRATION USING GIS-DATA , 2007 .

[8]  Matthijs C. Dorst Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[9]  A. Ardeshir Goshtasby,et al.  Point pattern matching using convex hull edges , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[10]  B. S. Manjunath,et al.  Registration Techniques for Multisensor Remotely Sensed Imagery , 1996 .

[11]  Wen-Hao Wang,et al.  Image registration by control points pairing using the invariant properties of line segments , 1997, Pattern Recognit. Lett..

[12]  Muhammad Usman Karim Khan,et al.  A Swift and Memory Efficient Hough Transform for Systems with Limited Fast Memory , 2009, ICIAR.

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

[14]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[15]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[16]  Josef Kittler,et al.  Matching and Recognition of Road Networks from Aerial Images , 1992, ECCV.

[17]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[18]  Satyabroto Sinha,et al.  Invariance of stereo images via the theory of complex moments , 1997, Pattern Recognit..

[19]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[20]  Edwin R. Hancock,et al.  Multiple line-template matching with the EM algorithm , 1997, Pattern Recognit. Lett..

[21]  Georgios S. Paschos,et al.  Perceptually uniform color spaces for color texture analysis: an empirical evaluation , 2001, IEEE Trans. Image Process..

[22]  Rama Chellappa,et al.  A new approach to image feature detection with applications , 1996, Pattern Recognit..