Real time image registration based on feature tracking using a Digital Signal Processor

Image registration is a stepwise process of overlaying various images of a scene. Accurate image registration in a real time environment has always been a challenge involving the detection of analogous features and then construction of mosaic within plausible accuracy range. Digital Signal Processor is an asset in this regard; its advanced versions equipped with multiple cores can handle many processes at the same time. In this paper we have implemented an efficient algorithm, proposed in our previous research work, for real time image registration on a Dual Core Digital Signal Processor ADSP-BF561. The key technique employed for image registration is based on feature tracking. Our work includes division of main tasks into two groups and then implementation of each group on a different core of dual core Digital Signal Processor. The proposed solution is then put to test in a real time environment on various sequences of images and its time variation is also analyzed.

[1]  Abdul Bais,et al.  Efficient Implementation of Image Registration Based on Feature Tracking , 2010, 2010 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM).

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

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

[4]  M. Sen,et al.  Reconfigurable image registration on FPGA platforms , 2006, 2006 IEEE Biomedical Circuits and Systems Conference.

[5]  Jason N. Dale,et al.  Cell Broadband Engine Architecture and its first implementation - A performance view , 2007, IBM J. Res. Dev..

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

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

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

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

[10]  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.

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

[12]  Hong Zhang,et al.  A multicore based parallel image registration method , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

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

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

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

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

[17]  Jun Zhou,et al.  Multithreading Method to Perform the Parallel Image Registration , 2009, 2009 International Conference on Computational Intelligence and Software Engineering.

[18]  N. Gupta.  A VLSI Architecture for Image Registration in Real Time , 2007, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

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

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