Abstract




 
   

IJE TRANSACTIONS B: Applications Vol. 31, No. 11 (November 2018) 1863-1870    Article in Press

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  DPML-RISK: AN EFFICIENT ALGORITHM FOR IMAGE REGISTRATION
 
S. Kazemi and M. R. Ahmadzadeh
 
( Received: March 16, 2018 – Accepted: October 26, 2018 )
 
 

Abstract    Nowadays targets and objects registration and tracking in a sequence of images play an important role in various areas. The feature-based image registration can be accomplished in two steps. The first step includes finding features of sensed and reference images and the second step includes describing features and matching of the images. In the first step, a scale space is used to reduce the sensitivity of detected features to the scale changes. In the second step we attribute feature points that obtained in the first step, a description using brightness value around the feature points. In this paper, a new algorithm is proposed based on BRISK and SIFT algorithms. The proposed algorithm uses the directional pattern to describe the feature. The pattern direction is perpendicular to the orientation feature. This idea provides more useful information regarding brightness around the feature point to make descriptor vector. Furthermore, in the proposed algorithm, the output vector consists of multilevel values instead of binary values. Levels of output vectors can be adjusted using a single parameter so that the processor with low computing ability can tune the output to a binary vector. Experimental results show that the proposed algorithm is more robust than the BRISK algorithm and the efficiency of the algorithm is about the same as BRISK algorithm.

 

Keywords    BRISK FREAK Image Registration

 

چکیده   

References    1. Oliveira, Francisco PM and Tavares, Joao Manuel RS, Medical image registration: a review, Computer methods in biomechanics and biomedical engineering, 17, 73–93 (2014) 2. Patel, Paresh M and Shah, Vishal M, Image registration techniques: a comprehensive survey, International Journal of Innovative Research and Development (2014) 3. Zitova, Barbara and Flusser, Jan, Image registration methods: a survey, Image and vision computing, 21, 977– 1000 (2003) 4. N. Kaushik, R. Rawat, and A. Bhalla, A Brief Study of Different Feature Detector and Descriptor, International Journal of Advanced Research in Computer and Communication Engineering, 5, 5 (2016) 5. M. Hassaballah, A. A. Abdelmgeid, and H. A. Alshazly, Image Features Detection, Description and Matching, Image Feature Detectors and Descriptors, 11–45 (2016) 6. D. G. Lowe, Distinctive image features from scaleinvariant keypoints, International journal of computer vision, 60, 91–110 (2004) 7. D. G. Lowe, Object recognition from local scale-invariant features, Computer vision, 1150–1157 (1999) 8. H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, Speededup robust features (SURF), Computer vision and image understanding, 110, 346–359 (2008) 9. S. Leutenegger, M. Chli, and R. Y. Siegwart, BRISK: Binary robust invariant scalable keypoints, Computer Vision (ICCV), 2548–2555 (2011) 10. E. Mair, G. D. Hager, D. Burschka, M. Suppa, and G. Hirzinger, Adaptive and generic corner detection based on the accelerated segment test, Computer VisionECCV 2010, 183–196 (2010) 11. A. Alahi, R. Ortiz, and P. Vandergheynst, Freak: Fast retina keypoint, Computer Vision and Pattern Recognition (CVPR), 510–517 (2012) 12. T. Lindeberg, Scale-space theory, computer vision, vol. 256 (2013) 13. K. Mikolajczyk and C. Schmid, A performance evaluation of local descriptors, Pattern Analysis and Machine Intelligence, vol. 27, 1615–1630 (2005)





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