Abstract
Multi-view registration is an essential step in order to generate the side information for multi-view Distributed Video Coding. As stated in our previous work (Ciobanu and Côrte-Real, Multimed Tools Appl 48(3):411–436, 2010) it can be achieved by SIFT (scale-invariant feature transform) generated keypoint matches. The registration accuracy is vital for the adequate generation of side information and it directly depends on the reliable match of possibly all the available point to point correlations between two complete-overlapped views. We propose a solution to this problem based on iterative filtering of SIFT-generated keypoint matches, using the Hough transform and block matching. It aims the generic, real-life and constraint-free scenarios having an arbitrarily close angle between the two views. Practical results show an overall significant reduction of the outliers while maintaining a high rate of correct matches.
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References
Benedek C, Havasi L, Sziranyi T, Szlavik Z (2005) Motion-based flexible camera registration. In: IEEE conference on advanced video and signal based surveillance, 2005. AVSS 2005, pp 439–444
Bergevin R, Soucy M, Gagnon H, Laurendeau D (1996) Towards a general multi-view registration technique. IEEE Trans Pattern Anal Mach Intell 18(5):540–547
Bicego M, Lagorio A, Grosso E, Tistarelli M (2006) On the use of SIFT features for face authentication. In: Conference on computer vision and pattern recognition workshop, 2006. CVPRW ’06, pp 35–35
Brown M, Lowe DG (2003) Recognising panoramas. In: Proc. IEEE international conference on computer vision, Nice, France, 13–16 October 2003
Brown M, Lowe DG (2007) Automatic panoramic image stitching using invariant features. Int J Comput Vis 74:59–73
Ciobanu L, Côrte-Real L (2010) Successive refinement of side information for multi-view distributed video coding. Multimed Tools Appl 48(3):411–436
Fiala M, Shu C (2006) 3D model creation using self-identifying markers and sift keypoints. In: IEEE international workshop on Haptic audio visual environments and their applications, 2006. HAVE 2006, pp 118–123
Forssén P-E, Lowe DG (2007) Shape descriptors for maximally stable extremal regions. In: International conference on computer vision (ICCV), Rio de Janeiro, Brazil
Gao K, Lin S, Zhang Y, Tang S, Ren H (2008) Attention model based SIFT keypoints filtration for image retrieval. In: Seventh IEEE/ACIS international conference on computer and information science, 2008. ICIS 08, pp 191–196
Gordon I, Lowe DG (2006) What and where: 3D object recognition with accurate pose. In: Toward category-level object recognition, pp 67–82
Izquierdo E (2003) Efficient and accurate image based camera registration. IEEE Trans Multimedia 5(3):293–302
Ke Y, Sukthankar R (2004) PCA-SIFT: a more distinctive representation for local image descriptors. In: IEEE computer society conference on computer vision and pattern recognition, vol 2, pp 506–513
Ledwich L, Williams S (2004) Reduced SIFT features for image retrieval and indoor localisation. In: Australian conference on robotics and automation
Li Y, Wang Y, Huang W, Zhang Z (2008) Automatic image stitching using SIFT. In: International conference on audio, language and image processing, 2008. ICALIP 2008, pp 568–571
López García F (2008) SIFT features for object recognition and tracking within the IVSEE system. In: ICPR08, pp 1–4
Loui A, Das M (2008) Matching of complex scenes based on constrained clustering. AAAI Press
Lowe D. Scale-Invariant Feature Transform (SIFT): matching with local invariant features. http://www.cs.ubc.ca/spider/lowe/research.html, http://www.cs.ubc.ca/~lowe/keypoints/
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110
Luo J, Ma Y, Takikawa E, Lao S, Kawade M, Lu B-L (2007) Person-specific SIFT features for face recognition. In: IEEE international conference on acoustics, speech and signal processing, 2007. ICASSP 2007, vol 2, pp II–593–II–596
Mikolajczyk K, Schmid C (2005) A performance evaluation of local descriptors. IEEE Trans Pattern Anal Mach Intell 27(10):1615–1630
Osada K, Furuya T, Ohbuchi R (2008) SHREC’08 entry: Local volumetric features for 3D model retrieval. In: IEEE international conference on shape modeling and applications, 2008. SMI 2008, pp 245–246
Park U, Pankanti S, Jain AK (2008) Fingerprint verification using SIFT features. In: Kumar BV, Prabhakar S, Ross AA (eds) SPIE, vol 6944, no 1, p 69440K. http://link.aip.org/link/?PSI/6944/69440K/1
Shuai X, Zhang C, Hao P (2008) Fingerprint indexing based on composite set of reduced SIFT features. In: 19th international conference on pattern recognition, 2008. ICPR 2008, pp 1–4
Szlávik Z, Szirányi T, Havasi L (2007) Video camera registration using accumulated co-motion maps. ISPRS J Photogramm Remote Sens 61(5):298–306. http://www.sciencedirect.com/science/article/B6VF4-4MBT1XC-1/2/4b51668788fbc721045553312424b95e
Zhou H, Yuan Y, Shi C (2009) Object tracking using SIFT features and mean shift. Comput Vis Image Underst 113(3):345–352. http://www.sciencedirect.com/science/article/B6WCX-4TB18B4-2/2/1e03891714acb1657ed9156696fdbcfe (special issue on Video Analysis)
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The first author acknowledges the Fundação para a Ciência e a Tecnologia, Portugal, for the financial support.
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Ciobanu, L., Côrte-Real, L. Iterative filtering of SIFT keypoint matches for multi-view registration in Distributed Video Coding. Multimed Tools Appl 55, 557–578 (2011). https://doi.org/10.1007/s11042-010-0565-4
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DOI: https://doi.org/10.1007/s11042-010-0565-4