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EURASIP Journal on Advances in Signal Processing
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Comparison of Feature-List Cross-Correlation Algorithms with Common Cross-Correlation Algorithms

EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 089150 (2007) Cite this article

Abstract

This paper presents a feature-list cross-correlation algorithm based on: a common feature extraction algorithm, a transformation of the results into a feature-list representation form, and a list-based cross-correlation algorithm. The feature-list cross-correlation algorithms are compared with known results of the common cross-correlation algorithms. Therefore, simple test images containing different objects under changing image conditions and with several image distortions are used. In addition, a medical application is used to verify the results. The results are analyzed by means of curve progression of coefficients and curve progression of peak signal-to-noise ratio (PSNR). As a result, the presented feature list cross-correlation algorithms are sensitive to all changes of image conditions. Therefore, it is possible to separate objects that are similar but not equal. Because of the high quantity of feature points and the strong PSNR, the loss of a few feature points does not have a significant influence on the detection results. These results are confirmed by a successfully applied medical application. The calculation time of the feature list cross-correlation algorithms only depends on the length of the feature-lists. The amount of feature points is much less than the number of pixels in the image. Therefore, the feature-list cross-correlation algorithms are faster than common cross-correlation algorithms. Better image conditions tend to reduce the size of the feature-list. Hence, the processing time decreases considerably.

References

  1. Burt PJ, Yen C, Xu X: Local correlation measures for motion analysis, a comparative study. Proceedings of IEEE Computer Society Conference on Pattern Recognition and Image Processing, June 1982, Las Vegas, Nev, USA 269–274.

    Google Scholar 

  2. Wesley E, Snyder WE, Qi H: Machine Vision. Cambridge University Press, Cambridge, UK; 2004.

    MATH  Google Scholar 

  3. Huang S-Y, Tsai W-C: A simple and efficient block motion estimation algorithm based on full-search array architecture. Signal Processing: Image Communication 2004,19(10):975-992. 10.1016/j.image.2004.08.001

    Google Scholar 

  4. Lohweg V, Diederichs C, Müller D: Algorithms for hardware-based pattern recognition. EURASIP Journal on Applied Signal Processing 2004,2004(12):1912-1920. 10.1155/S1110865704404247

    Google Scholar 

  5. Töreyin BU, Çetin AE, Aksay A, Akhan MB: Moving object detection in wavelet compressed video. Signal Processing: Image Communication 2005,20(3):255-264. 10.1016/j.image.2004.12.002

    Google Scholar 

  6. Förstner W: A feature based correspondence algorithm for image matching. Proceedings of International Archives of Photogrammetry and Remote Sensing Symposium (ISP Commission III), 1986, Rovaniemi, Finland 26(3/3): 150–166.

    Google Scholar 

  7. Maschotta R, Boymann S, Steuer D: Shift reducing of retinal vessel image series by using edge based template matching algorithm. Proceedings of the 2nd European Medical and Biological Engineering Conference (EMBEC '02), December 2002, Vienna, Austria 848–849.

    Google Scholar 

  8. Mori S, Nishida H, Yamada H: Optical Character Recognition. John Wiley & Sons, New York, NY, USA; 1999.

    Google Scholar 

  9. Tankus A, Yeshurun Y: Scene-consistent detection of feature points in video sequences. Computer Vision and Image Understanding 2005,97(1):1-29. 10.1016/j.cviu.2004.05.003

    Article  Google Scholar 

  10. Yang C-HT, Lai S-H, Chang L-W: Robust face image matching under illumination variations. EURASIP Journal on Applied Signal Processing 2004,2004(16):2533-2543. 10.1155/S1110865704410014

    MathSciNet  MATH  Google Scholar 

  11. Gui-Guang D, Bao-Long G: Motion vector estimation using line-square search block matching algorithm for video sequences. EURASIP Journal on Applied Signal Processing 2004,2004(11):1750-1756. 10.1155/S1110865704402273

    Google Scholar 

  12. Barbarien J, Munteanu A, Verdicchio F, Andreopoulos Y, Cornelis J, Schelkens P: Motion and texture rate-allocation for prediction-based scalable motion-vector coding. Signal Processing: Image Communication 2005,20(4):315-342. 10.1016/j.image.2004.12.006

    Google Scholar 

  13. Arivazhagan S, Ganesan L: Automatic target detection using wavelet transform. EURASIP Journal on Applied Signal Processing 2004,2004(17):2663-2674. 10.1155/S1110865704408208

    MATH  Google Scholar 

  14. Cideciyan AV: Registration of ocular fundus images: an algorithm using cross-correlation of triple invariant image descriptors. IEEE Engineering in Medicine and Biology Magazine 1995,14(1):52-58. 10.1109/51.340749

    Article  Google Scholar 

  15. Laliberté F, Gagnon L, Sheng Y: Registration and fusion of retinal images-an evaluation study. IEEE Transactions on Medical Imaging 2003,22(5):661-673. 10.1109/TMI.2003.812263

    Article  Google Scholar 

  16. Traver VJ, Pla F: Similarity motion estimation and active tracking through spatial-domain projections on log-polar images. Computer Vision and Image Understanding 2005,97(2):209-241. 10.1016/j.cviu.2004.07.007

    Article  Google Scholar 

  17. Stiller C, Konrad J: Estimating motion in image sequences, a tutorial on modeling and computation of 2D motion. IEEE Signal Processing Magazine 1999,16(4):70-91. 10.1109/79.774934

    Article  Google Scholar 

  18. Deans SR: Hough transform from the radon transform. IEEE Transactions on Pattern Analysis and Machine Intelligence 1981,3(2):185-188.

    Article  Google Scholar 

  19. Princen J, Illingworth J, Kittler J: A formal definition of the hough transform: properties and relationships. Journal of Mathematical Imaging and Vision 1992,1(2):153-168. 10.1007/BF00122210

    Article  Google Scholar 

  20. Jähne B: Digital Image Processing. 6th, revised and extended edition. Springer, Berlin, Germany; 2005.

    MATH  Google Scholar 

  21. Sonka M, Hlavac V, Boyle R: Image Processing, Analysis, and Machine Vision. PWS, Pacific Grove, Calif, USA; 1999.

    Google Scholar 

  22. Maschotta R, Boymann S, Hoppe U: Regelbasierte kantenerkennung zur schnellen kantenbasierten segmentierung der glottis in hochgeschwindigkeitsvideos. In Bildverarbeitung für die Medizin. Springer, Berlin, Germany; 2005:188-192.

    Google Scholar 

  23. Maschotta R, Rehs J, Boymann S, Hoppe U: Evaluation of feature extraction algorithms for the feature-list cross-correlation in retinal images. Proceedings of the 3rd European Medical and Biological Engineering Conference, November 2005, Prague, Czech Republic

    Google Scholar 

  24. Hansen KV, Toft PA: Fast curve estimation using preconditioned generalized radon transform. IEEE Transactions on Image Processing 1996,5(12):1651-1661. 10.1109/83.544572

    Article  Google Scholar 

  25. Radon J: Über die bestimmung von funktionen durch ihre integralwerte längs gewisser mannigfaltigkeiten. Berichte Schsische Akademie der Wissenschaften. Leipzig, Mathematisch-Naturwissenschaftliche Klasse 1917, 69: 262–277.

    MATH  Google Scholar 

  26. Beyerer J, León FP: Die radontransformation in der digitalen bildverarbeitung. Automatisierungstechnik 2002, 50: 472–480. 10.1524/auto.2002.50.10.472

    Article  Google Scholar 

  27. Olson CF: Constrained hough transforms for curve detection. Computer Vision and Image Understanding 1999,73(3):329-345. 10.1006/cviu.1998.0728

    Article  MATH  Google Scholar 

  28. Guil N, Gonzalez-Linares JM, Zapata EL: Bidimensional shape detection using an invariant approach. Pattern Recognition 1999,32(6):1025-1038. 10.1016/S0031-3203(98)00127-7

    Article  Google Scholar 

  29. Hough PVC: A method and means for recognizing complex patterns. US patent 3,069,654, 1962

    Google Scholar 

  30. Canny J: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 1986,8(6):679-698.

    Article  Google Scholar 

  31. Förstner W, Gülch E: A fast operator for detection and precise location of distinct points, corners and centres of circular features. Proceedings of ISPRS Intercommission Conference on Fast Processing of Photogrammetric Data, June 1987, Interlaken, Switzerland 281–305.

    Google Scholar 

  32. Harris C, Stephens M: A combined corner and edge detector. Proceedings of the 4th Alvey Vision Conference, August-September 1988, Manchester, UK 147–151.

    Google Scholar 

  33. Köthe U: Edge and junction detection with an improved structure tensor. Pattern Recognition, Proceedings of 25th DAGM Symposium, September 2003, Magdeburg, Germany, Lecture Notes in Computer Science 2781: 25–32.

    Google Scholar 

  34. Intel Open source computer vision library, reference manual, 2001, https://doi.org/www.intel.com

  35. Lewis JP: Fast normalized cross-correlation. Proceedings of Vision Interface (VI '95), May 1995, Quebec, Canada 120–123.

    Google Scholar 

  36. Theodoridis S, Koutroumbas K: Pattern Recognition. Academic Press, New York, NY, USA; 1999.

    MATH  Google Scholar 

  37. Ravanbakhsh M: Designing and developing a fully automatic interior orientation method in a digital photogrammetric workstation. Proceedings of the 20th ISPRS Congress (Commision II), July 2004, Istanbul, Turkey 543–547.

    Google Scholar 

  38. Maschotta R, Pietraszczyk M, Boymann S, Jannek D: Genauigkeit und generalisierbarkeit kantenlistenbasierter korrelationsverfahren im vergleich zu grauwertbasierten verfahren. In Bildverarbeitung für die Medizin. Springer, Berlin, Germany; 2004:110-114.

    Google Scholar 

  39. Vilser W, Riemer T, Nagel E, Fink A: Functional imaging of retinal vessels - principle and clinical potential. Proceedings of the 38th Annual Congress of the German Society for Biomedical Engineering (BMT), September 2004, Ilmenau, Germany

    Google Scholar 

  40. Maschotta R, Boymann S, Lehmann S, Steuer D: Software architecture for modular, extensible and reusable signal processing components. Proceedings of Advances in Automation, Multimedia and Video Systems, and Modern Computer Science, 2001 304–308.

    Google Scholar 

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Authors and Affiliations

  1. Institute of Biomedical Engineering and Informatics, Ilmenau Technical University, P.O. Box 100565, Ilmenau, 98693, Germany

    Ralph Maschotta & Simon Boymann

  2. Department of Audiology, University Hospital of Erlangen-Nuremberg, Waldstr. 1, Erlangen, 91054, Germany

    Ulrich Hoppe

Authors
  1. Ralph Maschotta
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  2. Simon Boymann
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  3. Ulrich Hoppe
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Correspondence to Ralph Maschotta.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Maschotta, R., Boymann, S. & Hoppe, U. Comparison of Feature-List Cross-Correlation Algorithms with Common Cross-Correlation Algorithms. EURASIP J. Adv. Signal Process. 2007, 089150 (2007). https://doi.org/10.1155/2007/89150

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  • DOI: https://doi.org/10.1155/2007/89150

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