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The Signal Multi-Vector

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Abstract

This work covers a fundamental problem of local phase based image analysis: the isotropic generalization of the classical 1D analytic signal to two dimensions. The analytic signal enables the analysis of local phase and amplitude information of 1D signals. Local phase, amplitude and additional orientation information can be extracted by the 2D monogenic signal with the restriction to intrinsically 1D signals. In case of 2D image signals the monogenic signal enables the rotationally invariant analysis of lines and edges. In this work we present the 2D analytic signal as a novel generalization of both the analytic signal and the 2D monogenic signal. In case of 2D image signals the 2D analytic signal enables the isotropic analysis of lines, edges, corners and junctions in one unified framework. Furthermore, we show that 2D signals are defined on a 3D projective subspace of the homogeneous conformal space which delivers a descriptive geometric interpretation of signals providing new insights on the relation of geometry and 2D image signals. Finally, we will introduce a novel algebraic signal representation, which can be regarded as an alternative and fully isomorphic representation to classical matrices and tensors. We will show the solution of isotropic intrinsically 2D image analysis without the need of steering techniques.

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References

  1. Bülow, T., Sommer, G.: Hypercomplex signals—a novel extension of the analytic signal to the multidimensional case. IEEE Trans. Signal Process. 49(11), 2844–2852 (2001)

    Article  MathSciNet  Google Scholar 

  2. Carneiro, G., Jepson, A.D.: Phase-based local features. In: 7th European Conference on Computer Vision—Part I. LNCS, vol. 2350, pp. 282–296. Springer, Berlin (2002)

    Google Scholar 

  3. Danielsson, P.E., Lin, Q., Ye, Q.Z.: Efficient detection of second-degree variations in 2D and 3D images. J. Vis. Commun. Image Represent. 12(3), 255–305 (2001)

    Article  Google Scholar 

  4. Delanghe, R.: Clifford analysis: History and perspective. Comput. Methods Funct. Theory 1(1), 107–153 (2001)

    MATH  MathSciNet  Google Scholar 

  5. Delanghe, R.: On some properties of the Hilbert transform in Euclidean space. Bull. Belg. Math. Soc. Simon Stevin 11(2), 163–180 (2004)

    MATH  MathSciNet  Google Scholar 

  6. Felsberg, M.: Low-level image processing with the structure multivector. Technical Report 2016, Kiel University, Department of Computer Science (2002)

  7. Felsberg, M., Sommer, G.: The monogenic signal. IEEE Trans. Signal Process. 49(12), 3136–3144 (2001)

    Article  MathSciNet  Google Scholar 

  8. Felsberg, M., Sommer, G.: The monogenic scale-space: A unifying approach to phase-based image processing in scale-space. J. Math. Imaging Vis. 21, 5–26 (2004)

    Article  MathSciNet  Google Scholar 

  9. Fleet, D.J., Jepson, A.D.: Stability of phase information. IEEE Trans. Pattern Anal. Mach. Intell. 15(12), 1253–1268 (1993)

    Article  Google Scholar 

  10. Fleet, D.J., Jepson, A.D., Jenkin, M.R.M.: Phase-based disparity measurement. CVGIP: Image Underst. 53 (1991)

  11. Gabor, D.: Theory of communication. J. IEE Lond. 93(26), 429–457 (1946)

    Google Scholar 

  12. Gradshteyn, I.S., Ryzhik, I.M.: Table of Integrals, Series, and Products. Academic Press, San Diego (2007)

    Google Scholar 

  13. Granlund, G.H., Knutsson, H.: Signal Processing for Computer Vision. Kluwer Academic, Dordrecht (1995)

    Google Scholar 

  14. Grau, V., Becher, H., Alison, Noble J.: Registration of multiview real-time 3-d echocardiographic sequences. In: MICCAI (1), pp. 612–619 (2006)

  15. Grau, V., Noble, J.A.: Adaptive multiscale ultrasound compounding using phase information. In: MICCAI, pp. 589–596 (2005)

  16. Hahn, S.L.: Hilbert Transforms in Signal Processing. Artech House, Boston (1996)

    MATH  Google Scholar 

  17. Hestenes, D., Sobczyk, G.: Clifford Algebra to Geometric Calculus, a Unified Language for Mathematics and Physics. Fundamental Theories of Physics. Kluwer Academic, Dordrecht (1999)

    Google Scholar 

  18. Huang, T., Burnett, J., Deczky, A.: The importance of phase in image processing filters. IEEE Trans. Acoust. Speech Signal Process. 23(6), 529–542 (1975)

    Article  Google Scholar 

  19. Jähne, B.: Digital Image Processing. Springer, Berlin (2001)

    Google Scholar 

  20. Köthe, U., Felsberg, M.: Riesz-transforms vs. derivatives: On the relationship between the boundary tensor and the energy tensor. In: Kimmel, R., Sochen, N., Weickert, J. (eds.) Scale Space and PDE Mathods in Computer Vision. LNCS, vol. 3459, pp. 179–191. Springer, Berlin (2005)

    Google Scholar 

  21. Larkin, K.G.: Natural Demodulation of 2D Fringe Patterns (2001)

  22. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)

    Article  Google Scholar 

  23. Oppenheim, A.V., Lim, J.S.: The importance of phase in signals. Proc. IEEE 69(5), 529–541 (1981)

    Article  Google Scholar 

  24. Pan, W., Bui, T.D., Suen, C.Y.: Rotation invariant texture classification by ridgelet transform and frequency-orientation space decomposition. Signal Process. 88(1), 189–199 (2008)

    Article  Google Scholar 

  25. Perwass, C.: Geometric Algebra with Applications in Engineering. Geometry and Computing, vol. 4. Springer, Berlin (2009)

    MATH  Google Scholar 

  26. Rieger, B., van Vliet, L.J.: Representing orientation in n-dimensional spaces. In: CAIP. LNCS, vol. 2756, pp. 17–24. Springer, Berlin (2003)

    Google Scholar 

  27. Sobczyk, G., Erlebacher, G.: Hybrid matrix geometric algebra. In: Li, H., Olver, P.J., Sommer, G. (eds.) Computer Algebra and Geometric Algebra with Applications. LNCS, vol. 3519, pp. 191–206. Springer, Berlin (2005)

    Google Scholar 

  28. Unser, M., Balać, K., Van De Ville, D.: The monogenic Riesz-Laplace wavelet transform. In: Proceedings of the Sixteenth European Signal Processing Conference (EUSIPCO’08) (2008)

  29. Weyl, H.: The Theory of Groups and Quantum Mechanics. Dover, New York (1928)

    Google Scholar 

  30. Wietzke, L., Fleischmann, O., Sommer, G.: 2D image analysis by generalized Hilbert transforms in conformal space. In: ECCV (2). LNCS, vol. 5303, pp. 638–649. Springer, Berlin (2008)

    Google Scholar 

  31. Wietzke, L., Sommer, G.: The 2D analytic signal. Technical Report 0802, Kiel University, Department of Computer Science (2008)

  32. Wietzke, L., Sommer, G.: The conformal monogenic signal. In: Rigoll, G. (ed.) DAGM, Pattern Recognition. LNCS, vol. 5096, pp. 527–536. Springer, Berlin (2008)

    Chapter  Google Scholar 

  33. Wietzke, L., Sommer, G.: The relation of inverse problems and isotropic 2D signal analysis. In: Mathematics in Signal Processing 8. Institute of Mathematics and its Applications (2008)

  34. Wietzke, L., Sommer, G., Fleischmann, O.: The geometry of 2D image signals. In: CVPR 2009, pp. 1690–1697. IEEE Computer Society on Computer Vision and Pattern Recognition (2009)

  35. Xiaoxun, Z., Yunde, J.: Local steerable phase (lsp) feature for face representation and recognition. In: CVPR ’06: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1363–1368. IEEE Computer Society, Los Alamitos (2006)

    Google Scholar 

  36. Zang, D., Wietzke, L., Schmaltz, C., Sommer, G.: Dense optical flow estimation from the monogenic curvature tensor. In: Scale Space and Variational Methods. LNCS, vol. 4485, pp. 239–250. Springer, Berlin (2007)

    Chapter  Google Scholar 

  37. Zetzsche, C., Barth, E.: Fundamental limits of linear filters in the visual processing of two-dimensional signals. Vis. Res. 30, 1111–1117 (1990)

    Article  Google Scholar 

Download references

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Correspondence to Lennart Wietzke.

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Wietzke, L., Sommer, G. The Signal Multi-Vector. J Math Imaging Vis 37, 132–150 (2010). https://doi.org/10.1007/s10851-010-0197-3

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