Spatio-featural scale-space

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5567 LNCS, Issue , 2009, Pages 808-819

  Felsberg, Michael ^a  

^a LINKÖPING UNIVERSITY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

CHANNEL REPRESENTATION; COMPUTATIONAL EFFORT; DESCRIPTORS; FEATURE DOMAIN; FEATURE MAP; FEATURE REPRESENTATION; FEATURE RESOLUTION; FUNDAMENTAL BUILDING BLOCKS; IMAGE STRUCTURES; LINEAR CHANNEL; LINEAR SCALE-SPACE THEORY; LINEAR SMOOTHING; LINEAR SPACES; LOCAL BASIS FUNCTIONS; LOWER BOUNDS; MEMORY REQUIREMENTS; NON-ITERATIVE; NONLINEAR EVOLUTIONS; NONLINEAR TECHNIQUES; SCALE-SPACE; SPATIAL DOMAINS; SPATIAL RESOLUTION; UNCERTAINTY RELATION;

COMPUTER VISION; CONTROL THEORY; IMAGE ANALYSIS; IMAGE ENHANCEMENT; UNCERTAINTY ANALYSIS;

IMAGE RECONSTRUCTION;

EID: 69049115417     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-02256-2_67     Document Type: Conference Paper

References (28)

1. Iijima, T.: Basic theory of pattern observation. Papers of Technical Group on Automata and Automatic Control, IECE, Japan (December 1959)
2. Witkin, A.P.: Scale-space filtering. In: Int. Joint Conf. Art. Intell., pp. 1019-1022 (1983)
3. Koenderink, J.J.: The structure of images. Biolog. Cybernetics 50, 363-370 (1984)
4. Weickert, J., Ishikawa, S., Imiya, A.: Linear scale-space has first been proposed in Japan. Mathematical Imaging and Vision 10, 237-252 (1999)
5. Felsberg, M., Sommer, G.: The monogenic scale-space: A unifying approach to phase-based image processing in scale-space. J. Math. Imag. Vis. 21, 5-26 (2004)
6. Duits, R., Florack, L.M.J., de Graaf, J., ter Haar Romeny, B.M.: On the axioms of scale space theory. Journal of Mathematical Imaging and Vision 20, 267-298 (2004)
7. Granlund, G.H.: In search of a general picture processing operator. Computer Graphics and Image Processing 8, 155-173 (1978)
8. Burt, P.J., Adelson, E.H.: The Laplacian pyramid as a compact image code. IEEE Trans. Communications 31(4), 532-540 (1983)
9. Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Machine Intelligence 11, 674-693 (1989)
10. Daubechies, I.: Orthonormal bases of compactly supported wavelets. Communications on Pure and Applied Mathematics 41(7), 909-996 (1988)
11. Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Analysis and Machine Intelligence 12(7), 629-639 (1990)
12. Weickert, J.: Theoretical foundations of anisotropic diffusion in image processing. Computing suppl. 11, 221-236 (1996)
13. Portilla, J., Strela, V., Wainwright, J., Simoncelli, E.P.: Image denoising using scale mixtures of Gaussians in the wavelet domain. IEEE Trans. Image Processing 12(11), 1338-1351 (2003)
14. Felsberg, M., Forssén, P.E., Scharr, H.: Channel smoothing: Efficient robust smoothing of low-level signal features. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(2), 209-222 (2006)
15. Granlund, G.H.: An associative perception-action structure using a localized space variant information representation. In: Sommer, G., Zeevi, Y.Y. (eds.) AFPAC 2000. LNCS, vol. 1888, pp. 48-68. Springer, Heidelberg (2000)
16. Forssén, P.E., Granlund, G.: Robust multi-scale extraction of blob features. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 11-18. Springer, Heidelberg (2003)
17. Jonsson, E.: Channel-Coded Feature Maps for Computer Vision and Machine Learning. PhD thesis, Linköping University, Sweden (2008)
18. Jonsson, E., Felsberg, M.: Efficient computation of channel-coded feature maps through piecewise polynomials. Image and Vision Computing (in press)
19. Felsberg, M., Granlund, G.: P-channels: Robust multivariate m-estimation of large datasets. In: International Conference on Pattern Recognition, Hong Kong (2006)
20. Zemel, R.S., Dayan, P., Pouget, A.: Probabilistic interpretation of population codes. Neural Computation 10(2), 403-430 (1998)
21. Pouget, A., Dayan, P., Zemel, R.: Information processing with population codes. Nature Reviews - Neuroscience 1, 125-132 (2000)
22. Howard, I.P., Rogers, B.J.: Binocular Vision and Stereopsis. Oxford University Press, Oxford (1995)
23. Forssén, P.E.: Low and Medium Level Vision using Channel Representations. PhD thesis, Linköping University, Sweden (2004)
24. Lindeberg, T.: Scale-Space Theory in Computer Vision. Kluwer Academic Publishers, Boston (1994)
25. Elder, J.H., Zucker, S.W.: Local scale control for edge detection and blur estimation. IEEE Trans. Pattern Analysis and Machine Intell. 20(7), 699-716 (1998)
26. Koenderink, J.J., van Doorn, A.J.: Image processing done right. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 158-172. Springer, Heidelberg (2002)
27. Santhanam, T.S.: Higher-order uncertainty relations. Journal of Physics A: Mathematical and General 33(8), 83-85 (2000)
28. Florack, L.M.J., ter Haar Romeny, B.M., Koenderink, J.J., Viergever, M.A.: Scale and the differential structure of images. Image Vision Comp. 10(6), 376-388 (1992)