Multiresolution Approach to “Visual Pattern” Partitioning of 3D Images
This paper deals with the problem of low level representation of 3D image contents. The presented solution makes use of multiresolution techniques to recover the so-called visual patterns or integral features that form images. It consists of decomposing the image into a set of elementary image features, representing frequency channels, using a filter bank, and grouping them by means of clustering analysis. The method introduces a novel design of the bank of oriented scaled filters. In addition, a new measure of dissimilarity between pairs of features is applied to the hierarchical clustering technique.
KeywordsFilter Bank Visual Pattern Dissimilarity Measure Filter Response Hierarchical Cluster Technique
Unable to display preview. Download preview PDF.
- 1.Kovesi, P.D.: Invariant Measures of Image Features from Phase Information, The University or Western Australia (1996), http://www.cs.uwa.edu.au/pub/robvis/theses/PeterKovesi/
- 3.Field, D.J.: Scale–Invariance and self-similar “wavelet” Transforms: An Analysis of Natural Scenes and Mammalian Visual Systems. In: Farge, M., Hunt, J.C.R., Vassilicos, J.C. (eds.) Wavelets, fractals and Fourier Transforms, pp. 151–193. Clarendon Press, Oxford (1993)Google Scholar
- 4.Chamorro-Martínez, J., Fdez-Valdivia, J.A., García, J.A., Martínez-Baena, J.: A frequency Domain Approach for the Extraction of Motion Patterns. In: IEEE International Conference on Acoustics, Speech and Signal Processing, Hong Kong, vol. 3, pp. 165–168 (2003)Google Scholar
- 6.Granlund, G.H., Knutsson, H.: Signal Processing for Computer Vision. Kluwer Academic Publishers, Boston (1995)Google Scholar