Abstract
This paper presents an empirical study of the joint wavelet statistics for textures and other random imagery. There is a growing realization that modeling wavelet coefficients as independent, or at best correlated only across scales, assuming independence within a scale, may be a poor assumption. While recent developments in wavelet-domain Hidden Markov Models (notably HMT-3S) account for within-scale dependencies, we find empirically that wavelet coefficients exhibit within- and across-subband neighborhood activities which are orientation dependent. Surprisingly these structures are not considered by the state-of-the-art wavelet modeling techniques. In this paper we describe possible choices of the wavelet statistical interactions by examining the joint-histograms, correlation coefficients, and the significance of coefficient relationships.
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Azimifar, Z., Fieguth, P., Jernigan, E. (2004). Textures and Wavelet-Domain Joint Statistics. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2004. Lecture Notes in Computer Science, vol 3212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30126-4_41
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DOI: https://doi.org/10.1007/978-3-540-30126-4_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23240-7
Online ISBN: 978-3-540-30126-4
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