Refereed full papers (journals, book chapters, international conferences)

1997

@inproceedings{ZaL1997,
	vgclass =	{refpap},
	author =	{R. Zarita and S. Lelandais},
	title =	{Wavelets and High Order Statistics for Texture
	Classification},
	booktitle =	{The 10th Scandinavian Conference on Image Analysis (SCIA'97)},
	address =	{Lappeenranta, Finland},
	pages =	{95--102},
	month =	{June},
	year =	{1997},
	abstract =	{In this paper we describe an original approach to
	unsupervised classification of textures. Our method is based on image
	decomposition with a wavelet packet transform. According to our
	experiments, we confirm recent observations which state that a large
	class of natural textures can be modelled as quasi-periodic signals
	whose dominant frequencies are located in the middle frequency
	channels. We thus consider that every frequency band produces a feature
	image and our aim is to quantify its visual content in order to
	decompose only dominant channels that are able to discriminate between
	different textures. A vector of texture attributes is computed at every
	frequency band. We use for this aim statistical operators issued from
	cooccurrence and run length matrices.  Our aim is to introduce for the
	multiscale analysis of textures a corresponding statistical framework.
	The classification results deal with good discrimination of textures.
	Finally, applications, results as well as future developments of our
	approach are discussed.},
}