Paul Scheunders

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We conjecture that texture can be characterized by the statistics of the wavelet detail coefficients and therefore introduce two feature sets: (1) the wavelet histogram signatures which capture all first order statistics using a model based approach and (2) the wavelet co-occurrence signatures, which reflect the coefficients' second-order statistics. The(More)
The problem of parameter estimation from Rician distributed data (e.g., magnitude magnetic resonance images) is addressed. The properties of conventional estimation methods are discussed and compared to maximum-likelihood (ML) estimation which is known to yield optimal results asymptotically. In contrast to previously proposed methods, ML estimation is(More)
In the last decade, multiscale techniques for gray-level texture analysis have been intensively used. In this paper, we aim on extending these techniques to color images. We introduce wavelet energy-correlation signatures and we derive the transformation of these signatures upon linear color space transformations. Experiments are conducted on a set of 30(More)
This contribution concerns the retrieval of colour texture. The interband correlation structure is considered by modeling the heavy-tailed image wavelet histograms with a multivariate generalized Gaussian. As a similarity measure we propose to use the Rao geodesic distance, which, in contrast to the Kullback-Leibler divergence, exists in a closed form for(More)
This paper describes a novel data clustering algorithm, which is a hybrid approach combining a genetic algorithm with the classical c-means clustering algorithm (CMA). The proposed technique is superior to CMA in the sense that it converges to a nearby global optimum rather than a local one. As an application the problem of color image quantization is(More)
In this paper, wavelets were employed for multi-scale image analysis to extract parameters for the description of chromatin texture in the cytological diagnosis and grading of invasive breast cancer. Their value was estimated by comparing the performance of co-occurrence, densitometric, and morphometric parameters in an automated K-nearest neighbor (Knn)(More)
The aim of this work is the development of a semiautomatic segmentation technique for efficient and accurate volume quantization of Magnetic Resonance (MR) data. The proposed technique uses a 3D variant of Vincent and Soilles immersion-based watershed algorithm that is applied to the gradient magnitude of the MR data and that produces small volume(More)
A musical score provides a great deal of information about a piece of music. In this paper we consider the incorporation of a music score to guide source separation on a single channel recording. We propose a method based on synthesizing lines of music in the score. Dynamic time warping (DTW) is used to to fit the synthesized data to the recording. These(More)
In this paper, a Bayesian wavelet-based denoising procedure for multicomponent images is proposed. A denoising procedure is constructed that 1) fully accounts for the multicomponent image covariances, 2) makes use of Gaussian scale mixtures as prior models that approximate the marginal distributions of the wavelet coefficients well, and 3) makes use of a(More)