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 introduced feature sets outperform the traditionally used energy. Best performance is achieved by combining histogram and co-occurrence signatures.

In this work we present a novel approach to handwritten character recognition which is based on the intuitive way in which characters are written as one or a few continuous lines. Therefore we calculate the zeroth, first and second radial moment as a function of the angle. In practice this is done by dividing the character into 32 angular sections. The three obtained curves can be used for pattern recognition using statistical analysis. The method has been evaluated using the NIST handwritten character data set. At first, a simple chi-square test gave a result of 80.81% recognition rate at zero rejection rate for digits. Using a back-propagation algorithm the recognition rate obtained was 87.54% also at zero rejection rate, showing that the features are sufficient to discriminate the characters.

Deals with the extraction of rotation-invariant texture features from multiscale image decomposition. We argue that the often used separable filtering schemes are very impractical for rotation-invariant feature extraction. Therefore we propose a scheme based on non-separable isotropic wavelet frames. The performance of the features is evaluated in a classification experiment.

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 demonstrated to be unbiased for high signal-to-noise ratio (SNR) and to yield physical relevant results for low SNR.

A new method for pattern recognition is proposed. The model uses information theory to extract in a systematic and hierarchic way the relevant information content in the input pattern so as to avoid preprocessing. An example of the power of the algorithm is demonstrated by its use in the recognition of heavily noisy binary printed letters.

An important issue in the analysis of two-dimensional electrophoresis images is the detection and quantification of protein spots. In this paper we describe a new robust technique to segment and model the different spots present in the gels. For the segmentation a watershed technique is applied. For the quantification of the spots, a new spot model is constructed, based on diffusion principles. Besides the advantage of having a physical interpretation, the model is demonstrated to be superior to the commonly used Gaussian models.

Starting from the physical principles of the dynamical scattering of high energetic electrons in high resolution electron microscopy (HREM) a new criterion is proposed for the determination of the absolute focus of reconstructed exit wave functions.

This paper describes a novel speech signal classification scheme based on spectrograms which are subjected to wavelet transform: a procedure which yields specific information regarding time and frequency variation of the signal. Feature vectors are extracted and classified using LVQ networks. The output of the network is interpreted as a fuzzy membership coefficient. This scheme is applied to the classification of voice dysphonia.

The factors limiting the resolution in high resolution electron microscopy are discussed. It is shown that electron microscopy is approaching its physical limits which are not limited anymore by the instrument but rather by the object. The problem however is how to extract the information from the images so as to fully exploit the resolution capabilities. Since the visualisation of individual atoms becomes possible, we can reconsider the concept of resolution in terms of information theory. New promising developments towards direct retrieval of the object information by processing of the electron images are discussed. These include solutions to the phase problem as well as inversion of the dynamical diffraction in the object. Finally, results are shown, which demonstrate the potential of this approach.

A new approach is proposed to retrieve the wavefunction at the exit face of an object by capturing images at a series of closely spaced focus values and to process the whole 3D data. This technique is optimised for high resolution transmission electron microscopy and applied to experimentally obtained defocus series.>