Hybrid image segmentation using watersheds and fast region merging.

A hybrid multidimensional image segmentation algorithm is proposed, which combines edge and region-based techniques through the morphological algorithm of watersheds. An edge-preserving statistical noise reduction approach is used as a preprocessing stage in order to compute an accurate estimate of the image gradient. Then, an initial partitioning of the image into primitive regions is produced by applying the watershed transform on the image gradient magnitude.

A generalized fuzzy mathematical morphology and its application in robust 2-D and 3-D object representation.

In this paper, the generalized fuzzy mathematical morphology (GFMM) is proposed, based on a novel definition of the fuzzy inclusion indicator (FII). FII is a fuzzy set used as a measure of the inclusion of a fuzzy set into another, that is proposed to be a fuzzy set. It is proven that the FII obeys a set of axioms, which are proposed to be extensions of the known axioms that any inclusion indicator should obey, and which correspond to the desirable properties of any mathematical morphology operation.

Varying fitness functions in genetic algorithm constrained optimization: the cutting stock and unit commitment problems.

We present a specific varying fitness function technique in genetic algorithm (GA) constrained optimization. This technique incorporates the problem's constraints into the fitness function in a dynamic way. It consists of forming a fitness function with varying penalty terms.

Hierarchical representation and coding of surfaces using 3-D polygon meshes.

This paper presents a novel procedure for the representation and coding of three-dimensional (3-D) surfaces using hierarchical adaptive triangulation. The proposed procedure is based on pyramidal analysis using the quincunx sampling minimum variance interpolation (QMVINT) filters. These are reduced pyramids with quincunx sampling applied to the parametric representation of the surface, chosen so as to minimize the variance of the interpolation error, and thus, when combined with the appropriate encoding of the coefficients, optimize the compression of the mesh information transmitted.

Frontal face authentication using morphological elastic graph matching.

A novel dynamic link architecture based on multiscale morphological dilation-erosion is proposed for frontal face authentication. Instead of a set of Gabor filters tuned to different orientations and scales, multiscale morphological operations are employed to yield a feature vector at each node of the reference grid. Linear projection algorithms for feature selection and automatic weighting of the nodes according to their discriminatory power succeed to increase the authentication capability of the method.

A recurrent fuzzy-neural model for dynamic system identification.

This paper presents a fuzzy modeling approach for identification of dynamic systems. In particular, a new fuzzy model, the Dynamic Fuzzy Neural Network (DFNN), consisting of recurrent TSK rules, is developed. The premise and defuzzification parts are static while the consequent parts of the fuzzy rules are recurrent neural networks with internal feedback and time delay synapses.

Adaptive LMS L-filters for noise suppression in images.

Several adaptive least mean squares (LMS) L-filters, both constrained and unconstrained ones, are developed for noise suppression in images and compared in this paper. First, the location-invariant LMS L-filter for a nonconstant signal corrupted by zero-mean additive white noise is derived. It is demonstrated that the location-invariant LMS L-filter can be described in terms of the generalized linearly constrained adaptive processing structure proposed by Griffiths and Jim (1982).

Multichannel transforms for signal/image processing.

This paper presents a novel approach to the Fourier analysis of multichannel time series. Orthogonal matrix functions are introduced and are used in the definition of multichannel Fourier series of continuous-time periodic multichannel functions. Orthogonal transforms are proposed for discrete-time multichannel signals as well.

Hierarchical partition priority wavelet image compression.

Image compression methods for progressive transmission using optimal hierarchical decomposition, partition priority coding (PPC), and multiple distribution entropy coding (MDEC) are presented. In the proposed coder, a hierarchical subband/wavelet decomposition transforms the original image. The analysis filter banks are selected to maximize the reproduction fidelity in each stage of progressive image transmission.

Modular neural networks for MAP classification of time series and the partition algorithm.

We apply the partition algorithm to the problem of time-series classification. We assume that the source that generates the time series belongs to a finite set of candidate sources. Classification is based on the computation of posterior probabilities.

On the properties of the feedforward method: A simple training law for on-chip learning.

This paper investigates the properties of the so-called feedforward method, which is a very simple training law suitable for on-chip learning. Its merit is conceptual and implementational simplicity. Its signals do not propagate in both directions and it works for various types of activation function, a feature that makes it particularly effective in the case of unmodeled activation functions.

Reconstruction of magnetic resonance images using one-dimensional techniques.

Whenever DFT (discrete Fourier transform) processing of a multidimensional discrete signal is required, one can apply either a multidimensional FFT (fast Fourier transform) algorithm, or a single-dimension FFT algorithm, both using the same number of points. That is, the dimensions of a "multidimensional" signal, and of its spectrum, are a matter of choice. Every multidimensional sequence is completely equivalent to a one-dimensional function in both "time" and "frequency" domains.