This paper addresses two pattern-recognition problems in the context of random sets. For the .rst, the random set law is known and the task is to estimate the observed pattern from a feature set calculated from the observation. For the second, the law is unknown and we wish to estimate the parameters of the law. Estimation is accomplished by an optimal linear system whose inputs are features ba...

The goal of this study is to classify magnetic resonance imaging (MRI) of brains of patients with Alzheimer′s Disease (AD) and those without AD and then to identify changes in brain MRI in early stages of AD. A novel approach based on the diffusion map framework, which is considered to be one of the leading manifold learning methods, is used for this classification. Diffusion mapping provides d...

Many studies in neuroscience have shown that nonlinear dynamic networks represent a bio-inspired models for information and image processing. Recent studies on the thalamo-cortical system have shown that weakly connected oscillatory networks have the capability of modelling the architecture of a neurocomputer. In particular they have associative properties and can be exploited for dynamic patte...

A stochastic change detection methodology for reliable monitoring complex nonlinear dynamic systems is proposed. For a semi-active magneto-rheological (MR) damper, the non-parametric, data-driven restoring force method was used to identify the nonlinear dynamic damping device. Both supervised and unsupervised statistical pattern recognition techniques were used to detect the changes in the phys...

Kernel Discriminant Analysis (KDA) has been successfully applied to many pattern recognition problems. KDA transforms the original problem into a space of dimension N where N is the number of training vectors. For speech recognition, N is usually prohibitively high increasing computational requirements beyond current computational capabilities. In this paper, we provide a formulation of a subsp...

Phase extraction pattern recognition is a special case of nonlinear matched filtering. The phase extraction procedure is executed on the input function's Fourier transform as well as on the filter function's Fourier transform, both of which are manipulated for correlation purposes. This novel process is examined theoretically, by computer simulations and laboratory experiments. The implementati...

Face recognition is a challenging task in computer vision and pattern recognition. It is well-known that obtaining a low-dimensional feature representation with enhanced discriminatory power is of paramount importance to face recognition. Moreover, recent research has shown that the face images reside on a possibly nonlinear manifold. Thus, how to effectively exploit the hidden structure is a k...