The principle that underlies the recognition of persons by their iris patterns is the failure of a test of statistical independence on texture phase structure as encoded by multiscale quadrature wavelets. The combinat...
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The principle that underlies the recognition of persons by their iris patterns is the failure of a test of statistical independence on texture phase structure as encoded by multiscale quadrature wavelets. The combinatorial complexity of this phase information across different persons spans about 249 degrees of freedom and generates a discrimination entropy of about 3.2 bits/mm/sup 2/ over the iris, enabling real-time decisions about personal identity with extremely high confidence. Algorithms first described by the author in 1993 have now been tested in several independent field trials and are becoming widely licensed. This presentation reviews how the algorithms work and presents the results of 9.1 million comparisons among different eye images acquired in trials in Britain, the USA, Korea, and Japan.
Soft thresholding has been a standard wavelet de-noising procedure in many signal and imageprocessing.applications. Theoretically, it is also almost optimal in the sense of nearly achieving the minimax mean-squared e...
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We investigate whether vectors of graph spectral features can be used for the purposes of graph clustering. We commence from the eigenvalues and eigenvectors of the adjacency matrix. Each of the leading eigenmodes rep...
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We investigate whether vectors of graph spectral features can be used for the purposes of graph clustering. We commence from the eigenvalues and eigenvectors of the adjacency matrix. Each of the leading eigenmodes represents a cluster of nodes and is mapped to a component of a feature vector. The spectral features used as components of the vectors are the eigenvalues and the shared perimeter length. We explore whether these vectors can be used for the purposes of graph clustering. Here we investigate the use of both central and pairwise clustering methods. On a database of view-graphs, both of the features provide good clusters while the eigenvectors perform better.
The following topics are dealt with: power system modelling, planning and operation; power electronics and machines; electromagnetics and optics; antenna theory, design and applications; remote sensing; SAR ; acoustic...
The following topics are dealt with: power system modelling, planning and operation; power electronics and machines; electromagnetics and optics; antenna theory, design and applications; remote sensing; SAR ; acoustic ranging; microelectronics and VLSI; nanotechnology and micromachining; instrumentation and sensors; circuits and systems; robotics and mechatronics; reliability engineering; computers and digital hardware; real-time systems; software and information technology; computational intelligence; neural networks; genetic algorithms and fuzzy logic; patternrecognition; imageprocessing. video processing. signal processing.and filter design; biomedical engineering; health-care systems; communications systems; computer networks; wireless networks; telecommunication traffic analysis; QoS; industrial applications.
We propose a segmentation-based method of object tracking using image warping and Kalman filtering. The object region is defined to include a group of patches, which are obtained by a watershed algorithm. In a robust ...
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We propose a segmentation-based method of object tracking using image warping and Kalman filtering. The object region is defined to include a group of patches, which are obtained by a watershed algorithm. In a robust M-estimator framework, we estimate dominant motion of the object region. A linear Kalman filter is employed to predict the estimated affine motion parameters based on a second order kinematic model. image (affine) warping is performed to predict the object region in the next frame. The warping error of each watershed segment (patch) and its rate of overlapping with the predicted region are utilized for classification of watershed segments near the object border. Applications of head and hand tracking using this method demonstrate its performance.
We consider the expression and recognition of dynamic concepts by regarding the movement patterns learned in a recurrent neural net as symbols. We then develop a method to express more abstract dynamic concepts by com...
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We consider the expression and recognition of dynamic concepts by regarding the movement patterns learned in a recurrent neural net as symbols. We then develop a method to express more abstract dynamic concepts by combining them with symbols and connecting several recurrent neural networks. Application of the method to actual recognition cases, ball bouncing and dancing, demonstrated its effectiveness. These experiments show the ability of the method to deal with dynamic concepts that are difficult to describe because of vagueness.
This paper deals with face retrieval using the 1st- and 2nd-order PCA mixture model. The well-known eigenface method uses one set of holistic facial features obtained by PCA. However, the single set of eigenfaces is n...
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This paper deals with face retrieval using the 1st- and 2nd-order PCA mixture model. The well-known eigenface method uses one set of holistic facial features obtained by PCA. However, the single set of eigenfaces is not enough to represent face images with large variations. To overcome this weakness, we propose the method that uses more than one set of eigenfaces obtained from the EM learning in PCA mixture model. 2nd-order eigenface method can be extended to the method using 2nd-order PCA mixture model, also. Simulation results show that the method using 2nd-order PCA mixture model is the best for the face images with illumination variations and the method using 1st-order PCA mixture model is the best for the face images with pose variations in, terms of ANMRR (average of the normalized modified retrieval rank).
The automated detection of electrographic seizures in the neonatal EEG is a difficult, unsolved problem because of the variety of seizure patterns and the large number of seizure-like artifacts and non-seizure rhythmi...
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The automated detection of electrographic seizures in the neonatal EEG is a difficult, unsolved problem because of the variety of seizure patterns and the large number of seizure-like artifacts and non-seizure rhythmic EEG events. In this paper we present an architecture and methodology for such a detection system designed around a combination of signal processing.patternrecognition, heuristic rules, and neural networks. We believe that this hybrid approach offers the best chance for reliable automated detection of neonatal seizures.
This paper presents a very effective algorithm for detecting and classifying cyclic and dihedral symmetries from images of finite patterns. Some results concerning reflectional and rotational symmetries of Fourier tra...
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This paper presents a very effective algorithm for detecting and classifying cyclic and dihedral symmetries from images of finite patterns. Some results concerning reflectional and rotational symmetries of Fourier transforms are presented and exploited to define a convenient frequency domain function whose zero-crossings contain distinctive pairs of orthogonal lines related to the order of the symmetry. The discrimination between cyclic and dihedral symmetries is accomplished by comparing these zero-crossings with those of a second frequency domain function. Several examples of symmetric patterns correctly classified by our algorithm are reported and discussed in the paper.
The proceedings contain 221 papers. The special focus in this conference is on Computational Neuroscience, Connectionist Cognitive Science, Data Analysis and patternrecognition. The topics include: A neurodynamical t...
ISBN:
(纸本)3540440747
The proceedings contain 221 papers. The special focus in this conference is on Computational Neuroscience, Connectionist Cognitive Science, Data Analysis and patternrecognition. The topics include: A neurodynamical theory of visual attention;a neural model of spatio temporal coordination in prehension;stabilized dynamics in physiological and neural systems despite strongly delayed feedback;learning multiple feature representations from natural image sequences;receptive fields similar to simple cells maximize temporal coherence in natural video;multiple forms of activity-dependent plasticity enhance information transfer at a dynamic synapse;macrocolumns as decision units;clustering within integrate-and-fire neurons for image segmentation;applying slow feature analysis to image sequences yields a rich repertoire of complex cell properties;a neural network model generating invariance for visual distance;comparing the information encoded by different brain areas with functional imaging techniques;mean-field population dynamics of spiking neurons with random synaptic delays;stochastic resonance and finite resolution in a network of leaky integrate-and-fire neurons;non-monotonic current-to-rate response function in a novel integrate-and-fire model neuron;small-world effects in lattice stochastic diffusion search;a direction sensitive network based on a biophysical neurone model;neural coding analysis in retinal ganglion cells using information theory;attractor neural networks with hypercolumns;encoding the temporal statistics of Markovian sequences of stimuli in recurrent neuronal networks;firing rate for a generic integrate-and-fire neuron with exponentially correlated input and iterative population decoding based on prior beliefs.
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