Showing papers in "Pattern Recognition in 2003"

TL;DR: The global k-means algorithm is presented which is an incremental approach to clustering that dynamically adds one cluster center at a time through a deterministic global search procedure consisting of N executions of the k-Means algorithm from suitable initial positions.

TL;DR: This survey introduces the most prominent automatic facial expression analysis methods and systems presented in the literature and discusses issues such as face normalization, facial expression dynamics and facial expression intensity.

TL;DR: This paper provides a comprehensive survey of research on computer-vision-based human motion analysis, namely human detection, tracking and activity understanding, and various methods for each issue are discussed in order to examine the state of the art.

TL;DR: The statistical variability that is the basis of iris recognition is analysed in this paper using new large databases, presenting the results of 9.1 million comparisons among several thousand eye images acquired in trials in Britain, the USA, Japan and Korea.

TL;DR: This paper points out the weakness of the previous LDA based methods, and suggests a complete PCA plus LDA algorithm, and experimental results indicate that the proposed method is more effective than the previous ones.

TL;DR: The high correlation between the appearance of the microcalcification clusters and the diseases show that the CAD (computer aided diagnosis) systems for automated detection/classification of MCCs will be very useful and helpful for breast cancer control.

TL;DR: The results of handwritten digit recognition on well-known image databases using state-of-the-art feature extraction and classification techniques are competitive to the best ones previously reported on the same databases.

TL;DR: Experimental results verify the validity of the proposed approaches in personal authentication using the template-matching and the backpropagation neural network to measure the similarity in the verification stage.

TL;DR: Simulation results for the IRIS data classification and the hand-written digit recognition, and the fraud detection show that the proposed SVM ensemble with bagging or boosting outperforms a single SVM in terms of classification accuracy greatly.

TL;DR: It is shown that AB gives consistently better results than bagging, both in accuracy and stability, and it is demonstrated that ranking the attribute subsets by their classification accuracy and voting using only the best subsets further improves the resulting performance of the ensemble.

TL;DR: Three methods for visual cryptography of gray-level and color images based on past studies in black-and-white visual cryptography, the halftone technology, and the color decomposition method are proposed.

TL;DR: A set of examples or training set (TS) is said to be imbalanced if one of the classes is represented by a very small number of cases compared to the other classes.

TL;DR: The experimental results indicate that the classification accuracy is increased significantly under parallel feature fusion and also demonstrate that the developed parallel fusion is more effective than the classical serial feature fusion.

TL;DR: It is shown that leave-one-out cross-validation of kernel Fisher discriminant classifiers can be implemented with a computational complexity of only O (l 3 ) operations rather than the O ( l 4 ) of a naive implementation, where l is the number of training patterns.

TL;DR: This paper considers the palmprint as a piece of texture and applies texture-based feature extraction techniques to palmprint authentication and the experimental results illustrate the eectiveness of this method.

TL;DR: A hybrid fingerprint matching scheme that uses both minutiae and ridge flow information to represent and match fingerprints, where the entire image is taken into account while constructing the ridge feature map.

TL;DR: This paper adopts an anomaly detection approach by detecting possible intrusions based on program or user profiles built from normal usage data using a scheme that can be justified from the perspective of hypothesis testing.

TL;DR: This paper shall use the dynamic programming strategy to get the optimal solution and the experimental results will show that this method consumes less computation time and also gets the optimal Solution.

TL;DR: A class of algorithms in which the idea is to deal with systems of independent variables corresponds to approximations of the pixels' interactions similar to the mean field approximation, and follows algorithms that have the advantage of taking the Markovian structure into account while preserving the good features of EM.

TL;DR: This study presents a simple method for high-hiding capacity that outperforms the simple LSB substitution method given the same range of data digits in the embedded data and achieves good image vision quality without the need for post-processing.

TL;DR: An up-to-date survey of the dimensionality estimation methods of a data set, paying special attention to the fractal-based methods.

TL;DR: This paper explores how to embed symbolic relational graphs with unweighted edges in a pattern-space using a graph-spectral approach and illustrates the utility of the embedding methods on neighbourhood graphs representing the arrangement of corner features in 2D images of 3D polyhedral objects.

TL;DR: A new technique, denoted as q -recursive method, is proposed, specifically for fast computation of Zernike moments, which uses radial polynomials of fixed order p with a varying index q to compute ZERNike moments.

TL;DR: Applications of the nonlinear shape statistics in segmentation and tracking of 2D and 3D objects demonstrate that the segmentation process can incorporate knowledge on a large variety of complex real-world shapes.

TL;DR: A feature extraction method is presented by utilizing an error estimation equation based on the Bhattacharyya distance to use classification errors in the transformed feature space, which are estimated using the error estimation equations, as a criterion for feature extraction.

TL;DR: The minimum classification error (MCE) training algorithm (which was originally proposed for optimizing classifiers) is investigated for feature extraction and a generalized MCE (GMCE)Training algorithm is proposed to mend the shortcomings of the MCE training algorithm.

TL;DR: Experiments with a face detection system show that combining feature reduction with hierarchical classification leads to a speed-up by a factor of 335 with similar classification performance.

TL;DR: The heart rate variability is used as the base signal from which certain parameters are extracted and presented to the ANN for classification, and the same data is also used for fuzzy equivalence classifier.

TL;DR: A new approach to constructing appearance models based on kernel canonical correlation analysis ( kernel-CCA), where a non-linear transformation of the input data is performed implicitly using kernel methods, which is especially well suited for relating two sets of measurements.

TL;DR: A novel minutiae matching method that describes elastic distortions in fingerprints by means of a thin-plate spline model that gives considerably higher matching scores compared to rigid matching algorithms, while only taking 100 ms on a 1 GHz P-III machine.