Showing papers in "IEICE Transactions on Information and Systems in 2001"

A Novel Validity Index for Determination of the Optimal Number of Clusters

Abstract: The structural characteristics of clusters are investigated in the partitioning process. Two partition functions, which show opposite properties around the optimal cluster number, are found and a new cluster validity index is presented based on the combination of these functions. Some properties of the index function are discussed and numerical examples are presented. key words: clustering, validity index, optimal cluster number

A Random Walk through Eigenspace

Abstract: SUMMARY It has been over a decade since the “Eigenfaces” approach to automatic face recognition, and other appearancebased methods, made an impression on the computer vision research community and helped spur interest in vision systems being used to support biometrics and human-computer interface. In this paper I give a personal viewof the original motivation for the work, some of the strengths and limitation of the approach, and progress in the years since. Appearance-based approaches to recognition complement feature- or shape-based approaches, and a practical face recognition system should have elements of both. Eigenfaces is not a general approach to recognition, but rather one tool out of many to be applied and evaluated in the appropriate context.

Improved Fingerpring Matching by Distortion Removal

Abstract: Fingerprint recognition is a well-researched problem, and there are several highly accurate systems commercially available. However, this biometric technology still suffers from problems with the handling of bad quality prints. Recent research has begun to tackle the problems of poor quality data. This paper takes a new approach to one problem besetting fingerprints — that of distortion. Previous attempts have been made to ensure that acquired prints are not distorted, but the novel approach presented here corrects distortions in fingerprints that have already been acquired. This correction is a completely automatic and unsupervised operation. The distortion modelling and correction are explained, and results are presented demonstrating significant improvements in matching accuracy through the application of the technique.

Two-Handed Multi-Fingers String-Based Haptic Interface Device

Abstract: Alkali metal or alkaline earth metal terminated rubbery polymers are reacted with monofunctional hydrocarbyl organometal compounds in which the metal is selected from Groups 2b, 4a, and 5a and which contain either halogen, -OR, -SR, or -N(R)2. The resulting rubbery polymers have improved physical properties in gum or filled stocks.

A new intrusion detection method based on discriminant analysis

Abstract: SUMMARY Many methods have been proposed to detect intrusions; for example, the pattern matching method on known intrusion patterns and the statistical approach to detecting deviation from normal activities. We investigated a new method for detecting intrusions based on the number of system calls during a user’s network activity on a host machine. This method attempts to separate intrusions from normal activities by using discriminant analysis, a kind of multivariate analysis. We can detect intrusions by analyzing only 11 system calls occurring on a host machine by discriminant analysis with the Mahalanobis’ distance, and can also tell whether an unknown sample is an intrusion. Our approach is a lightweight intrusion detection method, given that it requires only 11 system calls for analysis. Moreover, our approach does not require user profiles or a user activity database in order to detect intrusions. This paper explains our new method for the separation of intrusions and normal behavior by discriminant analysis, and describes the classification method by which

Speech Enhancement: New Approaches to Soft Decision

Abstract: In this paper, we propose new approaches to speech enhancement based on soft decision. In order to enhance the statistical reliability in estimating speech activity, we introduce the concept of a global speech absence probability (GSAP). First, we compute the conventional speech absence probability (SAP) and then modify it according to the newly proposed GSAP. The modification is made in such a way that the SAP has the same value of GSAP in the case of speech absence while it is maintained to its original value when the speech is present. Moreover, for improving the performance of the SAP’s at voice tails (transition periods from speech to silence), we revise the SAP’s using a hang-over scheme based on the hidden Markov model (HMM). In addition, we suggest a robust noise update algorithm in which the noise power is estimated not only in the periods of speech absence but also during speech activity based on soft decision. Also, for improving the SAP determination and noise update routines, we present a new signal to noise ratio (SNR) concept which is called the predicted SNR in this paper. Moreover, we demonstrate that the discrete cosine transform (DCT) enhances the accuracy of the SAP estimation. A number of tests show that the proposed method which is called the speech enhancement based on soft decision (SESD) algorithm yields better performance than the conventional approaches. key words: speech enhancement, global soft decision, hang-over, predicted SNR, DCT

Radial Distortion Snakes

Abstract: In this paper, we address the problem of recovering the camera radial distortion coefficients from one image. The approach that we propose uses a special kind of snakes called radial distortion snakes. Radial distortion snakes behave like conventional deformable contours, except that their behavior are globally connected via a consistent model of image radial distortion. Experiments show that radial distortion snakes are more robust and accurate than conventional snakes and manual point selection.

An Extension of the Dependency Pair Method for Proving Termination of Higher-Order Rewrite Systems

Abstract: This paper explores how to extend the dependency pair technique for proving termination of higher-order rewrite systems. We show that the termination property of higher-order rewrite systems can be checked by the non-existence of an infinite R-chain, which is an extension of Arts’ and Giesl’s result for the first-order case. It is clarified that the subterm property of the quasi-ordering, used for proving termination automatically, is indispensable.

Polynomial Learnability of Stochastic Rules with Respect to the KL-Divergence and Quadratic Distance

Abstract: We consider the problem of efficient learning of probabilistic concepts (p-concepts) and more generally stochastic rules in the sense defined by Kearns and Schapire [6] and by Yamanishi [18]. Their models extend the PAC-learning model of Valiant [16] to the learning scenario in which the target concept or function is stochastic rather than deterministic as in Valiant’s original model. In this paper, we consider the learnability of stochastic rules with respect to the classic ‘Kullback-Leibler divergence’ (KL divergence) as well as the quadratic distance as the distance measure between the rules. First, we show that the notion of polynomial time learnability of p-concepts and stochastic rules with fixed range size using the KL divergence is in fact equivalent to the same notion using the quadratic distance, and hence any of the distances considered in [6] and [18]: the quadratic, variation, and Hellinger distances. As a corollary, it follows that a wide range of classes of p-concepts which were shown to be polynomially learnable with respect to the quadratic distance in [6] are also learnable with respect to the KL divergence. The sample and time complexity of algorithms that would be obtained by the above general equivalence, however, are far from optimal. We present a polynomial learning algorithm with reasonable sample and time complexity for the important class of convex linear combinations of stochastic rules. We also develop a simple and versatile technique for obtaining sample complexity bounds for learning classes of stochastic rules with respect to the KL-divergence and quadratic distance, and apply them to produce bounds for the classes of probabilistic finite state acceptors (automata), probabilistic decision lists, and convex linear combinations. key words: PAC-learning, KL-divergence, quadratic-distance, stochastic rules, p-concepts

3D Shape Reconstruction Using Three Light Sources in Image Scanner

Abstract: We suggest the method to recover the 3D shape of the object by using the color image scanner which has three light sources. The photometric stereo is traditional to recover the surface normal of the object using the multiple light sources. In this method, it usually assumes the distant light sources to make the model simple. But the light sources in the image scanner are so close to the object that the illuminant intensity varies with the distance from the light source, therefore these light sources should be modeled as the linear light sources. In this method, by using this models and two step algorithm; the initial estimation by the iterating computation and the optimization by the non-linear least square method, not only the surface normal but also the absolute distance from the light source to the surface are estimated. By this method, we can recover the 3D shape more precisely. In the experimental results, the reconstruction of the 3D sh&e of the real object is shown. The proposed method changes the image scanner into the shape scanner. It makes the 3D data acquisition easily, so it will be very useful in the CAD/CAM and the virtual reality system.

Intelligent Image Retrieval Using Neural Network

Abstract: In content-based image retrieval (CBIR), the content of an image can be expressed in terms of different features such as color, texture, shape, or text annotations. Retrieval methods based on these features can be varied depending on how the feature values are combined. Many of the existing approaches assume linear relationships between different features, and also require users to assign weights to features for themselves. Other nonlinear approaches have mostly concentrated on indexing technique. While the linearly combining approach establishes the basis of CBIR, the usefulness of such systems is limited due to the lack of the capability to represent high-level concepts using lowlevel features and human perception subjectivity. In this paper, we introduce a Neural Network-based Image Retrieval (NNIR) system, a human-computer interaction approach to CBIR using the Radial Basis Function (RBF) network. The proposed approach allows the user to select an initial query image and incrementally search target images via relevance feedback. The experimental results show that the proposed approach has the superior retrieval performance over the existing linearly combining approach, the rank-based method, and the BackPropagationbased method. key words: content-based image retrieval, radial basis function network, neural network-based image retrieval, relevance feedback

Filtering and Smoothing for Motion Trajectory of Feature Poing Using Non-Gaussian State Space Model

Abstract: Filtering and smoothing using a non-Gaussian state space model are proposed for motion trajectory of feature point in image sequence. A heavy-tailed non-Gaussian distribution is used for measurement noise to reduce the effect of outliers in motion trajectory. Experimental results are presented to show the usefulness of the proposed method. key words: feature point tracking, image sequence, nonGaussian state space model, sequential Monte Carlo method