In this study, the authors extend and refine the process of fingerprint retrieval, with the goal of boosting recognition rates for the first rank candidate and low penetration rates. On top of a baseline retrieval system which extracts Gabor features in multiple directions from fingerprint images, the authors propose spatial modelling techniques to generate artificial samples for training the system. Translational modelling, rotational modelling and distorted sample generation techniques are used to augment the original training set in order to boost the accuracy of fingerprint retrieval. The effectiveness of the models is evaluated using the well‐known National Institute of Standards and Technology database 4. Experimental results, with reference to some leading fingerprint retrieval rates reported in the literature, confirm that the authors’ proposed system is promising in recognition performance.

The topics of fingerprint classification, indexing, and retrieval have been studied extensively in the past decades. One problem faced by researchers is that in all publicly available fingerprint databases, only a few fingerprint samples from each individual are available for training and testing, making it inappropriate to use sophisticated statistical methods for recognition. Hence most of the previous works resorted to simplek-nearest neighbor (k-NN) classification. However, thek-NN classifier has the drawbacks of being comparatively slow and less accurate. In this paper, we tackle this problem by first artificially expanding the set of training samples using our previously proposed spatial modeling technique. With the expanded training set, we are then able to employ a more sophisticated classifier such as the Bayes classifier for recognition. We apply the proposed method to the problem of one-to-Nfingerprint identification and retrieval. The accuracy and speed are evaluated using the benchmarking FVC 2000, FVC 2002, and NIST-4 databases, and satisfactory retrieval performance is achieved.

The recent advance in pen-based computing and mobile devices empowers many innovative e-learning systems with increased interactivity and improved features. In this paper, we proposed an innovative and pen-based COMPAD simulator to enhance both education and research in computer systems. Being model-based, our proposed system is adaptive and different from many commercially available Windows based emulators that are customized for specific computer architectures. Besides, our COMPAD simulator allows learners to flexibly modify any part of a program through pen-based inputs, and instantly visualize the computed results. In this way, the COMPAD simulator can support not only education but also research such as instruction scheduling in computer systems. To demonstrate the feasibility, we built the COMPAD-PRO as its prototype for empirical evaluation. After all, this work stimulates many interesting directions for further exploration.

The problem of offline handwritten Chinese character recognition has been extensively studied by many researchers and very high recognition rates have been reported. In this paper, we propose to further boost the recognition rate by incorporating a distortion model that artificially generates a huge number of virtual training samples from existing ones. We achieve a record high recognition rate of 99.46% on the ETL-9B database. Traditionally, when the dimension of the feature vector is high and the number of training samples is not sufficient, the remedies are to (i) regularize the class covariance matrices in the discriminant functions, (ii) employ Fisher's dimension reduction technique to reduce the feature dimension, and (iii) generate a huge number of virtual training samples from existing ones. The second contribution of this paper is the investigation of the relative effectiveness of these three methods for boosting the recognition rate.

In the past few years, many new development toolkits such as the Nebula2 and/or mobile technologies including the WiFi or mobileTV have opened up exciting learning opportunities on mobile devices. On top of it, new technologies continue to fuel the rapid growth of newly merged fields of research like the edutainment for educational entertainment. In a recent teaching development project, we have developed an interactive game platform to facilitate learning and more importantly the spirit of teamwork for collaborative problem-solving on desktop and pocket PCs. With the great challenges imposed by globalization, we strongly believe that learning to collaboratively analyze and then apply the ldquoappropriaterdquo knowledge to solve a specific problem is always the key to success. In this paper, we discuss about an on-going work, and share our relevant experience in system development. Furthermore, evaluation strategies will be thoroughly examined. After all, our work shed light on many interesting directions for future exploration.

This paper proposes a recursive algorithm for estimating the adaptive function coefficients autoregressive (AFAR) models. Due to its recursive nature, its arithmetic complexity is relatively lower than conventional methods. Furthermore, a new M-estimation-based AFAR parameter estimation algorithm is developed to suppress the effect of impulsive outliers. Simulation results show that the M-estimation-based algorithm offers improved performance than the conventional LS-based algorithm

This paper proposes a new QR-decomposition-based recursive frequency estimation algorithm for multiple sinusoids based on the linear prediction (LP) approach. It extends the batch processing algorithm of So et al. in order to process the input samples recursively at a much lower arithmetic complexity for supporting on-line applications. Furthermore, a weighted least M-estimate (WLM) algorithm is developed to improve robustness to impulsive noise. Simulation results show that the robust recursive frequency estimator has a better performance than the conventional LS estimation in impulsive noise environment

This paper summarizes the research activities of the pastdecade on the recognition of handwritten scripts used inChina, Japan, and Korea. It presents the recognitionmethodologies, features explored, databases used, andclassification schemes investigated. In addition, it includes adescription of the performance of numerous recognitionsystems found in both academic and industrial researchlaboratories. Recent achievements and applications are alsopresented. A list of relevant references is attached togetherwith our remarks on this subject.

In this paper, a method is proposed for offline signature verification. It is based on a smoothness criterion. It is observed that the cursive segments of forgery signatures are generally less smooth and less natural than the genuine ones, especially for those signatures that consist of cursive graphic patterns. Two approaches are proposed to extract a smoothness feature: a crossing method and a fractal dimension method. When the proposed smoothness feature is combined with other global shape features for signature verification, satisfactory results are obtained.

In recent years, wavelets have attracted great attention in both still image compression and video coding, and several novel wavelet-based image compression algorithms have been developed so far, one of which is Shapiro's embedded zerotree wavelet (EZW) image compression algorithm. However, there are still some deficiencies in this algorithm. In this paper, after the analysis of the deficiency in EZW, a new algorithm based on quantized coefficient partitioning using morphological operation is proposed. Instead of encoding the coefficients in each subband line-by-line, regions in which most of the quantized coefficients are significant are extracted by morphological dilation and encoded first. This is followed by using zerotrees to encode the remaining space which has mostly zeros. Experimental results show that the proposed algorithm is not only superior to the EZW, but also compares favorably with the most efficient wavelet-based image compression algorithms reported so far.

Proposes a method to tackle the problem of detecting skilled forgeries in off-line signature verification. Inspired by the approach adopted by expert examiners, it is based on a smoothness criterion. From a collection of genuine and forged signatures, it is observed that, although skilled forgery signatures are very similar to genuine ones on a global scale, they are generally less smooth and natural on a detailed scale than the genuine ones, especially for those skilled forgery signatures which consist of cursive graphic patterns. A smoothness index is derived from such signatures. This is combined with other global shape features and used for verification. Satisfactory results are obtained.

The Embedded Zerotree Wavelet (EZW) image compression algorithm has been widely used in real applications for its high compression performance. In this paper an improvement of EZW is presented. In the original EZW algorithm, when a new significant coefficient is generated, its children are all encoded, although its descendants maybe all insignificant, and thus its performance is affected. The improvement proposed in this paper is based on significance checking in wavelet trees (SCIWT). It is aimed to avoid encoding the children of each newly generated significant coefficient if it has no significant descendant. Experiments show that this proposed algorithm not only outperforms the original EZW over a wide range of compression ratios, but also completely retains all its key features without introducing any sophisticated and computationally complex method.

Two patterns are matched by putting one on top of the other and iteratively moving their individual parts until most of their corresponding parts are aligned. An energy function and a neighborhood of influence are defined for each iteration. Initially, a large neighborhood is used such that the movements result in global features being coarsely aligned. The neighborhood size is gradually reduced in successive iterations so that finer and finer details are aligned. Encouraging results have been obtained when applied to match complex Chinese characters. It has been observed that computation increases with the square of the number of moving parts which is quite favorable compared with other algorithms. The method was applied to the recognition of handwritten Chinese characters. After performing the iterative matching, a set of similarity measures are used to measure the similarity in topological features between the input and template characters. An overall recognition rate of 96.1% is achieved.

Given the feature vector from an unknown class, the branch and bound algorithm (BAB) is very efficient for finding the nearest neighbor among the set of reference vectors. The Euclidean distance measure is adopted. In this article, the BAB algorithm is extended so that it can be used with the Bayes classifier which uses the probability measure instead of the Euclidean distance for classification. Gaussian statistics is assumed in the derivations. Satisfactory results are obtained in recognition experiments.

In this paper, we present a novel approach to optical character recognition that utilizes ring-projection-wavelet-fractal-signatures. In particular, the proposed approach reduces the dimensionality of a two-dimensional pattern by way of a ring-projection method, and thereafter, performs Daubechies' wavelet transform on the derived one-dimensional pattern to generate a set of wavelet sub-patterns, namely, curves that are non-self intersecting. Further from the resulting non-self intersecting curves, the divider dimensions are readily computed. These divider dimensions constitute a new characteristic vector for the original two-dimensional pattern, defined over the curves' fractal dimensions.