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A new hiding scheme of digital image is introduced in this paper, which is robust to image processing such as JPEG compression, median filtering, adding Gauss noise, cropping, and histogram equalization. In this paper, we embed watermark into DWT domain, using integer modulo, retrieval of embedded watermark does not need original image. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the application of a two weights neural network (TWNN) to the channel equalization problem. In particular, the purpose of the paper is to improve the previously developed TWNN equalizer with training using K-means and LMS methods; reducing the TWNN network size by considering a lesser number of TWNN kernels, and developing new techniques for determining channel order which is required to specify the structure of an TWNN equalizer. A linear regression model was used for estimating the channel order. The basic idea of reducing the network size is to select the centers, based on the channel lag. This work includes the comparison of the limits of mean square error (MSE) convergence of both a linear equalizer and an TWNN equalizer. mean square error. [ABSTRACT FROM AUTHOR]

To protect the privacy of proxy blind signature holders from dissemination of signatures by verifiers, this paper introduces universal designated-verifier proxy blind signatures. In a universal designated-verifier proxy blind signature scheme, any holder of a proxy blind signature can convert the proxy blind signature into a designated-verifier signature. Given the designated-signature, only the designated-verifier can verify that the message was signed by the proxy signer, but is unable to convince anyone else of this fact. This paper also proposes an ID-based universal designated-verifier proxy blind signature scheme from bilinear group-pairs. The proposed scheme can be used in E-commerce to protect user privacy. [ABSTRACT FROM AUTHOR]

Network forensic plays an important role in the modern network environment for computer security, but it has become a time-consuming and daunting task due to the sheer amount of data involved. This paper proposes a new method for constructing incremental fuzzy decision trees based on network service type to reduce the human intervention and time-cost, and to improve the comprehensibility of the results. At the end of paper, we discuss the performance of the forensic system and present the result of experiments. [ABSTRACT FROM AUTHOR]

Auto image segmentation can segment the image without operators interfering and is an important technique in the image processing. The Boltzmann-Color-Image-Segmentation (BCIS), which could control the degree of segmentation by adjusting the temperature parameter, is designed based on the Boltzmann-theory and the Metropolis-rule in the paper. Then the criterion function of image segmentation, which could balance between the number of segmented region and the affinity of the segmented image with the original image, is presented. Based the BCIS and Criterion function, the auto color image segmentation is schemed out by using the artificial immune algorithm. Experiments showed that the color image segmentation algorithm, which we designed in the paper, had the good capabilities. [ABSTRACT FROM AUTHOR]

This paper describes the Next Generation PARLAY X with QoS / QoE in Next Generation Network (NGN). PARLAY has introduced the architecture for the development and deployment of services by service providers over 3G network. But the existing PARLAY X does not provide the open Application Programming Interface (API) for QoS / QoE. Therefore, to solve this issue, this paper suggests the PARLAY X with QoS / QoE. The object of this paper is to support the architecture and the API of the network service for QoS / QoE in NGN. The PARLAY X can provide users with QoS / QoE in network according to the detected context such as location and speed and user's preference. The architecture of the Next Generation PARLAY X is comprised of the functions for context-awareness, adaptation, and personalization. [ABSTRACT FROM AUTHOR]

A single web service is most likely inadequate to serve the customers' business needs; it takes a selection of various web services composed together to form a business process. The cost is the primary concern of many business processes. In this paper, we propose a new solution using Genetic Algorithm (GA) in cost-reduction driven web service selection. GA is utilized to optimize business process composed of many service agents (SAg), each of which corresponds to a collection of available web services provided by multiple service providers to perform a specific function. Service selection is an optimization process with taking into account the relationships among the services. Better performance has been gotten using GA in the paper than using local service selection strategy. [ABSTRACT FROM AUTHOR]

Application of agent technology in Intrusion Detection Systems (IDSs) has been developed. Intrusion Detection (ID) agent technology can bring IDS flexibility and enhanced distributed detection capability. However, the security of the ID agent and methods of collaboration among ID agents are important problems noted by many researchers. In this paper, coordination among the intrusion detection agents by BlackBoard Architecture (BBA), which transcends into the field of distributed artificial intelligence, is introduced. A system using BBA for information sharing can easily be expanded by adding new agents and increasing the number of BlackBoard (BB) levels. Moreover the subdivided BB levels enhance the sensitivity of ID. This paper applies fuzzy logic to reduce the false positives that represent one of the core problems of IDS. ID is a complicated decision-making process, generally involving enormous factors regarding the monitored system. A fuzzy logic evaluation component, which represents a decision agent model of in distributed IDSs, considers various factors based on fuzzy logic when an intrusion behavior is analyzed. The performance obtained from the coordination of an ID agent with fuzzy logic is compared with the corresponding non-fuzzy type ID agent. [ABSTRACT FROM AUTHOR]

CBR is one of the most important artificial intelligence methods. In this paper, it is introduced to detect the variation of known attacks and to reduce the false negative rate in rule based IDS. After briefly describes the basic process of CBR and the methods of describing case and constructing case base by rules of IDS, this paper focuses on the CBR engine. A new CBR engine adapting to IDS is designed because the common CBR engines cannot deal with the specialties of intrusion cases in IDS. The structure of the new engine is described by class graph, and the core class as well as the similarity algorithm adopted by it is analyzed. At last, the results of testing the new engine on Snort are shown, and the validity of the engine is substantiated. [ABSTRACT FROM AUTHOR]

Correlation of intrusion alerts is a major technique in attack detection to build attack scenario. Rule-based and data mining methods have been used in some previous proposals to perform correlation. In this paper we integrate two complementary methods and introduce fuzzy clustering in the data mining method. To determine the fuzzy similarity coefficients, we introduce a hierarchy measurement and use weighted average to compute total similarity. This mechanism can measure the semantic distance of intrusion alerts with finer granularity than the common similarity measurement . The experimental results in this paper show that using fuzzy clustering method can reconstruct attack scenario which are wrecked by missed attacks. [ABSTRACT FROM AUTHOR]

Security and privacy are two primary concerns in RFID adoption. In this paper we focus on security issues in general and data tampering in particular. Here we present a conceptual framework to detect and identify data tampering in RFID tags. The paper surveys the existing literature and proposes to add a tamper detection component in the existing RFID middleware architecture. The tamper detection component is supported by mathematical algorithm to embed and extract secret information which can be employed to detect data tampering. [ABSTRACT FROM AUTHOR]

In the long term usage of the network, users will form certain types of habit according to their specific characteristics, individual hobbies and given restrictions. On the burst-out of worms, the overwhelming flow caused by random scanning will temporarily alter the behavior representation of users. Therefore, it is reasonable to conclude that the statistics and classification of the user habit can contribute to the detection of worms. On the basis of analysis about both users and worms, we construct the model of user-habit and propose a new approach for the early warning of worms. This paper possesses strong direction significance due to its broad applicability since extended models can be derived from the model proposed in this paper. [ABSTRACT FROM AUTHOR]

When considering information security and privacy issues most of the attention has previously focussed on data protection and the privacy of personally identifiable information (PII). What is often overlooked is consideration for the operational and transactional data. Specifically, the security and privacy protection of metadata and metastructure information of computing environments has not been factored in to most methods. Metadata, or data about data, can contain many personal details about an entity. It is subject to the same risks and malicious actions personal data is exposed to. This paper presents a new perspective for information security and privacy. It is termed Meta Privacy and is concerned with the protection and privacy of information system metadata and metastructure details. We first present a formal definition for meta privacy, and then analyse the factors that encompass and influence meta privacy. In addition, we recommend some techniques for the protection of meta privacy within the information systems. Further, the paper highlights the importance of ensuring all informational elements of information systems are adequately protected from a privacy perspective. [ABSTRACT FROM AUTHOR]

Although studies regarding the classification and analysis of source-level vulnerabilities in operating systems are not direct and practical solutions to the exploits with which computer systems are attacked, it is important that these studies supply the elementary technology for the development of effective security mechanisms. Linux systems are widely used on the Internet and in intra-net environments. However, researches regarding the fundamental vulnerabilities in the Linux kernel have not been satisfactorily conducted. In this paper, characteristic classification and correlation analysis of source-level vulnerabilities in the Linux kernel, open to the public and listed on the SecurityFocus site for the 6 years from 1999 to 2004, are presented. This study will enable Linux kernel maintenance groups to understand the wide array of vulnerabilities, to analyze the characteristics of the attack abusing vulnerabilities, and to prioritize their development effort according to the impact of these vulnerabilities on the Linux systems. [ABSTRACT FROM AUTHOR]

This paper considers a real-time risk assessment method for information systems and networks based on observations from networks sensors such as intrusion detection systems. The system risk is dynamically evaluated using hidden Markov models, providing a mechanism for handling data from sensors with different trustworthiness in terms of false positives and negatives. The method provides a higher level of abstraction for monitoring network security, suitable for risk management and intrusion response applications. [ABSTRACT FROM AUTHOR]

Nematodes are primitive organisms which nonetheless devour many of the essential resources that are critical for human beings. For effective and quick inspection and quarantine, we propose a general image based system for quantitatively characterizing and identifying nematodes. We also describe the key methods ranging from gray level image acquisition and processing to information extraction for automated detection and identification. The main contributions of this paper are not only presenting a framework of the system architecture, but also giving detail analysis and implementation of each system component with instance of Caenorhabditis elegans. Therefore with a little modification, this system can be applied to other nematode species discrimination and analysis. [ABSTRACT FROM AUTHOR]

In this paper, we presented a new wavelet-based digital image stabilization (DIS) algorithm on the motion estimation for the stabilization of the 2-axes rotation sight system. In proposed algorithm, we first estimate the local motion defined in terms of translational motion vector by using fine-to-coarse (FtC) multi-resolution motion estimation (MRME). Next, we estimate the global motion defined as the rotational motion parameters such as the rotational center and angular frequency by using the vertical and horizontal component in wavelet domain. The rotational center and angular frequency are estimated from the local motion field and the special subset of the motion vector respectively. The experimental results show the improved stabilization performance compared with the conventional digital image stabilization algorithm. [ABSTRACT FROM AUTHOR]

This paper suggests a prototype system for visualization and analysis of anatomic shape and functional features of the hippocampus. Based on the result of MR-SPECT multi-modality image registration, anatomical and functional features of hippocampus are extracted from MR and registered SPECT images, respectively. The hippocampus is visualized in 3D by applying volume rendering to hippocampus volume data extracted from the MR image with color coded by registered SPECT image. In order to offer the objective and quantitative data concerning to the anatomic shape and functional features of the hippocampus, the geometric volume and the SPECT intensity histogram of hippocampus regions are automatically measured based on the MR and the registered SPECT image, respectively. We also propose a new method for the analysis of hippocampal shape using an integrated Octree-based representation, consisting of meshes, voxels, and skeletons. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the notion of vector-valued multiresolution analysis. We discuss the existence of biorthogonal multiple vector-valued wavelets. An algorithm for constructing a class of compactly supported biorthogonal multiple vector-valued wavelets associated with the biorthogonal multiple vector-valued scaling functions is presented by using multiresolution analysis and matrix theory. [ABSTRACT FROM AUTHOR]

In this paper, we presents HyperSausage Neuron based on the High-Dimension Space(HDS), and proposes a new algorithm for speaker independent continuous digit speech recognition. At last, compared to HMM-based method, the recognition rate of HyperSausage Neuron method is higher than that of in HMM-based method. [ABSTRACT FROM AUTHOR]

In this paper, we will present the design and implementation of the crypto coprocessors which can be used for providing the RFID security. By analyzing the power consumption characteristics of our crypto coprocessors, we can see which crypto algorithms can be used for highly resource constrained applications such as RFID. To provide the security mechanism such as integrity and authentication between the RFID tag and the RFID reader, crypto algorithm or security protocol should be implemented at the RFID tag and reader. Moreover, the security mechanism in the RFID tag should be implemented with low power consumption because the resources of RFID tag are highly constrained, that is, the RFID tag has low computing capability and the security mechanism which operates in the RFID tag should be low power consumed. After investigating the power consumption characteristics of our designed crypto processors, we will conclude which crypto algorithm is suitable for providing the security of RFID systems. Keywords: Crypto Coprocessor, Crypto Algorithm, Power Consumption. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel solution called joint encryption, in which audio and video data are scrambled efficiently by using modified phase scrambling, modified motion vector, and wavelet tree shuffling. Experimental results indicate that the proposed joint encryption technique is very simple to implement, has no adverse impact on error resiliency and coding efficiency, and produces level of security. [ABSTRACT FROM AUTHOR]

In this paper, a new public-key broadcast encryption scheme, which is asymmetric and without involvement of trusted third parties, is proposed by using the "subset-cover" framework and the identity-based encryption scheme of Boneh and Franklin. This scheme is the first concrete construction of an asymmetric identity-based public-key broadcast encryption that does not rely on trusted agents and the costly Oblivious Polynomial Evaluation mechanism. Moreover, this novel work contains other desirable features, such as efficient encryption and decryption, low memory requirements, traitor tracing, asymmetry and non-repudiation. The tracing algorithm of this system is more efficient than that of the previous ones. [ABSTRACT FROM AUTHOR]

Character skeleton plays a significant role in character recognition. This paper presents a novel algorithm based on multiscale approach to extract skeletons of printed and hand-written characters. The development of the method is inspired by some desirable characteristics of the modulus minima of wavelet transform. Namely, the local minima of wavelet transform are scale-independent and locate at the medial axis of the symmetrical contours of character stroke. Thus it is particularly suitable for characterizing the inherent skeletons of character strokes. The proposed skeletonization algorithm contains two major steps. First, by thresholding for the modulus minima of wavelet transform, the modulus minima points underlying the character strokes are extracted as the primary skeletons. Based on these primary skeletons, the modulus minima points are being eventually computed as the final skeleton by iteratively performing wavelet transform. The skeleton form the proposed method can be exactly located on the central line of the stroke, and the artifacts and branches of skeletons from traditional methods can be avoided. We tested the algorithm on handwritten and printed character images. Experimental results indicate that the proposed algorithm is applicable to not only binary image but also gray-level image. [ABSTRACT FROM AUTHOR]

This paper presents an efficient control approach for the elevator group control system. The essential of the method used is based on an operation strategy with a talented algorithm. In order to analyze the performance of the presented system, the control method was evaluated by considering different performance characteristics in the elevator group control system. The results of the presented method compared with the results of the area weight algorithm. [ABSTRACT FROM AUTHOR]

Many secure electronic payment protocols have been proposed, most of which are based on public key cryptograph. These protocols, however, are not suitable for mobile network due to the limitations of mobile environments, such as limited computation capability of mobile devices, limited bandwidth, etc. In this paper, we propose a private and efficient payment protocol for mobile network, which only involves symmetric key algorithm, such as symmetric encryption, hash function and keyed hash function. All these operations can be implemented on mobile devices feasibly. The proposed protocol also achieves completely privacy protection of buyers, which is one of the important requirements in mobile commerce. First, the identity of the buyer is protected from the merchant. Second, the transaction privacy of the buyer, such as what the buyer buys, and whom the buyer buys from, are also protected from any other parties and financial institutions. By giving a security analysis, we show that our protocol satisfies all security requirements in electronic payment. [ABSTRACT FROM AUTHOR]

An automatic classification system of Korean traditional music is proposed using robust multi-feature clustering method. The system accepts query sound and automatically classifies input query into one of the six Korean traditional music categories. This paper focuses on the feature optimization method to alleviate system uncertainty problem due to the different query patterns and lengths, and consequently increase the system stability and performance. In order to fit this needs, a robust feature optimization method called multi-feature clustering (MFC) based on VQ and SFS feature selection is proposed. Several pattern classification algorithms are tested and compared in terms of the system stability and classification accuracy. [ABSTRACT FROM AUTHOR]

The issues of trust are especially of great importance in peer-to-peer electronic online communities [5]. One way to address these issues is to use community-based reputations to help estimate the trustworthiness of peers. This paper presents a reputation-based trust supporting framework which includes a mathematical trust model, a decentralized trust data dissemination scheme and a distributed implementation algorithm of the model over a structured P2P network. In our approach, each peer is assigned a unique trust value, computed by aggregating the similarity-filtered recommendations of the peers who have interacted with it. The similarity between peers is computed by a novel simplified method. We also elaborate on decentralized trust data management scheme ignored in existing solutions for reputation systems. Finally, simulation-based experiments show that the system based on our algorithm is robust even against attacks from groups of malicious peers deliberately cooperating to subvert it. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of robust reliable control design for a class of fuzzy uncertain systems with time-varying delay. The system under consideration is more general than those in other existent works. A reliable fuzzy control design scheme via state feedback is proposed in terms of linear matrix inequality (LMI). The asymptotic stability of the closed-loop system is achieved for all admissible uncertainties as well as actuator faults. A numerical example is presented for illustration. [ABSTRACT FROM AUTHOR]

Compared with spatial domain motion compensated temporal filtering (MCTF) scheme, in-band MCTF scheme needs more coding bits for motion information since the motion estimation (ME) and motion compensation (MC) are implemented on each spatial subband. Therefore, how to employ motion prediction and coding is a key problem to improve the coding efficiency of in-band MCTF. In this paper, we proposed an efficient level-by-level modebased motion prediction and coding scheme for in-band MCTF. In our scheme, three motion prediction and coding modes are introduced to exploit the subband motion correlation at different resolution as well as the spatial motion correlation in the high frequency subband. To tradeoff the complexity and the accuracy of block-based motion search, a jointly rate-distortion criterion is proposed to decide a set of optimized motion vector for three spatial high frequency subbands at the same level. By the rate-distortion optimized mode selection engine, the proposed scheme can improve the coding efficiency about 0.6db for 4CIF sequence. [ABSTRACT FROM AUTHOR]

The scaling behavior has been discovered in the past decade, which has provided hope that mathematical models can be found to describe the nature of the traffic. Similarly to long-range dependence (LRD), nonstationarity is also one of vital characteristics of network traffic. In this paper, a novel traffic model is proposed based on that the traffic aggregation behavior is abstracted in hierarchical way. The traffic model is focused on the burst traffic rate. Firstly, the burst size of output aggregated flow by edge device of a network domain is derived by pseudo queue system methods. And, the nonstationarity of input traffic is developed by a generalized fractal Gaussian noise process, which is constructed by a large number of train traffic series. They are Poisson arrival and their lifetime is exponential distribution. The model has a good performance of fitting to real traffic data within multi-scale for long time, which is illuminated by simulated results. [ABSTRACT FROM AUTHOR]

Phase field models provide a well-established framework for the mathematical description of free boundary problems for image segmentation. In phase field models interfaces represent edges of jacquard images and the determination of the edges of jacquard images is the main goal of image segmentation. In this paper, the phase field model was applied to segment and recognize pattern structures of jacquard images. The segmentation was performed in two major steps. Firstly, a pattern extraction and representation was performed by an adaptive mesh generation scheme. For the conjugate gradient method has been successfully used in solving the symmetric and positive definite systems obtained by the finite element approximation of energy functionals, a novel conjugate gradient algorithm was adapted to the minimization of energy functional of discrete phase model. Experimental results show efficiency of our approach. [ABSTRACT FROM AUTHOR]

In this paper, an efficient adaptive de-blocking algorithm is proposed to reduce blocking artifacts. Blocking artifacts are modeled as step functions and then the image blocks are divided into three categories: smooth blocks, texture blocks and edge blocks. For smooth blocks, the expression of amplitude of blocking artifacts is educed firstly in our algorithm, and then the adaptive smooth filter according to the amplitude of blocking artifacts and the smooth degree function is proposed to reduce blocking artifacts. For the texture blocks and edge blocks, the Sigma filter is used to smooth the block boundaries. The experiment results show that the proposed algorithm reduces the blocking artifacts effectively and preserves the original edges faithfully. [ABSTRACT FROM AUTHOR]

This paper presents a fast stereo matching algorithm using SUSAN window. The response of SUSAN window is used to calculate the dissimilarity cost. From this dissimilarity cost, an initial match can be found. Then, with this initial match, a dynamic programming algorithm searches for the best path of two scan lines. Since the proposed dissimilarity cost calculation method is very simple, and does not make use of any complicated mathematic formula, its running time is almost as same as SAD in the fixed window. In addition, the proposed matching algorithm only has two control parameters, bright threshold and occlusion penalty, which make it to be easily optimized. [ABSTRACT FROM AUTHOR]

This paper proposes a novel digital auto-focusing algorithm using image fusion, which restores an image with out-of-focus objects. Instead of designing an image restoration filter for auto-focusing, we propose an image fusion-based auto-focusing algorithm by fusing multiple, restored images based on regularized iterative restoration. The proposed auto-focusing algorithm consists of (i) sum-modified-Laplacian (SML) for obtaining salient focus measure, (ii) iterative image restoration, (iii) auto focusing error metric (AFEM) for optimal restoration(iv) soft decision fusion and blending (SDFB) which enables smooth transition across region boundaries. By utilizing restored images at consecutive levels of iteration, the soft decision fusion and blending algorithm can restore images with multiple, out-of-focus objects. An auto-focusing error metric is used to provide an appropriate termination point for iterative restoration. [ABSTRACT FROM AUTHOR]

Hough Transform (HT) is a powerful tool to detect straight lines in noisy images since it is a voting method. However, there is no effective way to detect line segments and dominate points, which are more important in pattern recognition and image analysis. In this paper, we propose a simple way to detect lines segments and dominate points simultaneously in binary images based on HT using generalized labelling. The new framework firstly detects straight lines using HT and then labels each black point of the image by considering the discrete errors of HT. Finally, the connectivity among the points having the same labels is checked in order to reduce the effect of noises and detect line segments properly. The experimental results show that our new framework is an powerful and effective way to detect line segments and dominate points in noisy binary images. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel blind adaptive multiuser detector using the recursive least squares (RLS) algorithm based on singular value decomposition (SVD) for code division multiple access systems. The new presented algorithm can overcome the disadvantages of numerical instability and divergence of the conventional RLS algorithm. Simulation results show that the novel SVD-based RLS algorithm is superior to the conventional RLS algorithm in convergence rate, numerical stability and robustness. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of how to quantize the wavelet coefficients in the lowest frequency subband with non-uniform scalar method. A novel wavelet image coding algorithm based on non-uniform scalar quantization is proposed. This algorithm adopts longer step to quantize the wavelet coefficients in the lowest frequency subband and uses shorter step for other ones. According as the results of the experiment we design a coding approach by using two labels 0 or 1 to code a coefficient bit of decimal plane. Experiment results have shown the proposed scheme improves the performance of wavelet image coders. In particular, it will get better coding gain in the low bit rate image coding. [ABSTRACT FROM AUTHOR]

This paper shows that an algorithm technique involving image classification and valley-edge based image segmentation is a highly efficient way of delineating densely packed rock fragments. No earlier work on segmentation of rock fragments has exploited these two building blocks for making robust segmentation. Our method has been tested experimentally for different kinds of closely packed fragment images which are difficult to detect by ordinary edge detections. The reason for the powerfulness of the technique is that image classification (knowledge of scale) and image segmentation are highly cooperative processes. Moreover, valley-edge detection is a nonlinear filter picking up evidence of valley-edge by only considering the strongest response for a number of directions. As tested, the algorithm can be applied into other applications too. [ABSTRACT FROM AUTHOR]

The traditional methods of optical flow estimation have some problems, such as huge computation cost for the inverse of time-varying Hessian matrix, aperture phenomena for the points with 1D or little texture and drift phenomena with long sequences. A novel nonrigid object tracking algorithm based on inverse component uncertainty factorization subspace constraints optical flow is proposed in this paper, which resolves the above problems and achieves fast, robust and precise tracking. The idea of inverse Component is implemented in each recursive estimation procedure to make the algorithm fast. Uncertainty factorization is used to transform the optimization problem from a hyper-ellipse space to a hyper-sphere space. SVD is correspondingly performed to involve the subspace constraints. The proposed algorithm has been evaluated by both the standard test sequence and the consumer USB camera recorded sequence. The potential applications vary from articulated automation to structure from motion. [ABSTRACT FROM AUTHOR]

In wavelet-based video coding with motion-compensated lifting, efficient compression is achieved by exploiting motion-compensated temporal filtering (MCTF). The performance of a 3D wavelet video codec is greatly dependent on the efficient of MCTF. In this paper, an adaptive motion-compensated temporal filter scheme is proposed for wavelet video coding. Our method focused to control the level number of the temporal decomposition by detecting the number of the un-connection pixels in low-pass frame to avoid the inefficiency of MCTF while scene changing quickly. Moreover, this method has most useful features of predictive wavelet codecs, such as multiple reference frame and bi-directional prediction. Our experimental results show that, compared with the conventional MCTF, the proposed scheme has better coding performance for most sequences. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel fast motion estimation algorithm based on successive elimination algorithm (SEA), which can dramatically reduce complexity of the variable block size motion estimation by removing the unnecessary computation of SAD in H.264 encoder. The proposed method, which accumulates current sum norms and pre-computed SAD for the bigger block sizes than 4×4 drives tighter bound in the inequality than an ordinary SEA depending on the availability of SAD. Experimental results explain that our method reduces computation complexity. In addition, the proposed method is an extended version of the rate constrained block matching for variable block-sized applications. It surely works on variable block-based motion estimation with just a little degradation. [ABSTRACT FROM AUTHOR]

In this paper, we propose a simple method for designing M-channel near-prefect-reconstruction (NPR) cosine-modulated filter banks (FBs). By employing the Parks-McClellan algorithm in constructing the prototype filter, an ideal magnitude response of the filter is achieved. Furthermore, the transition band of the prototype filter is constrained in such a way that it follows a cosine function. As a result, the FBs are approximately power complementary and therefore possess the NPR property. There are two main advantages in our proposed method. The first one is that no objective function in the optimization is explicitly required and the second one is that the resulting prototype filter is a global optimal solution. Compared with the traditional design method using general optimization methodology, the proposed method here is very simple and efficient. In addition, the stopband attenuation of the resulting FB is significantly higher than those offered by the traditional methods. [ABSTRACT FROM AUTHOR]

This paper presents the improved algorithm according to image compression technology to pledge the real time of the image transmission and gain the high compression ratio under the image quality. The improved SPIHT image coding algorithm based on fast lifting wavelet transform presents fast lifting wavelet transform to improve trasform course, because of many consecutive zero appearing in SPIHT quantification coding, adopting the simultaneous encoding of entropy and SPIHT. Entropy coding adopts run-length-changeable coding. Experiment results show that encoding by this method can improve PSNR and efficiency. This method can apply in the image data transmission and storage of remote image surveillance systems. [ABSTRACT FROM AUTHOR]

Component detectors can accurately locate facial components, and component-based approaches can be used to build detectors that can handle partial occlusions. This paper proposes a face detection and verification method using component-based online learning. The main difference from previously reported component-based approaches is the use of online learning, which is ideal for highly repetitive tasks. This results in faster and more accurate face detection, because system performance improves with continued use. Further, uncertainty is added by calculating the standard deviation of face components and their relations. [ABSTRACT FROM AUTHOR]

The removing of specular highlight spots has been the hotspot in the field of computer vision. A variety of methods have been designed to remove the specular on the surface of the objects. Unfortunately few paper on removal of (specular) highlight spots on the metal surface have been reported. Since the region of highlight spots on metal surface is much larger than that of non-metal, it is harder to remove them. We propose a novel Inpainting with Multi-Sources-Flashing method to resolve the problem. The outcome of the experiments demonstrate that the light-spots on the surface of the metal objects with complex texture are removed effectively, and the physical property of the surface maintains well. The pyramid structure of our algorithm flow makes the innovation function efficiently. [ABSTRACT FROM AUTHOR]

The recognition of vowels in Chinese speech is very important for Chinese speech recognition and understanding. However, it is rather difficult and there has been no efficient method to solve it yet. In this paper, we propose a new approach to the recognition of Chinese vowels via the support vector machine (SVM) with the Mel-Frequency Cepstral Coefficients (MFCCs) as the vowel's features. It is shown by the experiments that this method can reach a high recognition accuracy on the given vowels database and outperform the SVM with the Linear Prediction Coding Cepstral (LPCC) coefficients as the vowel's features. [ABSTRACT FROM AUTHOR]

In this paper, a robust lane detection approach, that is primary and essential for driver assistance systems, is proposed to handle the situations where the lane boundaries in an image have relatively weak local contrast, or where there are strong distracting edges. The proposed lane detection approach makes use of a deformable template model to the expected lane boundaries in the image, a maximum a posteriori (MAP) formulation of the lane detection problem, and a particle swarm optimization algorithm to maximize the posterior density. The model parameters completely determine the position of the vehicle inside the lane, its heading direction, and the local structure of the lane. Experimental results reveal that the proposed method is robust against noise and shadows in the captured road images. [ABSTRACT FROM AUTHOR]

This paper proposes a novel revocable two-factor authentication approach which combines user-specific tokenized pseudo-random bit sequence with biometrics data via a logic operation. Through the process, a distinct binary code per person, coined as bio-Bit, is formed. There is no deterministic way to acquire bio-Bit without having both factors. This feature offers an extra protection layer against biometrics fabrication since bio-Bit authenticator is replaceable via token replacement. The proposed method also presents functional advantages of obtaining zero equal error rate and yielding a clean separation between the genuine and imposter populations. Thereby, false accept rate can be eradicated without suffering from the increased occurrence of false reject rate. [ABSTRACT FROM AUTHOR]

This paper presents a novel dynamic face and fingerprint fusion system for identity authentication. To solve the face pose problem in dynamic authentication system, multi-route detection and parallel processing technology are used in this system. A multimodal part face recognition method based on principal component analysis (MMP-PCA) algorithm is adopted to perform the face recognition task. Fusion of face and fingerprint by SVM (Support Vector Machine) fusion strategy which introduced a new normalization method improved the accuracy of identity authentication system. Furthermore, key techniques such as fast and robust face detection algorithm and dynamic fingerprint detection and recognition method based on gray-Level histogram statistic are accepted to guarantee the fast and normal running. Practical results on real database proved that this authentication system can achieve better results compared with face-only or fingerprint-only system. Consequently, this system indeed increases the performance and robustness of identity authentication systems and has more practicability. [ABSTRACT FROM AUTHOR]