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Recognition of road guidance signs is an important issue for developing driving assistance systems. One of major problems in this field is recognition of junction structure from road signs. In the proposed paper both detection and recognition problems are presented. Detection method based on color and shape properties of sign allows detecting signs in various lighting and weather conditions. Using interlaced images for most time-consuming operations and full-resolution images for final result reduces computation time without loss of quality. Recognition method is based on decomposition of guidance signs into principal components and representation of arrow region as a graph. Path extraction uses finite automate methodology which in order to recognize all possible paths to pass the junction. Proposed method showed processing speed about 10 fps and can be used in real-time applications. [ABSTRACT FROM AUTHOR]

A new method of motion deadlock resolving by using fuzzy decision in robot soccer games is proposed in this paper. For the reasons of complex competition tasks and limited intelligence, soccer robots fall into motion deadlocks in many conditions, which is very difficult for robots to decide whether it is needed to retreat for finding new opportunities. Based on the analysis of human decision for dealing with these kinds of motion deadlocks, the fuzzy decision method is introduced in this paper. Then, fuzzy rules based deadlock resolving system is designed according to relative positions and orientations among robots and the ball in local regions. Lots of experiments by human experts and the fuzzy controller are implemented for comparison. Experimental results show that the method proposed is reasonable and efficient for motion deadlock resolving in most conditions for real soccer robot games. [ABSTRACT FROM AUTHOR]

Based on the research of MDS array code of size n(n in distributed storage system, in this paper, we present a novel encoding scheme called the F-code and prove that the column distance of the F-code is 3, i.e. F-code is a MDS array code given that odd number n is greater than 3 and does not include factor 3. And we also implement a novel decoding algorithm of the F-code. The algorithm only needs two decoding chains in each linear equation group and is able to recover all unknown symbols on two erasure columns. The analysis of F-code shows that our method extends the range of number n in n×n MDS array code and gets lower/reduction algorithmic complexity. Therefore, the reliability of a distributed storage system that features the F-code can be effectively reinforced. [ABSTRACT FROM AUTHOR]

This paper presents a robust real-time face alignment algorithm based on Active Appearance Models(AAMs). Fitting an AAM to an image is considered to be a problem of minimizing the error between the input image and the closest model instance. If the input image is far from the model space, the fitting process will fail. This can always occur in application because of illumination variation. So, building a good appearance space is very important. We propose a weighted cost function which can incorporate intensity and edgeness of an image into AAMs framework. To achieve high performance, Active Appearance Models proposed by Iain Matthews is employed. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm based on fuzzy support vector machine (FSVM), a new pattern analysis method, for detecting the abnormal trajectory patterns of moving objects from surveillance video. Firstly, feature points are extracted for presenting continuous trajectories. Then fuzzy memberships are introduced to measure contributions of the feature points of trajectory. Finally, the algorithm is applied to detect the abnormal patterns in 2D object trajectories. Experiments on trajectory data set show the validity of the algorithm. [ABSTRACT FROM AUTHOR]

In this paper medical applications of supervised and unsupervised neural networks image processing algorithms are presented and discussed by means of quantitative experimental results in the field of radiotherapy. The investigated case study concerns the problems and the consequent solutions referred to the two phases of the treatment plan necessary after the quadrantectomy of a cohort of patients affected by breast cancer. [ABSTRACT FROM AUTHOR]

In this paper, we propose the novel feature extraction method, called the Polar wavelet, which can improve the search performance for locally distributed time series data. Among various feature extraction methods, the Harr wavelet has been popularly used to extract features from time series data. However, the Harr wavelet does not show the good performance for sequences of similar averages. The proposed method uses polar coordinates which are not affected by averages and can reduce the search space efficiently without false dismissals. The experiments are performed on real temperature dataset to verify the performance of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, Umeyama's eigen-decomposition approach to weighted graph matching problems is critically examined. We argue that Umeyama's approach only guarantees to work well for graphs that satisfy three critical conditions: (1) The pair of weighted graphs to be matched must be nearly isomorphic; (2) The eigenvalues of the adjacency matrix of each graph have to be single and isolated enough to each other; (3) The rows of the matrix of the corresponding absolute eigenvetors cannot be very similar to each other. For the purpose of matching general weighted graph pairs without such imposed constraints, we shall propose an approximate formula with a theoretical guarantee of accuracy, from which Umeyama's formula can be deduced as a special case. Based on this approximate formula, a new algorithm for matching weighted graphs is developed. The experimental results demonstrate great improvements to the accuracy of weighted graph matching. [ABSTRACT FROM AUTHOR]

The Minimum Bit Error Rate (MBER) detectors outperform the conventional Minimum Mean Squared Error (MMSE) detector by minimizing the Bit Error Rate (BER) directly. In this paper an MBER multiuser detector for Orthogonal Frequency Division Multiplexing-Space Division Multiple Access (OFDM-SDMA) system is proposed employing Particle Swarm Optimization (PSO) for finding the optimum weight vectors. Simulation results show that the proposed system achieves faster convergence with lower complexity as compared to Genetic Algorithms (GA) with same Bit Error Rate (BER) performance. [ABSTRACT FROM AUTHOR]

This paper presents an speech information hiding model for transmitting secret speech through subliminal channel covertly for secure communication over PSTN (Public Switched Telephone Network) or VoIP (Voice over Internet Protocol). This model's main statement is that the embedding operation of a secure communication system should work indeterminate from the attacker's point of view. This model for secure communication based on the technique of information hiding has more severe requirements on the performances of data embedding than watermarking and fingerprinting in the aspects of real time, hiding capacity, and speech quality. Experiments show that this model meets the requirement of secure communication and suits for practical application of covert communication. The security analysis of this model by means of information theory and actual test proved that it is theoretically and practically secure. This information theory based model can be commonly used to help design a system of speech secure communication with different coding schemes. [ABSTRACT FROM AUTHOR]

This paper presents an approach for speech information hiding based on G.721 scheme. This approach proposes an Analysis-By-Synthesis (ABS)-based speech information hiding and extracting algorithm, is called ABS algorithm, which form the theoretical basis for designing a secure speech communication system. The ABS algorithm adopts a speech synthesizer in a speech coder. Speech embedding and coding are synchronous, i.e. fusing of secret speech information data into speech coding. Dynamic secret speech information data bits can be embedded into original carrier speech data, with high efficiency in steganography and good quality in output speech. This method is superior to available classical algorithms on hiding capacity and robustness. This paper implements the proposed approach based on speech coding scheme G.721 and the experiments show that this approach meets the requirements of information hiding, satisfies the constraints of speech quality for secure communication, and achieves high hiding capacity of 1.6Kbps with an excellent speech quality and complicating speakers' recognition. [ABSTRACT FROM AUTHOR]

In this paper, an efficient approach to search for the global threshold of image using Gaussian mixture model is proposed. Firstly, a gray-level histogram of an image is represented as a function of the frequencies of gray-level. Then to fit the Gaussian mixtures to the histogram of image, the Expectation Maximisation (EM) algorithm is developed to estimate the number of Gaussian mixture of such histograms. Finally, the optimal threshold which is the average of these means is chosen. The paper compares the new method with the classical discriminate analysis method of Otsu's. And the experimental results show that the new algorithm performs better than that of Otsu's. [ABSTRACT FROM AUTHOR]

The problem of face feature points detection is an important research topic in many fields such as face image analysis and human-machine interface. In this paper, we propose a robust method of 2D nose detection and tracking system. This system can be valuable for disabled people or for cases where hands are busy with other tasks. The required information is derived from video data captured with an inexpensive web camera. Position of the nose tip is determined with the use of a Gabor wavelet feature based GentleBoost detector. Once the nose tip is initially located, an improved Lucas-Kanade optical flow method is used to track the nose tip feature point. Experiments show that our system is able to process 18 frames per second at a resolution of 320×240 pixels. This method will in future be used in a non-contact interface for disabled users. [ABSTRACT FROM AUTHOR]

New proteomic technologies have brought the hope of discovering novel early cancer-specific biomarkers in complex biological samples. Novel mass spectrometry (MS) based technologies in particular, such as surface-enhanced laser desorption/ionisation time of flight (SELDI-TOF-MS), have shown promising results in recent years. To find new potential biomarkers and establish the patterns for detection of cancers, we proposed a novel method to analysis SELDI-TOF-MS using binary cross section imaging and energy curve technology. The proposed method with advantage of visualization is to mining local information adequately so as to discriminate cancer samples from non-cancer ones. Applying the procedure to MS data of proteomic analysis of serum from ovarian cancer patients and serum from cancer-free individuals in the Food and Drug Administration/National Cancer Institute Clinical Proteomics Database, we find that there are outputs of the cancerous when the threshold is above 90 and M/Z is in the range 9362.3296-9747.2723, while outputs of the non-cancerous will appear when the threshold is 60 80 and M/Z is 243.4940-247.8824. [ABSTRACT FROM AUTHOR]

We have realized an online gesture recognition platform for hand gestures using 2-channel surface EMG signals acquired from the forearm. Several features, such as AMV, AMV ratio and fourth-order AR model coefficients are extracted from the sEMG signal and the gesture segments are recognized with a Weighted Euclidean Distance Classifier. An above 90% recognition rate has been achieved with only a 400 μs recognition time. The methods developed in this study are aimed to be applied in a fast-response sEMG control system and be transplanted into an embedded microprocessor system. [ABSTRACT FROM AUTHOR]

This paper suggests 3D co-occurrence texture features by extending the concept of co-occurrence feature to the 3D world. The suggested 3D features are described as a 3D co-occurrence matrix by using a co-occurrence histogram of digital elevations at two contiguous positions. With the addition of 3D co-occurrence features, we encounter the high dimensionality problem in the classification process. Since these ANN (Artificial Neural Networks) clustering algorithms are known as robust in this situation, FCM (Fuzzy C-mean) and GBFCM (Gradient Based Fuzzy C-mean) clustering algorithms are employed to implement the terrain classifier. Experimental results show that the classification accuracy with the addition of 3D co-occurrence features is significantly improved over the conventional classification method only with 2D features. [ABSTRACT FROM AUTHOR]

Natural and efficient HCI technology has been rapidly developing. However, there is always cognitive 'gap' between HCI and application space. Taking command post of the future as main researching object, the paper explores how to properly combine information modeling with HCI technology and realize the proper abstraction, description and mapping of command space element, according to an order from 'top' to 'down': Cognition level, Operation level, Control level and Device level. Starting with conceptual model, command space is modeled on the cognition level. Next, basic methodology are brought forward, which combine command space cognitive concept and map them to operation and control, based on multi-fingers/two-handed touch interaction. This paper focuses on the modeling method of user multi-fingers touch operation model, and the control method from user operation model to multi-fingers touch HCI, to realize conversation between user and device in command space, to improve efficiency of commanding operation and decision-making. The research above can provide theory and method for constructing command and decision-making space of the future and has forerunner effect on multi-modal HCI system. [ABSTRACT FROM AUTHOR]

This paper describes a method to know objects in outdoor environment for autonomous robot navigation. The proposition of the method segments and recognizes the object from an image taken by moving robot in outdoor environment. Features are color, straight line, edge, HCM (Hue Co-occurrence Matrix), PCs (Principal Components), vanishing point and geometrical information. We classify the object natural and artificial. We detect tree of natural object and building of artificial object.Then we define their characteristics individually. In the process, we segment regions objects included by preprocessing. Objects can be recognized when we combine predefined multiple features. The correct object recognition of proposed system is over 92% among our test database which consist about 1200 images. We confirm the result of image segmentation using multiple features and object recognition through experiments. [ABSTRACT FROM AUTHOR]

In this paper, selecting line segments as matching features in stereo vision, the orientation difference (O.D.) of the line segments is more deeply evaluated than the previous studies and two new constraints i.e. ordering and collinearity are proposed. The O.D. was supposed to be used for an indoor application, reducing candidates without elimination of the actual matches, especially if the line is horizontal or vertical with respect to the floor. The findings of this paper are as follows: 1) Applying a threshold on O.D. would result in a missing probability for the actual matches and this probability can be calculated for any given threshold, 2) The upper limit of O.D. for horizontal and vertical lines is a function of geometric parameters of the system, 3) An optimal tilt angle, whose results is the minimum upper limit for O.D., can be computed, 4) for disambiguation process, ordering and collinearity constraints are proposed. These are applied in a matching algorithm and their effectiveness is investigated on real stereo images. [ABSTRACT FROM AUTHOR]

In many cases human identification biometrics systems are motivated by real-life criminal and forensic applications. Some methods, such as fingerprinting and face recognition, proved to be very efficient in computer vision based human recognition systems. In this paper we focus on novel methods of human identification motivated by the forensic and criminal practice. Our goal is to develop computer vision systems that would be used to identify humans on the basis of their lips, palm and ear images. [ABSTRACT FROM AUTHOR]

In this paper, we proposed image processing technique for automatic real time fire and smoke detection in tunnel environment. To avoid the large scale of damage of fire occurred in the tunnel, it is necessary to have a system to minimize and to discover the incident as fast as possible. However it is impossible to keep the human observation of Closed-Circuit Television (CCTV) in tunnel for 24 hour. So if the fire and smoke detection system through image processing can warn fire state, it will be very convenient, and it can be possible to minimize damage even when people is not in front of monitor. The fire and smoke detection is different from the forest fire detection as there are elements such as car and tunnel lights and others that are different from the forest environment so that an indigenous algorithm has to be developed. The two algorithms proposed in this paper, are able to detect the exact position, at the earlier stage of incident. In addition, by comparing properties of each algorithm throughout experiment, we have proved the validity and efficiency of proposed algorithm. [ABSTRACT FROM AUTHOR]

The quality of character image plays an important role for the performance of character recognition system. However there is no good way to measure the recognition difficulty of a given character image. For the given character image with unknown quality, it is improper that apply the single character database to recognize it by the same feature and the same classifier. This paper proposed a novel approach for multi-resolution character recognition whose feature is extracted directly from gray-scale image and classification is adaptive classification which adaptively selects the appropriate character database and classifiers by evaluating the image quality of the input character. A resolution evaluation algorithm based on gray distribution feature was proposed to decide the adaptive classification weights for the classifiers, which make the classification have the higher probability of being the correct decision. Experiment results demonstrate the proposed approach highly improved the performance of character recognition system. [ABSTRACT FROM AUTHOR]

In this paper, the line segment tracking is considered for an imaging system with translational motions (T.M.), rotational motions (R.M.) and arbitrary motions. Assuming a CCD camera with an arbitrary tilt angle installed on a mobile robot equipped with odometry system, to reduce the search space in the correspondence problem, two constraints are developed. These constraints are location and orientation differences (O.D.) for line segments in consecutive images. The findings of this paper include: 1)The development of the effect of camera tilt on the location constraint for T.M., 2)Illustrating that the upper bound of O.D. for both horizontal and vertical lines with respect to the floor, is a function of tilt in T.M., which can considerably be reduced and thus providing a tight constraint, 3)The development of the location constraint for R.M., 4) The development of the PDF and upper bound of O.D. for R.M., 5) The development of the location and O.D. constraint for arbitrary motions. Furthermore, the efficiency of these developed constraints in a line tracking algorithm was verified. [ABSTRACT FROM AUTHOR]

In speaker recognition fields, score normalization is a widely used and effective technique to enhance the recognition performances and is developing further. In this paper, we are focused on the comparison among many kinds of candidates of score normalization methods and a new implementation of the speaker adaptive test normalization (ATnorm) based on a cross similarity measurement is presented which doesn't need an extra corpus for speaker adaptive impostor cohort selection. The use of ATnorm for the language robustness of the multi-language speaker verification is also investigated. Experiments are conducted on the core task of the 2006 NIST Speaker Recognition Evaluation (SRE) corpus. The experimental results indicate that all the score normalization methods mentioned can improve the recognition performances and ATnorm behaves best. Moreover, ATnorm can further contribute to the performance as a means of language robustness. [ABSTRACT FROM AUTHOR]

Content-based image classification has always been a hot research topic. This paper aims to propose an efficient image analysis algorithm using geometric features of skin regions to effectively classify images. First, a nonparametric skin color classifier is used to skin detection. Then, the contours of skin regions are constructed using a curve evolution method based on adaptive grids. Finally, the geometric features are extracted from the contours, and the cosine similarity measure is adopted for image classification. The algorithm is tested on a large database consisting of 6000 images. Experimental results illustrate the proposed method perform well in classifying skin images. [ABSTRACT FROM AUTHOR]

This work introduces a new approach to detect fake fingers, based on the analysis of time-series fingerprint images. When a user puts a finger on the scanner surface, a time-series sequence of fingerprint images is captured. Five features are extracted from the image sequence. Two features represent the skin elasticity, and three features represent the physiological process of perspiration. Finally the Support Vector Matching (SVM) is used to discriminate the finger skin from other materials such as gelatin. The experiments carried out on a dataset of real and fake fingers show that the proposed approach and features are effective in fake finger detection. [ABSTRACT FROM AUTHOR]

Based on neural network with favorable adaptability to handwritten Chinese character multi-features, in this paper a new method is proposed, using existing multi-features as inputs to structure multi neural network recognition subsystems and these subsystems are integrated with parallel connection mode. The integrated system has the lowest false recognition rate. When using traditional von Neumann architecture computer to implement this system, the system response time is longer as a result of serial computation. This paper introduces a kind of parallel computation method of using pc cluster to implement multi subsystems. It can reduce effectively recognition system's response time. [ABSTRACT FROM AUTHOR]

Automatic reconstruction of patient-specific 3D bone model from a limited number of calibrated X-ray images is not a trivial task. Previous published works require either knowledge about anatomical landmarks, which are normally obtained by interactive reconstruction, or a supervised initialization. In this paper, we present an automatic 2D/3D reconstruction scheme and show its applications to reconstruct a surface model of the proximal femur from a limited number of calibrated X-ray images. In our scheme, the geometrical parameters of the proximal femur are obtained by using a Bayesian filter based inference algorithm to fit a parameterized multiple-component geometrical model to the input images. The estimated geometrical parameters are then used to initialize a point distribution model based 2D/3D reconstruction scheme for an accurate reconstruction of a surface model of the proximal femur. Here we report the quantitative and qualitative evaluation results on 10 dry cadaveric bones. Compared to the manual initialization, the automated initialization results in a little bit less accurate reconstruction but has the advantages of elimination of user interactions. [ABSTRACT FROM AUTHOR]

In this paper, a novel face recognition method based on binary face edges is presented to deal with the illumination problem. The Binary Face Edge Map (BFEM) is extracted using the Locally Adaptive Threshold (LAT) algorithm. Based on BEFM, a new image similarity metric is proposed. Experimental results show that face recognition rates of 76.32% and 82.67% are achieved respectively on 798 AR images and 150 Yale images with changed lighting conditions and facial expression variations when one sample per subject is used as the target image. The proposed method takes less time for image matching and outperforms some existing face recognition approaches, especially in changed lighting conditions. [ABSTRACT FROM AUTHOR]

In this paper we present the experiments and results obtained in the classification of infant cry using a variety of classifiers, ensembles among them. Three kinds of cry were classified: normal (without detected pathology), hypo acoustic (deaf), and asphyxia. The feature vectors were formed by the extraction of Mel Frequency Cepstral Coefficients (MFCC); these were then processed and reduced through the application of five statistics operations, namely: minimum, maximum, average, standard deviation and variance. For the classification there were used supervised machine learning methods as Support Vector Machines, Neural Networks, J48, Random Forest and Naive Bayes. The ensembles used were combinations of these under different approaches like Majority Vote, Staking, Bagging and Boosting. The 10-fold cross validationtechnique was used to evaluate precision in all classifiers. [ABSTRACT FROM AUTHOR]

In order to recognize the acoustic emission source with different characteristics, the parameter-ratio method was put forward to analyze the characteristic parameters of acoustic emission from different source further. According to the peak amplitude, counts, energy and rise-time, the three ratios of the amplitude to the energy difference, the amplitude to the counts difference and the amplitude to the rise-time difference were used as the parameter-ratios. Based on the matter-element of extension theory, a matter-element model was built to describe the characteristics of the acoustic emission. The dependent function and degree of the characteristics of the acoustic sources were introduced to evaluate the possibility of the acoustic sources. The acoustic sources can be recognized, putting forward the recognition rules of parameter-ratio method. The recognition example was taken to validate the parameter-ratio method. It is shown that the parameter-ratio method can recognize the acoustic emission source well. [ABSTRACT FROM AUTHOR]

Polygonal models have grown rapidly in complexity over recent years, yet most conventional simplification algorithms were designed to handle modest size datasets of a few tens of thousands of triangles. We present a parallel simplification method for large polygonal models. Our algorithm will partition the original model firstly, send each portion to a slave processor, simplify them concurrently, and merge them together lastly. We give an efficient method to deal with the problem of partition border and portion merging. With parallel implementation, the algorithm can handle extremely large data set, and speed up the execution time. Experiment shows that our algorithm can produce approximations of high quality. [ABSTRACT FROM AUTHOR]

The aim of this study is to identify the author of an unauthorized document. Ten different feature vectors are obtained from authorship attributes, n-grams and various combinations of these feature vectors that are extracted from documents, which the authors are intended to be identified. Comparative performance of every feature vector is analyzed by applying Naïve Bayes, SVM, k-NN, RF and MLP classification methods. The most successful classifiers are MLP and SVM. In document classification process, it is observed that n-grams give higher accuracy rates than authorship attributes. Nevertheless, using n-gram and authorship attributes together, gives better results than when each is used alone. [ABSTRACT FROM AUTHOR]

This paper presents a new description of image edge in the form of asymptote equation utilizing knowledge of differential geometry, and modifies a popular parlance in the traditional image processing. This paper develops the gradient concept and defines a generalized grads operator. The operator is a novel nonlinear transform, which inherits the strongpoint of noise suppression from the classical Gaussian differential filter and has a better edge extraction function. The experimental results show the proposed edge detection algorithm is powerful and effective. [ABSTRACT FROM AUTHOR]

We describe the architecture and implementation of bilateral background subtraction for real-time stereo vision system. Pre-smoothing a signal and noise removal may help to improve the performance for many signal-processing algorithms such as compression, detection, enhancement, recognition, and more [2]. Bilateral filtering proposed by C. Tomasi and R. Manduchi can be used as an edge-preserving smoother, removing high-frequency components of an image without blurring its edges [1][3]. Recently, [3] showed enhanced real-time stereo through software implementation of bilateral filtering. In this paper, we show hardware implementation of bilateral background subtraction for real-time stereo and present its architecture as well as required hardware resources. Also, we provide experimental results with real data and present our future works. [ABSTRACT FROM AUTHOR]

This paper presents a systematic isotropy analysis of a caster wheeled omnidirectional mobile robot (COMR) with three active caster wheels. Unlike previous analysis, no assumption is made on the relative scale of the steering link offset and the wheel radius. First, with the characteristic length introduced, the kinematic model of a COMR is obtained based on the orthogonal decomposition of the wheel velocities. Second, the necessary and sufficient isotropy conditions are examined to categorize three different groups to be handled in a similar way. Third, the isotropy conditions are further explored to identify four different sets of all possible isotropic configurations. Fourth, the characteristic lengths required for the isotropy of a COMR are obtained in a closed-form. Finally, the local and the global isotropy indices are used to determine the optimal design parameters. [ABSTRACT FROM AUTHOR]

From the Bayesian Ying-Yang (BYY) harmony learning theory, a harmony function has been developed for finite mixtures with a favorite property that its maximization can make model selection automatically during parameters learning. In this paper, we propose an adaptive gradient learning rule for maximizing the harmony function on Poisson mixtures which are applied more and more extensively in practice, especially for overdispersed data. It is demonstrated by simulation experiments that this adaptive gradient learning rule can determine the number of Poisson distributions during the parameters learning on a sample data set. [ABSTRACT FROM AUTHOR]

This paper presents a digital shot system based on three dimensions integrated accelerometer. The digital shot with almost the same size and weight as the standard shot for open female has been designed, fabricated and tested. The data collecting and processing system, human-machine interface and athlete train guiding system are illuminated in detail. By using wavelet transformation, the characteristics of acceleration signals during the shot-putting period can be extracted. In this manner, the force sensing system serves as a powerful tool for coaches and sports scientists to make scientific researches on shot-put techniques. It also provides an intuitive and reliable guidance for the shot-put athletes to improve their skills. [ABSTRACT FROM AUTHOR]

Two-dimension road following is one of the crucial tasks of vision navigation. For the reasons of environment complexity and the discrepancy between motion images, the robust outdoor road following for two-dimension image sequence is still a challenging task. This paper proposes a novel road following method, which firstly uses the Mean Shift algorithm with embedded edge confidence to partition the images into homogenous regions with precise boundary. Then, according to the color statistic information of the road/non-road model obtained from previous frames, the Graph Cuts (GC) algorithm is used to achieve the final binary images and update the road/non-road model simultaneously. This algorithm combines the advantages of Graph Cuts algorithm and Mean Shift algorithm, and effectively solves some difficult problems of conventional methods, such as the adaptive selection of road model under complex environments, and the choice of effective criteria for the region merging. Experiment results indicate our method possesses excellent performance under complicated environment, and meets the requirements of fast computing. [ABSTRACT FROM AUTHOR]

A novel method is developed for the dichromat's visual correction. This scheme includes three steps. Firstly, two similarity matrices are established respectively for the normal three-dimensional color space and the color plane in which dichromats can distinguish all the colors. Then a 3D-2D mapping relationship is searched based on these similarity matrices. Finally, the original color image is transformed to a new one which is suitable for dichromats. The experiments on both color test images and real images demonstrate the ability of the scheme for color blindness correction. With the fixed transformation of color space, this scheme may have capability to help training dichromats to recognize most colors. [ABSTRACT FROM AUTHOR]

A new algorithm for decomposition of mixed pixels based on orthogonal bases of data space is proposed in this paper. It is a simplex-based method which extracts endmembers sequentially using computations of largest simplex volumes. At each searching step of this extraction algorithm, searching for the simplex with the largest volume is equivalent to searching for a new orthogonal basis which has the largest norm. The new endmember corresponds to the new basis with the largest volume. This algorithm runs very fast and can also avoid the dilemma in traditional simplex-based endmember extraction algorithms such as N-FINDR which generally produces different sets of final endmembers if different initial conditions are used. Moreover, with this set of orthogonal bases, the proposed algorithm can also determine the proper number of endmembers and finish the unmixing of the original images which cannot be done by the traditional simplex-based algorithms. Experimental results of both artificial simulated images and practical remote sensing images demonstrate that the algorithm proposed in this paper is a fast and accurate algorithm for the decomposition of mixed pixels. [ABSTRACT FROM AUTHOR]

This paper introduces the idea that recognition and interpretation of engineering drawings should be two interwoven phases, with each providing feedback to another, and applies this idea to a dynamic-rule-based method. Recognition rules with attributes are obtained by an automatic object feature extraction procedure, and stored in rule database. During the recognition phase, rules are firstly selected according to two attributes, domain and priority. Then the thresholds of the rules are adjusted automatically to obtain better match results and their priorities are modified dynamically to improve recognition efficiency. Especially, the interpretation phase based on the recognition is also valued in validating and rectifying the recognition result automatically and efficiently. This approach was implemented in a system for recognizing and interpreting architectural structure drawings, and has shown to embody good self-adaptability to various drafting conventions. [ABSTRACT FROM AUTHOR]

Emotion is fundamental to human experience influencing cognition, perception and everyday tasks such as learning, communication and even rational decision-making. This aspect must be considered in human-computer interaction. In this paper, we compare four different weighting functions in weighted KNN-based classifiers to recognize five emotions, including anger, happiness, sadness, neutral and boredom, from Mandarin emotional speech. The classifiers studied include weighted KNN, weighted CAP, and weighted D-KNN. To give a baseline performance measure, we also adopt traditional KNN classifier. The experimental results show that the used Fibonacci weighting function outperforms than others in all weighted classifiers. The highest accuracy achieves 81.4% with weighted D-KNN classifier. [ABSTRACT FROM AUTHOR]

A new image coding method based on discrete directional wavelet transform (S-WT) and quadtree decomposition is proposed here. The S-WT is a kind of transform proposed in [1], which is based on lattice theory, and with the difference with the standard wavelet transform is that the former allows more transform directions. Because the directional property in a small region is more regular than in a big block generally, in order to sufficient make use of the multidirectionality and directional vanishing moment(DVM) of S-WT, the input image is divided into many small regions by means of the popular quadtree segmentation, and the splitting criterion is on the rate-distortion sense. After the optimal quadtree is obtained, a resource bit allocation algorithm is fast implemented utilizing the model proposed in [15]. Experiment results indicate that our algorithms perform better compared to some state-of-the-art image coders. [ABSTRACT FROM AUTHOR]

According to Cachin's steganography security criterion, if the statistical distributions of cover and stego images are identical, the hidden message is assumed undetectable. However, any steganographic method will surely cause some statistical distortions, which gives steganalyst a hint. This paper presents a steganalysis method for JPEG images based on Cachin criterion. It estimates the cover image from the stego one by using a small-scale geometrical transform, and then detects the statistical distortions between the cover and stego images based on some features, which are sensitive to the steganographic modifications. Then a classifier is trained on these features. Three different modern steganographic schemes are tested. Experimental results show that the proposed steganalysis scheme has better performance compared to the current steganalysis methods. [ABSTRACT FROM AUTHOR]

Filtering is a preliminary process in many medical image processing applications. It is aiming at reducing noise in images, and any post-processing tasks may benefit from the reduction of noise. The major two noises in computed radiography (CR) images are Gaussian white noise and Poisson noise. By considering both the characteristics of CR images and the statistical features of wavelet transformed coefficients, an efficient spatial adaptive filtering algorithm, which is based on statistical model of local dependency of CR image wavelet coefficients and the approximate minimum mean squared error (MMSE) estimation, is proposed to decrease the Gaussian white noise in computed images. The process is computational cost saving, and the denoising experiments show the algorithm outperforms other approaches in peak-signal-to-noise ratio (PSNR). [ABSTRACT FROM AUTHOR]

In design and realization of analog electronic circuit, we usually use preferred value components, the performance of practical circuits often deviate from the ideal design target due to rounding the calculated component values to preferred ones. The best combination of the preferred value components exists in general, but the searching space of all combinations of preferred-value components is very huge. Clonal Selection Algorithms (CSA) is a widely used approach for handling optimization problems. In this paper, CSA is applied into searching optimal components for 4th order Butterworth filter design. Simulation results demonstrate that the proposed method is much superior to the conventional means. This method also can be applied into other types of filter design. [ABSTRACT FROM AUTHOR]

Involvement of decision support systems in the process of selecting optimal decisions in transport logistics requires preliminary estimation of cargo delivery quality. The number of delivery criteria, their ranking according to degree of importance, and correlations among these criteria play an important role in the process of decision making. For the elaboration of effective approaches to the above mentioned tasks in the transport logistics we propose use of an intelligent system that applies methods from formal concept analysis and paraconsistent annotated logic. [ABSTRACT FROM AUTHOR]

A framework based on manifold learning in reconstructed state space is proposed as feature extraction means for nonlinear signal classification. On one hand, manifolds are of importance in characterizing chaotic attractors. On the other hand, there are a large number of toolkits in the context of manifold learning. These motivate us to apply manifold learning in reconstructed state space as feature extraction means for nonlinear signal classification, which bridges the gap between nonlinear science and manifold learning and enables a new viewpoint to study nonlinear signals. In this study, the nonlinear signal analysis is performed as follows. First, we embed the time series of interest into a high-dimensional space via state space reconstruction. Then, we employ locally linear embedding (LLE) to obtain the local manifold characteristics around every point in the reconstructed state space. Finally, we summarize all the local features into a global representation via principal component analysis (PCA). Two case studies of oceanic and EEG signal classification were carried out with the proposed scheme. As confirmed by the experiments, the proposed methodology is effective for such applications. This paper puts forward not only a feature extraction method but also a new direction in which a large number of toolkits are available for nonlinear signal analysis for the sake of signal classification. [ABSTRACT FROM AUTHOR]

In this paper, a frame rate up-conversion (FRC) algorithm using the motion vector frequency of neighboring blocks to reduce the block artifacts caused by failure of conventional motion estimation (ME) based on block matching algorithm (BMA) is proposed. The proposed method is based on the Spiral Full Search with early termination, which is applied to avoid the local minima on pattern-like image. Also, the motion vector correction that replaces a low frequency motion vector by a high frequency motion vector of neighborhood is used in the proposed method to reduce the block artifacts caused by the failure of conventional ME. In addition, bi-directional motion compensated interpolation (MCI) using the blocking index to reduce the block artifacts in occlusion area is used in the proposed method. Experimental results show good performance of the proposed scheme with significant reduction of the erroneous motion vectors and block artifacts. [ABSTRACT FROM AUTHOR]