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The design of self-organizing fuzzy control (SOFC) systems does not rely on experts' knowledge. The systems can establish fuzzy reasoning rules and adjust fuzzy parameters on-line automatically. So they are suitable for the control of plants for which we have no appropriate mathematical models and no other knowledge. There are three kinds of SOFCs, i.e., conventional, neural networks based and genetic algorithm based ones. New achievements of the above SOFC are summarized in this paper. [ABSTRACT FROM AUTHOR]

Biologically inspired soft computing paradigms such as neural networks are popular learning models adopted in adaptive control systems for their ability to cope with a changing environment. However, continual changes induce uncertainty that limits the applicability of conventional validation techniques to assure a reliable system performance. In this paper, we present a dynamic approach to estimate the performance of two types of neural networks employed in an adaptive flight controller: the validity index for the outputs of a Dynamic Cell Structure (DCS) network and confidence levels for the outputs of a Sigma-Pi (or MLP) network. Both tools provide statistical inference of the neural network predictions and an estimate of the current performance of the network. We further evaluate how the quality of each parameter of the network (e.g., weight) influences the output of the network by defining a metric for parameter sensitivity and parameter confidence for DCS and Sigma-Pi networks. Experimental results on the NASA F-15 flight control system demonstrate that our techniques effectively evaluate the network performance and provide validation inferences in a real-time manner. [ABSTRACT FROM AUTHOR]

The main idea of this paper is to characterize the uncertainties of control system base upon entropy concept. The wavelet neural networks is used to approach the nonlinear system through minimizing Renyi's entropy criterion of the system estimated error, and the controller design is based upon minimizing Renyi's entropy criterion of the system tracking errors. An illustrative example is utilized to demonstrate the effectiveness of this control solution, and satisfactory results have been obtained. [ABSTRACT FROM AUTHOR]

This paper addresses a new type of control framework for dynamical stochastic systems, which is called statistic tracking control here. General non-Gaussian systems are considered and the tracked objective is the statistic information (including the moments and the entropy) of a given target probability density function (PDF), rather than a deterministic signal. The control is aiming at making the statistic information of the output PDFs to follow those of a target PDF. The B-spline neural network with modelling error is applied to approximate the corresponding dynamic functional. For the nonlinear weighting system with time delays in the presence of exogenous disturbances, the generalized H2 and H∞ optimization technique is then used to guarantee the tracking, robustness and transient performance simultaneously in terms of LMI formulations. [ABSTRACT FROM AUTHOR]

In this paper, we present a new sliding mode controller for a class of unknown nonlinear discrete-time systems. We make the following two modifications: 1) The neural identifier which is used to estimate the unknown nonlinear system, applies new learning algorithms. The stability and non-zero properties are proved by dead-zone and projection technique. 2) We propose a new sliding surface and give a necessary condition to assure exponential decrease of the sliding surface. The time-varying gain in the sliding mode produces a low-chattering control signal. The closed-loop system with sliding mode controller and neural identifier is proved to be stable by Lyapunov method. [ABSTRACT FROM AUTHOR]

This paper considers the problem of stabilizing a black-box plant with time delay using an improved double controller scheme. The PID parameters of the load controller of the double-controller scheme are obtained by a neural network controller with back propagation algorithm. Based on the adaptive algorithm of Universal Learning Network (ULN), ULN is adopted for modeling the plant and being a predictor of the control system. Simulation results prove the applicability and effectiveness of the improved double-controller scheme. ULN and the neural network controller give the double-controller scheme more representing abilities and robust ability. [ABSTRACT FROM AUTHOR]

Combining multiple neural networks appears to be a very promising approach for improving neural network generalization since it is very difficult, if not impossible, to develop a perfect single neural network. Therefore in this paper, a nonlinear model predictive control (NMPC) strategy using multiple neural networks is proposed. Instead of using a single neural network as a model, multiple neural networks are developed and combined to model the nonlinear process and then used in NMPC. The proposed technique is applied to water level control in a conic water tank. Application results demonstrate that the proposed technique can significantly improve both setpoint tracking and disturbance rejection performance. [ABSTRACT FROM AUTHOR]

In this paper, a design procedure of support vector machine (SVM) with RBF kernel function based internal model control (IMC) strategy for stable nonlinear systems with input-output form is proposed. The control scheme consists of two controllers: a SVM based controller which fulfils the direct inverse model control and a traditional controller which fulfils the close-loop control. And so the scheme can deal with the errors between the process and the SVM based internal model generated by model mismatch and additional disturbance. Simulations are given to illustrate the proposed design procedure and the properties of the SVM based internal model control scheme for unknown nonlinear systems with time delay. [ABSTRACT FROM AUTHOR]

In this paper, a support vector machine (SVM) with polynomial kernel function enhanced nonlinear self-tuning controller is developed, which combines the SVM identifier and parameters' modifier together. The inverse model of a nonlinear system is achieved by off-line black-box identification according to input and output data. Then parameters of the model are modified online using gradient descent algorithm. Simulation results show that SVM based self-tuning control can be well applied to nonlinear uncertain system. And the SVM based self-tuning control of nonlinear system has good robustness performance in tracking reference input with good generalization ability. [ABSTRACT FROM AUTHOR]

A new control scheme using multiple models and RBF neural networks is developed in this paper. The proposed scheme consists of multiple feedback linearization controllers, which are based on the known nominal dynamics model and a compensating controller, which is based on RBF neural networks. The compensating controller is applied to improve the transient performance. The neural network is trained online based on Lyapunov theory and learning convergence is thus guaranteed. Simulation results are presented to demonstrate the validity of the proposed method. [ABSTRACT FROM AUTHOR]

This paper focuses on the design of passive controller with adaptive neural compensation for uncertain strict-feedback nonlinear systems with input-delay. For local linearization model, the delay-dependent γ-passive control is presented. Then, γ-passive control law of local linear model is decomposed as the virtual control of sub-systems by backstepping. In order to compensate the nonlinear dynamics, the adaptive neural model is proposed. The NN weights are turned on-line by Lyapunov stability theory with no prior training. The design procedure of whole systems is a combination of local γ-passive control and adaptive neural network compensation techniques. [ABSTRACT FROM AUTHOR]

In this paper, we address, in a backstepping way, stabilization problem for a class of switched nonlinear systems whose subsystem with trigonal structure by using neural network. An adaptive neural network switching control design is given. Backsteppping, domination and adaptive bounding design technique are combined to construct adaptive neural network stabilizer and switching law. Based on common Lyapunov function approach, the stabilization of the resulting closed-loop systems is proved. [ABSTRACT FROM AUTHOR]

A stable adaptive neural network control approach is proposed in this paper for uncertain nonlinear strict-feedback systems based on backstepping. The key assumptions are that the neural network approximation errors satisfy certain bounding conditions. By a special scheme, the controller singularity problem is avoided perfectly. The proposed scheme improves the control performance of systems and extends the application scope of nonlinear systems. The overall neural network control systems guarantee that all the signals of the systems are uniformly ultimately bounded and the tracking error converges to a small neighborhood of zero by suitably choosing the design parameter. [ABSTRACT FROM AUTHOR]

Based on the time evolution similarity principle of the topological neighbor phase points in the Phase Space Reconstruction (PSR), a new modeling approach of Short-Term Load Forecasting (STLF) with integrated dynamics mechanism and based on the fusion of the dynamic optimal neighbor phase points (DONP) and Improved Chaotic Neural Networks (ICNN) model was presented in this paper. The ICNN model can characterize complicated dynamics behavior. It possesses the sensitivity to the initial load value and to the walking of the whole chaotic track. The input dimension of ICNN is decided using PSRT, and the training samples are formed by means of the stepping dynamic space track on the basis of the DONP. So it can improve associative memory and generalization ability of ICNN model. The testing results show that proposed model and algorithm can enhance effectively the precision of STLF and its stability. [ABSTRACT FROM AUTHOR]

This paper analyzes various formulations for the recursive training of neural networks that can be used for identifying and optimizing nonlinear processes on line. The study considers feedforward type networks (FFNN) adapted by three different methods: inverse Hessian matrix approximation, calculation of the inverse Hessian matrix using a Gauss-Newton recursive sequential algorithm, and calculation of the inverse Hessian matrix in a recursive type Gauss-Newton algorithm. The study is completed using two network structures that are linear in the parameters: a radial basis network and a principal components network, both trained using a recursive least squares algorithm. The corresponding algorithms and a comparative test consisting of the on-line estimation of a reaction rate are detailed. The results indicate that all the structures were capable of converging satisfactorily in a few iteration cycles, FFNN type networks showing better prediction capacity, but the computational effort of the recursive algorithms is greater. [ABSTRACT FROM AUTHOR]

This paper proposes a dynamic recurrent fuzzy wavelet network (RCFWN) for identified nonlinear dynamic systems. Temporary relations are embedded in the network by adding feedback connections in the second layer of the fuzzy wavelet network. In addition, the study algorithm of the RCFWN is introduced and its stability analysis is studied. Finally, the RCFWN is applied in several simulations. The results verify the effectiveness of the RCFWN. [ABSTRACT FROM AUTHOR]

In this paper a new structure of a recurrent neurofuzzy network is proposed. The network considers two cascade-interconnected Fuzzy Inference Systems (FISs), one recurrent and one static, that model the behaviour of a unknown dynamic system from input-output data. Each FIS's rule involves a linear system in a controllable canonical form. The training for the recurrent FIS is made by a gradient-based Real-Time Recurrent Learning Algorithm (RTRLA), while the training for the static FIS is based on a simple gradient method. The initial parameter conditions previous to training are obtained by extracting information from a static FISs trained with delayed input-output signals. To demonstrate its effectiveness, the identification of two non-linear dynamic systems is included. [ABSTRACT FROM AUTHOR]

A new reproducing kernel in reproducing kernel Hilbert space (RKHS), namely the multi-resolution reproducing kernel, is presented in this paper. The multi-resolution reproducing kernel is generated by scaling basis function at some scale and wavelet basis function with different resolution. Based on multi-resolution reproducing kernel and ν- support vector regression (ν-SVR) method, a new regression model is proposed. The regression model used to nonlinear system identification, incorporate the advantage of the support vector machines and the multi-resolution property of wavelet. Simulation examples are given to illustrate the feasibility and effectiveness of the method. [ABSTRACT FROM AUTHOR]

A single neural network model developed from a limited amount of sample data usually lacks robustness and generalization. Neural network model robustness and prediction accuracy can be improved by combining multiple neural networks. In this paper a new method of the multiple neural networks for nonlinear modeling is proposed. A whole training sample data set is partitioned into several subsets with different centers using fuzzy c-means clustering algorithm (FCM), and the individual neural network is trained by each subset to construct the subnet respectively. The degrees of memberships are used for combining the outputs of subnets to obtain the final result, which are gained from the relationship between a new input sample data and each cluster center. This model has been evaluated and applied to estimate the status-of-loose of jig washer bed. Simulation results and actual application demonstrate that this model has better generalization, better prediction accuracy and wider potential application online. [ABSTRACT FROM AUTHOR]

The least squares support vector machines (LS-SVMs) is a kernel method. The training problem of LS-SVMs is solved by finding a solution to a set of linear equations. This makes online adaptive implementation of the algorithm feasible. An improved adaptive algorithm is proposed for training the LS-SVMs in this paper. This algorithm is especially useful on online nonlinear system modeling. The experiments with benchmark problem have shown the validity of the proposed method even in the case of additive noise to the system. [ABSTRACT FROM AUTHOR]

In this paper, based on the Volterra expansion of nonlinear dynamical system functions and the deterministic and nonlinear characterization of chaotic time series, the discrete Volterra neural networks are proposed to make prediction of chaotic time series. The predictive model of chaotic time series is established with the discrete Volterra neural networks and the steps of the learning algorithm with discrete Volterra neural networks are expressed. The predictive model and the learning algorithm are more effective and reliable than the adaptive higher-order nonlinear FIR filter. The Experimental and simulating results show the discrete Volterra neural networks can be successfully used to predict chaotic time series. Keywords: Chaotic time series; discrete Volterra neural networks; prediction. [ABSTRACT FROM AUTHOR]

Time series analysis is an important and complex problem in machine learning. In this paper, least squares support vector machine (LS-SVM) combined with particle swarm optimization (PSO) is used to time series prediction. The LS-SVM can overcome some shortcoming in the multilayer perceptron (MLP) and the PSO is used to tune the LS-SVM parameters automatically. A benchmark problem, Hénon map time series, has been used as an example for demonstration. It is showed this approach can escape from the blindness of man-made choice of the LS-SVM parameters. It enhances the efficiency and the capability of prediction. [ABSTRACT FROM AUTHOR]

In this paper, a new predictive algorithm for multivariate chaotic time series is proposed. Considering the correlations among time series, multivariate time series instead of univariate ones are taken as the inputs of predictive model. The model is implemented by a radial basis function neural network. To determine the number of model inputs, C-C method is applied to construct the embedding of the chaotic time series by choosing delay time window. The annual river runoff and annual sunspots are used in the simulation, and the proposed method is proven effective and valid. [ABSTRACT FROM AUTHOR]

In this paper, independent component analysis (ICA) is applied for feature extraction of non-Gaussian acoustic signals. The generalized Gaussian model (GGM) is introduced as the p.d.f. estimator in ICA because it can provide a general method for modeling non-Gaussian statistical structure of univariate distributions. It is demonstrated that the proposed method can efficiently extract ICA features for not only sup-Gaussian but also sub-Gaussian signals. The basis vectors are localized in both time and frequency domain and the resulting coefficients are statistically independent and sparse. The experiments of Chinese speech and the underwater signals show that the proposed method is more efficient than conventional methods. [ABSTRACT FROM AUTHOR]

This paper presents a novel texture segmentation method using neural networks and a Markov random field (MRF) model. Multi-scale wavelet coefficients are used as input for the neural networks. The output of the neural network is modeled as a posterior probability. Initially, the multi-scale texture segmentation is performed by the posterior probabilities from the neural networks and MAP (maximum a posterior) classification. Then the MAP segmentation maps are produced at all scales. In order to obtain the more improved segmentation result at the finest scale, our proposed method fuses the multi-scale MAP segmentations sequentially from coarse to fine scales. This is done by computing the MAP segmentation given the segmentation map at one scale and a priori knowledge regarding contextual information which is extracted from the adjacent coarser scale segmentation. In this fusion process, the MRF prior distribution and Gibbs sampler are used, where the MRF model serves as the smoothness constraint and the Gibbs sampler acts as the MAP classifier. [ABSTRACT FROM AUTHOR]

Eye movements, cardiac signals, muscle noise and line noise, etc. present serious problems for the accuracy of Electroencephalographic (EEG) analysis. Some research results have shown that independent component analysis (ICA) can separate artifacts from multichannel EEG data. Further, considering the nonlinear dynamic properties of EEG signals, exponential analysis can be used to identify various artifacts and basic rhythms, such as α rhythm, etc., from each independent component (IC). In this paper, we propose an automatic artifacts removal scheme for EEG data by combining ICA and exponential analysis. In addition, the proposed scheme can also be used to detect basic rhythms from EEG data. The experimental results on both the simulated data and the real EEG data demonstrate that the proposed scheme for artifacts removal has excellent performance. [ABSTRACT FROM AUTHOR]

Recently semi-supervised learning has attracted a lot of attention. Different from traditional supervised learning, semi-supervised learning makes use of both labeled and unlabeled data. In face recognition, collecting labeled examples costs human effort, while vast amounts of unlabeled data are often readily available and offer some additional information. In this paper, based on Support Vector Machine (SVM), we introduce a novel semi-supervised learning method for face recognition. The basic idea of the method is that, if two data points are close to each other, they tend to share the same label. Therefore, it is reasonable to search a projection with maximal margin and locality preserving property. We compare our method to standard SVM and transductive SVM. Experimental results show efficiency and effectiveness of our method. [ABSTRACT FROM AUTHOR]

In this paper, we propose a flexible two-stage algorithm for extracting desired periodic signals. In the first stage, if the period and phase information of the desired signal is available (or can be estimated), a minimum mean square error approach is used to coarsely recover the desired source signal. If only the period information is available (or can be estimated), a robust correlation based method is proposed to achieve the same goal. The second stage uses a higher-order statistics based Newton-like algorithm, derived from a constrained maximum likelihood criteria, to process the extracted noisy signal as cleanly as possible. A parameterized nonlinearity is adopted in this stage, adapted according to the estimated statistics of the desired signal. Compared with many existing extraction algorithms, the proposed algorithm has better performance, which is confirmed by simulations. [ABSTRACT FROM AUTHOR]

Although the baseline fractal image encoding algorithm could obtain very high compression ratio in contrast with other compression methods, it needs a great deal of encoding time, which limits it to widely practical applications. In recent years, an accelerating algorithm based on variance is addressed and has shortened the encoding time greatly; however, in the meantime, the image fidelity is obviously diminished. In this paper, a neural network is utilized to modify the variance-based encoding algorithm, which makes the quality of reconstructed images improved remarkably as the encoding time is significantly reduced. Experimental results show that the reconstructed images quality measured by peak-signal-to-noise-ratio is better than conventional variance-based algorithm, while the time consumption for encoding and the compression ratio are almost the same as the conventional variance-based algorithm. [ABSTRACT FROM AUTHOR]

A new voice activity detection (VAD) method for microphone array signals is developed in this paper. A relatively pure speech signal can be obtained by applying noise canceling algorithms on some signals from microphone array. For suppressing correlated and uncorrelated noises, the proposed method doesn't perform the same processing, but analyze the natures of the background noises by calculating the correlation between the noisy signals during silence intervals firstly. If the additive noises are correlated, relatively pure speech component is separated by blind source separation (BSS) method. Otherwise, this speech component is estimated by beamforming and maximum a posterior (MAP) algorithm. Then, a voice activity detection method based on entropy is employed to determine whether this relatively pure speech signal is active or not. Finally, this VAD result is used as reference to produce those of all array signals. Simulation results illustrate the validity of the proposed method. [ABSTRACT FROM AUTHOR]

Detecting the presence of speech in a noisy signal is an unsolved problem affecting numerous speech processing applications. This paper shows an effective method employing support vector machines (SVM) for voice activity detection (VAD) in noisy environments. The use of kernels in SVM enables to map the data into some other dot product space (called feature space) via a nonlinear transformation. The feature vector includes the subband signal-to-noise ratios of the input speech and a radial basis function (RBF) kernel is used as SVM model. It is shown the ability of the proposed method to learn how the signal is masked by the acoustic noise and to define an effective non-linear decision rule. The proposed approach shows clear improvements over standardized VADs for discontinuous speech transmission and distributed speech recognition, and other recently reported VADs. [ABSTRACT FROM AUTHOR]

In this paper, we proposed the image processing techniques for extracting the cracks in a concrete surface crack image and the ART1-based RBF network for recognizing the directions of the extracted cracks. The image processing techniques used are the closing operation of morphological techniques, the Sobel masking used to extract edges of the cracks, and the iterated binarization for acquiring the binarized image from the crack image. The cracks are extracted from the concrete surface image after applying two times of noise reduction to the binarized image. We proposed the method for automatically recognizing the directions (horizontal, vertical, -45 degree, 45 direction degree) of the cracks with the ART1-based network. The proposed ART1-based RBF network applied ART1 to the learning between the input layer and the middle layer and the Delta learning method to the learning between the middle layer and the output layer. The experiments using real concrete crack images showed that the cracks in the concrete crack images were effectively extracted and the proposed ART1-based RBF network was effective in the recognition of the direction of extracted cracks. [ABSTRACT FROM AUTHOR]

This paper presents a likelihood estimation method from SOM (self organizing feature map), and texture segmentation is performed by using Bayesian estimation and SOM. Multi-scale wavelet coefficients are used as input for SOM, and likelihood probabilities for observations are obtained from trained SOMs. Texture segmentation is performed by the likelihood probability from trained SOMs and ML (maximum likelihood) classification. The result of texture segmentation is improved using contextual information. The proposed segmentation method performed better than segmentation method using HMT (hidden Markov trees) model. In addition, texture segmentation results by SOM and multi-scale Bayesian image segmentation technique called HMTseg also performed better than those by HMT and HMTseg. [ABSTRACT FROM AUTHOR]

This paper provides the construction of statistical shape model based on elliptic Fourier transformation and minimum description length (MDL). The method does not require manual identification of landmarks on training shapes. Each training shapes can be decomposed into a set of ellipse by elliptic Fourier transformation at a different frequency level. The MDL objective function is based on elliptic Fourier descriptors and principal component analysis (EF-PCA). Experiments show that our method can get better models. [ABSTRACT FROM AUTHOR]

A simple and efficient inpaiting algorithm is proposed based on radial basis function network in this paper. Using the user defined areas, a neighborhood narrow band of the needing fixed pixels are computed by an erosion operator of mathematical morphology technique. Then the weights of RBF network are estimated and a continuous function is constructed, from which the needy inpainted pixels can be filled in. [ABSTRACT FROM AUTHOR]

No-reference perceptual quality assessment for JPEG images in real time is a critical requirement for some applications, such as in-service visual quality monitoring, where original information can not be available. This paper proposes a no-reference perceptual quality-assessment method based on a general regression neural network (GRNN). The three visual features of artifacts introduced in JPEG images are formulated block by block individually so that our method is computation-efficient and memory-efficient. The GRNN is used to realize the mapping of these visual features into a quality score in real time because of its excellent approximation and very short training time (one-pass learning). Experimental results on an on-line database show that our estimated scores have an excellent correlation with subjective MOS scores. [ABSTRACT FROM AUTHOR]

In recent years digital watermarking was developed significantly and applied broadly for copyright protection and authentication. In this paper, a digital image watermarking scheme is developed using neural network to embedded watermark into DCT domain of each subimage blocks obtained by subsampling, which achieves adaptively watermark embedding and stronger robustness. Furthermore, in order to improve the security of the proposed watermarking, a random permutation process is used in watermarking process. Experiments show that the proposed watermarking scheme is effect and encouraging. [ABSTRACT FROM AUTHOR]

By right of the great convenience of computer graphics and digital imaging, it is much easier to alter the content of an image than before without any visually traces. Human has not believed what they see. Many digital images can not be judged whether they are real or feigned visually, i.e., many fake images are produced whose content is feigned. In this paper, firstly, image fakery is introduced, including how to produce fake images and its characters. Then, a fake image detection scheme is proposed, which uses radial basis function (RBF) neural network as a detector to make a binary decision on whether an image is fake or real. The experimental results also demonstrated the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

It has been shown that support vector machines (SVM) can be used in content-based image retrieval. Existing SVM based methods only extract low-level global or region-based features to form feature vectors and use traditional non-structured kernel function. However, these methods rarely consider the image structure or some new structured kernel types. In order to bridge the semantic gap between low-level features and high-level concepts, in this paper, a novel graph kernel based SVM method is proposed, which takes into account both low-level features and structural information of the image. Firstly, according to human selective visual attention model, for a given image, salient regions are extracted and the concept of Salient Region Adjacency Graph (SRAG) is proposed to represent the image semantics. Secondly, based on the SRAG, a novel graph kernel based SVM is constructed for image semantic retrieval. Experiments show that the proposed method shows better performance in image semantic retrieval than traditional method. [ABSTRACT FROM AUTHOR]

This paper designs an edge preserving regularization model for image restoration. First, we propose a generalized form of Digitized Total Variation (DTV), and then introduce it into restoration model as the regularization term. To minimize the proposed model, we map digital image onto network, and then develop energy descending schemes based on Hopfield neural network. Experiments show that our model can significantly better preserve the edges of image compared with the commonly used Laplacian regularization (with constant and adaptive coefficient). We also study the effects of neighborhood and gaussian parameter on the proposed model through experiments. [ABSTRACT FROM AUTHOR]

In this paper, RBF network (RBFN) is used to provide effective methodologies for solving difficult computational problems in camera calibration and 3D reconstruction process. RBFN works in three aspects: Firstly, a RBFN is adopted to learn and memorize the nonlinear relationship in stereovision system. Secondly, another RBFN is trained to search the correspondent lines in two images such that stereo matching is performed in one dimension. Finally, the trained network in the first stage is used to reconstruct the object's 3D figuration and surface. The technique avoids the complicated and large calculation in conventional methods. Experiments have been performed on common stereo pairs and the results are accurate and convincing. [ABSTRACT FROM AUTHOR]

Using the Group Method of Data Handling (GMDH) a polynomial network is designed in this paper for learning the nonlinear image distortion of a camera. The GMDH network designed can effectively learn image distortion in various camera systems of different optical features unlike most existing techniques that assume a physical model explicitly. Compared to multilayer perceptrons (MLPs), which are popularly used to learn a nonlinear relation without modeling, a GMDH network is self-organizing and its learning is faster. We prove the advantages of the proposed technique with various simulated data sets and in a real experiment. [ABSTRACT FROM AUTHOR]

In order to enhance the contrast of an image, histogram equalization is wildly used. With global histogram equalization (GHE), the image is enhanced as a whole, and this may induce some areas to be overenhanced or blurred. Although local histogram equalization (LHE) acts adaptively to overcome this problem, it brings noise and artifacts to image. In this paper, a region-based enhancement algorithm is proposed, in which Grossberg network is employed to generate histogram and extract regions. Simulation results show that the image is obviously improved with the advantage of both GHE and LHE. [ABSTRACT FROM AUTHOR]

The resource of image noise is analysized in this paper. Considering the image fuzzy generated in the process of image denoising in spatial field, the image denoising method based on wavelet neural network with orthonormal bases is elaborated. The denoising principle and construction method of orthonormal wavelet network is described. In the simulation experiment, median filtering, adaptive median filtering and sym wavelet neural network with orthonormal bases were used separately in the denoising for contaminated images. The experiment shows that, compared with traditional denoising method, image denoising method based on orthonormal wavelet neural network improves greatly the image quality and decreases the image ambiguity. [ABSTRACT FROM AUTHOR]

The self-localization problem is very important when the mobile robot has to move in autonomous way. Among techniques for self-localization, landmark-based approach is preferred for its simplicity and much less memory demanding for descriptions of robot surroundings. Door-plates are selected as visual landmarks. In this paper, we present an adaptive segmentation approach based on Principal Component Analysis (PCA) and scale-space filtering. To speed up the entire color segmentation and use the color information as a whole, PCA is implemented to project tristimulus R, G and B color space to the first principal component (1st PC) axis direction and scale-space filtering is used to get the centers of color classes. This method has been tested in the color segmentation of door-plate images captured by mobile robot CASIA-1. Experimental results are provided to demonstrate the effectiveness of this proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a support vector machine (SVM) approach for automatic impulsive noise detection in corrupted image is proposed. Once the noises are detected, a filtering action based on regularization can be taken to restore the image. Experimental results show that the proposed SVM-based approach provides excellent performance with respect to various percentages of impulse noise. [ABSTRACT FROM AUTHOR]

Automated segmentation of MR images is a difficult problem due to the complexity of the images. Up to now, several approaches have been proposed based on spectral characteristics of MR images, but they are sensitive to the noise contained in the MR images. In this paper, we propose a robust method for noisy MR image segmentation. We use region-based features for a robust segmentation and use principle component analysis (PCA) to reduce the large dimensionality of feature space. Experimental results show that the proposed method is very tolerant to the noise and the segmentation performance is significantly improved. [ABSTRACT FROM AUTHOR]

In this paper, we present a new scheme to segment a given image. This scheme utilizes neuro-fuzzy system to derive a proper set of contour pixels based on multi-scale images. We use these fuzzy derivatives to develop a new curve evolution model. The model automatically detect smooth boundaries, scaling the energy term, and change of topology according to the extracted contour pixels set. We present the numerical implementation and the experimental results based on the semi-implicit method. Experimental results show that one can obtains a high quality edge contour. [ABSTRACT FROM AUTHOR]

In this paper, we present an adaptive spatially-constrained deterministic annealing (ASDA) algorithm, which takes into account the spatial continuity constraints by using a dissimilarity index that allows spatial interactions between image pixels, for image segmentation. The local spatial continuity constraint reduces the noise effect and the classification ambiguity. More importantly, the strength of spatial constraint for each given image pixel is auto-selected by the scaled variance of its neighbor pixels, which results in the adaptiveness of the presented algorithm. The effectiveness and efficiency of the presented method for image segmentation are supported by experimental results on synthetic and MR images. [ABSTRACT FROM AUTHOR]

As for unsupervised image segmentation, one important application is content based image retrieval. In this context, the key problem is to automatically determine the number of regions(i.e., clusters) for each image so that we can then perform a query on the region of interest. This paper presents an iterative entropy regularized likelihood (ERL) learning algorithm for cluster analysis based on a mixture model to solve this problem. Several experiments have demonstrated that the iterative ERL learning algorithm can automatically detect the number of regions in a image and outperforms the generalized competitive clustering. [ABSTRACT FROM AUTHOR]