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Image moments such as zernike, tchebichef and geometric moments have been widely used in image processing applications. They have useful properties to detect edges. In this paper, we present how one of the moment families, in particular geometric moments (GM) can be utilized in estimating the sigma and size of the Gaussian point spread function (PSF) that degrades the images. With the knowledge of how edges vary in the presence of Gaussian blur, a method that uses low order geometric moments is proposed to estimate the PSF parameter. This is achieved by using the difference of the GMs of the original and the reblurred images as feature vectors to train extreme learning machine (ELM) to estimate the PSF parameters respectively. Further, a novel method that uses a cascaded digital filters operating as subtractors is proposed to transform the image from geometric moment domain to spatial domain. The effectiveness of the proposed method of estimating the PSF parameters is examined using cross database validation. The results show that the proposed method in most of the cases performs better than the three existing methods when examined in terms of the visual quality evaluated using structural similarity (SSIM) index.

In this work, an image encryption method is proposed based on fuzzy cellular neural network (FCNN). First, the shortcomings of FCNN in encrypting image are identified, and the FCNN model is then modified to address these shortcomings. Specifically, a theoretical framework is developed to identify the values of the parameters of FCNN to generate chaotic signals, which are in turn utilized to encrypt the image. The encryption method is designed where an encrypted pixel is generated based on the corresponding plaintext pixel together with the neighbouring encrypted pixels. The proposed method has a key sensitivity in the order of 10−10 to achieve adequate security robustness. Further evaluations on standard test images verified and confirmed that the proposed encryption method is robust against plaintext-only (i.e., brutal force) and chosen-plaintext attacks.

This paper proposes a new fuzzy approach for the segmentation of images. L-interval-valued intuitionistic fuzzy sets (IVIFSs) are constructed from two L-fuzzy sets that corresponds to the foreground (object) and the background of an image. Here, L denotes the number of gray levels in the image. The length of the membership interval of IVIFS quantifies the influence of the ignorance in the construction of the membership function. Threshold for an image is chosen by finding an IVIFS with least entropy. Contributions also include a comparative study with ten other image segmentation techniques. The results obtained by each method have been systematically evaluated using well-known measures for judging the segmentation quality. The proposed method has globally shown better results in all these segmentation quality measures. Experiments also show that the results acquired from the proposed method are highly correlated to the ground truth images.

Text detection and recognition in poor quality video is a challenging problem due to unpredictable blur and distortion effects caused by camera and text movements. This affects the overall performance of the text detection and recognition methods. This paper presents a combined quality metric for estimating the degree of blur in the video/image. Then the proposed method introduces a blind deconvolution model that enhances the edge intensity by suppressing blurred pixels. The proposed deblurring model is compared with other state-of-the-art models to demonstrate its superiority. In addition, to validate the usefulness and the effectiveness of the proposed model, we conducted text detection and recognition experiments on blurred images classified by the proposed model from standard video databases, namely, ICDAR 2013, ICDAR 2015, YVT and then standard natural scene image databases, namely, ICDAR 2013, SVT, MSER. Text detection and recognition results on both blurred and deblurred video/images illustrate that the proposed model improves the performance significantly. We explore quality metrics for blur text image/video classification.Proposed a deblur model explores Gaussian weighted-L1 in different ways.Kernel based energy minimization enhances the edge strengths.Experiments to evaluate and validate the proposed deblur model are presented.Experimental result shows that the proposed method is useful for text detection.

The mu rhythm is an electroencephalogram (EEG) signal located at the central region of the brain that is frequently used for studies concerning motor activity. Quite often, the EEG data are contaminated with artifacts and the application of blind source separation (BSS) alone is insufficient to extract the mu rhythm component. We present a new two-stage approach to extract the mu rhythm component. The first stage uses second-order blind identification (SOBI) with stationary wavelet transform (SWT) to automatically remove the artifacts. In the second stage, SOBI is applied again to find the mu rhythm component. Our method is first compared with independent component analysis with discrete wavelet transform (ICA-DWT) as well as SOBI-DWT, ICA-SWT, and regression method for artifact removal using simulated EEG data. The results showed that the regression method is more effective in removing electrooculogram (EOG) artifacts, while SOBI-SWT is more effective in removing electromyogram (EMG) artifacts as compared to the other artifact removal methods. Then, all the methods are compared with the direct application of SOBI in extracting mu rhythm components on simulated and actual EEG data from ten subjects. The results showed that the proposed method of SOBI-SWT artifact removal enhances the extraction of the mu rhythm component.

Character segmentation from License plate is challenging as it suffers from non-uniform illumination, blur and touching adjacent character due to head light of vehicles effect etc. This paper presents a new approach based on sharpness of character and the space regions for segmentation. We explore Gradient Vector Flow Opposite Direction Pair of pixels for seed point's selection. From the seed points, the proposed approach detects probable cuts between character components based on minimum cost path estimation. Then we propose a new sharpness for detecting candidate cuts from the probable cuts based on Laplacian zero crossing points and Sobel-Gradient. The average width of candidate cuts is considered as reference cut to identify the correct cuts between character components. Experimental results on real industry dataset of license plate shows that the proposed approach is robust to touching, blur, poor quality images. Further, comparative study with an existing approach shows that the proposed approach outperforms the existing approach.

This paper shows how dynamic heart rate measurements that are typically obtained from sensors mounted near to the heart can also be obtained from video sequences. In this study, two experiments are carried out where a video camera captures the facial images of the seven subjects. The first experiment involves the measurement of subjects’ increasing heart rates (79 to 150 beats per minute (BPM)) while cycling whereas the second involves falling heart beats (153 to 88 BPM). In this study, independent component analysis (ICA) is combined with mutual information to ensure accuracy is not compromised in the use of short video duration. While both experiments are going on measures of heartbeat using the Polar heart rate monitor is also taken to compare with the findings of the proposed method. Overall experimental results show the proposed method can be used to measure dynamic heart rates where the root mean square error (RMSE) and the correlation coefficient are 1.88 BPM and 0.99 respectively.

Pseudo-Zernike moments have better feature representation capability, and are more robust to image noise than those of the conventional Zernike moments. However, due to the computation complexity of pseudo-Zernike polynomials, pseudo-Zernike moments are yet to be extensively used as feature descriptors as compared to Zernike moments. In this paper, we propose two new algorithms, namely coefficient method and p-recursive method, to accelerate the computation of pseudo-Zernike moments. Coefficient method calculates polynomial coefficients recursively. It eliminates the need of using factorial functions. Individual order or index of pseudo-Zernike moments can be derived independently, which is useful if selected orders or indices of moments are needed as pattern features. p-recursive method uses a combination of lower order polynomials to derive higher order polynomials with the same index q. Fast computation is achieved because it eliminates the requirements of calculating polynomial coefficients, Bpqk, and power of radius, rk, in each polynomial. The performance of the proposed algorithms on moment computation and image reconstruction, as compared to those of the present methods, are experimentally verified using a set of binary and grayscale images.

This paper introduces a new method to estimate the instantaneous heart rates from facial images obtained from video camera. In this study, facial images of a subject whose heart rate varying from 150 beats per minute (BPM) to 110 BPM in 100 seconds are captured through video camera and analyzed using filter bank. The filter bank is utilized to provide the localized temporal information. We test the proposed method on videos with duration of 20s. The proposed method is compared to short-time Fourier Transform, which can also give temporal information. Experimental results show the proposed method can provide an acceptable result. The root mean square error is about 2.31 beats per minute (BPM). The average percentage error is 1.42% while the correlation coefficient 0.96.

This paper presents an image block classification method using Tchebichef moments (TMs) and support vector machine (SVM). The test images are divided into non-overlapping 16 × 16 blocks and transformed into moment domain using Discrete Tchebichef Transform. These moment features are then used in the image content (block) classification. SVM is used for learning and classifying the blocks into three types: “plain”, “edge” and “texture”, based on their moment energy level. Experimental results show that the proposed method works well and the classification accuracy is 98.7%.

Non-invasive heart rate measurement is essential in medical and sport sciences. Typically, the first stage of heart rate measurements is obtained from non-invasive sensors. In this paper, the data from video sequences of a subject cycling are processed using short-time Fourier transform (STFT) to indicate the heart rate of the subject. The choice of using STFT is its ability to provide more accurately localized temporal and frequency information, especially for the rapidly changing heart rate pattern during the exercise routine. Experimental results show the proposed method can provide an acceptable result and the root mean square error is less than 2.5 beats per minute (BPM) for heart rate variations between 80 BPM and 130 BPM.

In this paper, a face recognition system based on Discrete Tchebichef-Krawtchouk Transform DTKT and Support Vector Machines SVMs is proposed. The objective of this paper is to present the following: (1) the mathematical and theoretical frameworks for the definition of the DTKT including transform equations that need to be addressed. (2) the DTKT features used in the classification of faces. (3) results of empirical tests that compare the representational capabilities of this transform with other types of discrete transforms such as Discrete Tchebichef transform DTT, discrete Krawtchouk Transform DKT, and Discrete Cosine transform DCT. The system is tested on a large number of faces collected from ORL and Yale face databases. Empirical results show that the proposed transform gives very good overall accuracy under clean and noisy conditions.

This paper presents a technique of selecting an optimal number of features from the original set of features. Due to the large number of features considered, it is computationally more efficient to select a subset of features that can discriminate as well as the original set. The subset of features is determined using stepwise discriminant analysis. The results of using such a scheme to classify scaled, rotated, and translated binary images and also images that have been perturbed with random noise are reported. The features used in this study are Zernike moments, which are the mapping of the image onto a set of complex orthogonal polynomials. The performance of using a subset is examined through its comparison to the original set. The classifiers used in this study are neural network and a statistical nearest neighbor classifier. The back-propagation learning algorithm is used in training the neural network. The classifers are trained with some noiseless images and are tested with the remaining data set. When an optimal subset of features is used, the classifers performed almost as well as when trained with the original set of features.

This paper provides a preliminary study of compression efficiency and noise robustness of two orthogonal moments (Legendre and Tchebichef) on medical images. Similar to a typical JPEG compression, the medical images are first subdivided into 8x8 blocks and transformed into moment domain. Moments up to order of 2 to 8 are used to reconstruct the image. The mean square error (MSE) between the reconstructed and the original images are computed as the measure of compression efficiency. For the noise robustness test, Gaussian white noise is applied to the original image before the same processing steps mentioned above are done. Discrete Cosine Transform (DCT), a standard JPEG compression algorithm, is used as the benchmark for this experimental study. The result shows that the compression ability of Tchebichef moments is comparable to DCT while Legendre moment has the highest noise robustness among them.

On behalf of the technical program committee, we are pleased and honored to invite and welcome you to Malaysia and to the 2010 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS 2010). In APCCAS 2010, the technical program shows the novel and advanced development in the field of circuits and systems. We also hope all attendees have fun in enjoying the history and cultures of Malaysia.

Image quality assessment is one of the challenging field of digital image processing system. It can be done subjectively or objectively. PSNR is the most popular and widely used objective image quality metric but it is not correlate well with the subjective assessment. Thus, there are a lot of objective image quality metrics (IQM) developed in the past few decades to replace PSNR. This paper provides a literature review of the current subjective and objective image quality measures. The purpose of this paper is to collect reported quality metrics and group them according to their strategies and techniques.

A common approach in mapping a signal to discrete events is to define a set of symbols that correspond to useful acoustic features of the signal over a short constant time interval. This paper proposes a Hidden Markov Models (HMM) based speech recognition by using cepstrum feature of the signal over adaptive time interval. First pitch period is detected by dyadic wavelet transform and divides the voiced speech signal according to the detected period. Then, system performs HMM-based speech recognition using cepstrum feature to classify the speech signals. Two speech recognition systems have been developed, one is based on constant time framing and the other is adaptive framing. The results are compared and found that adaptive framing method shows better result in both data distribution and recognition rate.

This paper presents the face recognition using discrete orthogonal moment that is Krawtchouk moments (KMs) on combined features of global and local face images. KMs are considered due to their ability to localize face image according to Region of Interest (ROI) unlike other moments which generally capture the global features. To obtain the global face image, both parameters of KMs are set equal at 0.5 while the parameters for local face image are set unequal. The selection of orders and parameters of KMs determines the ROI. The experiments carried out utilized the database face images from Olivetti research laboratory (ORL) consisting of 40 subjects of 10 non-similar images each [1]. Each face image differs in terms of position, rotation, scale and expression, with and without glasses. Euclidean square distance or Nearest Neighbour (NN) is used as the classifier in the recognition stage. From the experiments, the combined features of global and local faces images using KMs showed improved classification accuracy.

Changes in illumination condition, pose, facial expression and others is not an easy task in face recognition. Solving these problems requires not only a feature extraction method that can generate distinct features for each class of image but requires other additional technique that able to improve the overall classification accuracy. This paper presents the face recognition using combined features of original and pre-processed face images. This technique is experimented using orthogonal moments namely Zernike moments (ZMs) and Krawtchouk moments (KMs). The classification technique used in the recognition stage is Euclidean square distance or Nearest Neighbour (NN) classifier. Database face images from Olivetti research laboratory (ORL) consisting of 40 subjects of 10 images each where none of them are identical, is used in the experiments. The face images vary in position, rotation, scale and expression, with and without spectacles. From the experiments, the new technique is able to improve the classification accuracy significantly.

We used neural network for blood glucose level determination in this study. The data set used in this study was collected using a non-invasive blood glucose monitoring system with six laser diodes, each laser diode operating at distinct near infrared wavelength between 1500nm and 1800nm. The neural network is specifically used to determine blood glucose level of one individual who participated in an oral glucose tolerance test (OGTT) session. Partial least squares regression is also used for blood glucose level determination for the purpose of comparison with the neural network model. The neural network model performs better in the prediction of blood glucose level as compared with the partial least squares model.

In this letter, neural networks (NNs) classify alcoholics and nonalcoholics using features extracted from visual evoked potential (VEP). A genetic algorithm (GA) is used to select the minimum number of channels that maximize classification performance. GA population fitness is evaluated using fuzzy ARTMAP (FA) NN, instead of the widely used multilayer perceptron (MLP). MLP, despite its effective classification, requires long training time (on the order of 10/sup 3/ times compared to FA). This causes it to be unsuitable to be used with GA, especially for on-line training. It is shown empirically that the optimal channel configuration selected by the proposed method is unbiased, i.e., it is optimal not only for FA but also for MLP classification. Therefore, it is proposed that for future experiments, these optimal channels could be considered for applications that involve classification of alcoholics.

H.264/AVC exploits temporal and spatial redundancies to obtain higher coding gain. This comes with a cost of higher complexity encoder and higher computational time, which makes it difficult for real-time applications. In this paper, we present a fast intra-prediction mode selection method for H.264/AVC based on local edge information. Prior to intra prediction process, edge vectors of every pixel in a 4x4 block are totaled up to determine the edge direction. Based on the edge direction information, one directional mode is chosen and compared with the DC mode to choose the best mode with least rate distortion cost. Experimental results show that the proposed method reduces the computation complexity while it gives an almost similar visual quality of the reconstructed frame as compared to the one obtained by using exhaustive prediction method. The proposed method also outperforms Feng Pan's method (2005) in computational time and number of bits generated

This study is to investigate the effects of different montaging methods on the classification rate. The EEG signal is recorded from the motor cortex region when the subjects tap the keyboard using the left or right index finger. In this experiment, One subject’s data is downloaded from the BCI 2003 competition and two other right handed subjects participated in a similar experiment. In this preliminary experimental study, we found that the surface laplacian method outperforms other types of montaging.

This paper describes the comparison between Principal component analysis (PCA) also known as eigenfaces and Zernike moments (ZM) as feature extractors, used in face recognition. These feature extraction methods are still being research until today even though the techniques may either be hybrid or fusion. The study look into the capability of these two feature extraction methods to recognize face due to changes in illumination condition, pose, facial expression and others. The experiment carried out utilizes the earliest eigenfaces technique adopted from Turk and Pentland [1] and ZM polynomials [2]. The classification technique employed in the recognition stage is a simple Euclidean square distance classifier or nearest neighbor (NN). The experiments utilized database face images from Olivetti research laboratory (ORL) consisting of 40 subjects of 10 images each where none of them are identical [3]. They vary in position, rotation, scale and expression, with and without glasses. From the comparative study, the outstanding feature extraction method is considered for face recognition system.

In this paper, facial images from various video sequences are used to obtain a heart rate reading. In this study, a video camera is used to capture the facial images of eight subjects whose heart rates vary dynamically, between 81 and 153 BPM. Principal component analysis (PCA) is used to recover the blood volume pulses (BVP) which can be used for the heart rate estimation. An important consideration for accuracy of the dynamic heart rate estimation is to determine the shortest video duration that realizes it. This video duration is chosen when the six principal components (PC) are least correlated amongst them. When this is achieved, the first PC is used to obtain the heart rate. The results obtained from the proposed method are compared to the readings obtained from the Polar heart rate monitor. Experimental results show the proposed method is able to estimate the dynamic heart rate readings using less computational requirements when compared to the existing method. The mean absolute error and the standard deviation of the absolute errors between experimental readings and actual readings are 2.18 BPM and 1.71 BPM respectively.

Recognizing face images due to changes in illumination condition, pose, facial expression and others are challenging task. Solving these problems requires a feature extraction method that can generate distinct features for each class of image. Hence, this paper describes the comparative analysis of feature extraction methods namely geometric moments, Zernike moments, Krawtchouk moments and principle component analysis (PCA) in terms of their capability to recognize face images. The classification technique employed in the recognition stage is back propagation neural network (BPNN). The experiments utilized database face images from Olivetti research laboratory (ORL) consisting of 40 subjects of 10 samples each where none of them are identical. They vary in position, rotation, scale and expression. From the comparative study, the most suitable feature extraction method is considered for face recognition system.

This paper presents an algorithm in which the moment invariant features of a numerical character are presented as inputs to a neural network. The image is digitised, by a frame grabber card plugged into the extension slot of an IBM AT computer. Three moment invariant features derived from the geometrical moments were extracted and fed to the neural network. The neural network, used back propagation learning in its training, classifies these features and attempts to produce the desired output. Noisefree and noisy images of various variances of the ten numerals were experimented and the results are reported.

Human crowds organize themselves spontaneously in a confined environment when crowd density exceeds certain critical value. This self-organization behaviour of crowd motion can be observed from formation of walking lane with similar walking direction. In this work, we proposed a modified social force model, which is originally proposed by Helbing. Individuality, irregularity and unpredictability are characteristics of crowd motion. These properties are chaotic properties of nonlinear dynamical equation. Using this equation, chaotic perturbation is introduced into social force model to take into account the properties of crowd motion. It is shown that individuality, irregularity and unpredictability of crowd motion can eventually lead the crowd into self-organization.

Proposes a technique that uses genetic algorithm (GA) to select optimal features for classification applications using fuzzy ARTMAP (FA) neural network (NN). The technique is applied to select features for classification of two groups of subjects: alcoholics and controls, using multi-channel single trial electroencephalogram (EEG) signals evoked during visual response. The results show that the proposed technique is successful in selecting the features that contribute towards classification. This serves to reduce the number of required features while improving the classification performance. The results also indicate that the gamma band spectral power could be used to support evidence on the residual effects of long-term use of alcohol on visual response.

Presents a technique to classify signatures produced by the pressure exerted on the pen tip. Before the features are extracted, a low-pass filter is designed to remove frequencies greater that 50 Hz. A segmentation method using moving-average filtering and gradient calculation is used to divide the time series data into segments. The autoregressive (AR) coefficients are derived from each segment. From the coefficients, the power spectral density (PSD) is determined for every segment. A genetic algorithm (GA) is used to select the range of frequencies that contains the most important information. The selected range of frequencies is then fed into a multilayer perceptron (MLP) classifier with one hidden layer for verification. A database of 1,000 signatures is used for training and testing. The system is tested for genuine as well as forged signatures. The result obtained showed an average error rate of 3.09-3.33%.

It is shown that by utilizing the method of Tchebichef (also know as Chebyshev) moments, an image degraded by Gaussian, salt-and-pepper and speckle noise can be restored. The performance of Tchebichef moments is compared with that of Zernike and Legendre moments.

Automatic focusing of a visual inspection system is described. A vidicon camera was mounted on top of a high powered microscope to capture an image, process it and send an appropriate signal to a stepper motor which controls the movements of the microscope. The method used is based on the acutance of the video signal waveform. Acutance is a measure of the steepness of the video signal waveform. It implies that a sharp image will have a sharp gradient of the gray level across the image. This is measured vertically and horizontally and used to control the movements of a stepper motor to achieve an optimal focusing state. On achieving it a signal from the control circuitry of the automatic focusing system will be sent to the control circuitry of the die inspection machine to determine whether the die is good or a reject. >

This paper presents a new technique to derive features for images that are translated, scaled equally/unequally and rotated. The problem is formulated using conventional regular moments. It is shown that the conventional regular moment-invariants remain no longer invariant when the image is scaled unequally in the x- and y-directions. A method is proposed to form moment-invariants that do not change under such unequal scaling. The newly formed moments are also invariant to translation and reflection. However, it is not invariant for images that are rotated. A neural network is trained to estimate the angle of rotation; it is then used to derive the invariant moments for images that are unequally scaled, translated and rotated. Computer simulation results are also included to show the validity of the method proposed.

This paper presents a technique to derive features for images that are shifted and unequally scaled. The problem is formulated using conventional regular moments. It is shown that the conventional regular moment-invariants remain no longer invariant when the image is scaled unequally in the x- and y-directions. A method is proposed to form moment-invariants that do not change under such unequal scaling. Computer simulation results are also included to show the validity of the method proposed.

We describe a method to extract single trial visual evoked potential (VEP) buried in ongoing electroencephalogram (EEG) activity. The common method for separating VEP from EEG is to use signal averaging. But we use digital filters to extract VEP assuming that VEP spectra are in the gamma band. As an application, a fuzzy ARTMAP (FA) neural network classifier with voting strategy is used with this extracted VEP to discriminate alcoholics from normal subjects. The VEP is extracted from subjects while seeing visuals of the Snodgrass and Vanderwart (1980) picture set. The high FA classification of 96.5% shows the validity of the proposed method to successfully remove EEG contamination.

Visual inspection of EEG signals in their unprocessed form is still the predominant way of discriminating EEG patterns in the medical community and requires highly trained medical professionals. To overcome this problem, automatic EEG analysis using Fourier transform methods are popular since most EEG signals consist of the spectral power in the range of /spl delta/, /spl theta/, /spl alpha/ and /spl beta/, i.e. from 0 to 30 Hz. However, this method suffers from high noise sensitivity and is not suitable for short and variable length signal segments. We analyse EEG signals with time series analysis using autoregression techniques. We classify these extracted features for different mental tasks using a fuzzy ARTMAP classifier. We study the effects of different EEG segment or window lengths and different overlapping lengths on the overall performance of the classifier. Our results show that the segment length affects the performance and that overlapping the segments improves the performance greatly.

This paper presents a technique to classify images that have been elongated or contracted. The problem is formulated using conventional regular moments. It is shown that the conventional regular moment-invariants remain no longer invariant when the image is scaled unequally in the x- and y-directions. A method is proposed to form moment-invariants that do not change under such unequal scaling. Results of computer simulations for images are also included verifying the validity of the method proposed. >

A new measure of image focus based on the discrete orthogonal Chebyshev moments is introduced. The low- and high-spatial-frequency components of an image can be represented as the low- and high-order Chebyshev moments, respectively. The focus measure is defined as the ratio of the norm of the high-order moments to that of low-order moments. It is shown that the focus measure is monotonic and unimodal with respect to image blurring. Additionally, it is invariant to contrast changes due to the differences in the intensities of illumination. The focus measure is tested for its discriminating power of images blurred to various degrees. Noise studies show that the focus measure is robust under Gaussian and salt-and-pepper noise. The performance of the proposed focus measure is compared with the existing focus measures.

Generally, visual evoked potential (VEP) estimation requires power spectrum computation. Here VEP is estimated using a recursive digital filter, which is faster and simpler to design. The method assumes VEP can be represented by a 40 Hz spectrum and the filter extracts a 40 Hz signal in the time domain, circumventing the normal procedure of power spectrum computation. Experimental results validate the method for discriminating alcoholics from control subjects.

The paper proposes a neural network technique to classify numerals using Zernike moments that are invariant to rotation only. In order to make them invariant to scale and shift, we introduce modified Zernike moments based on regular moments. Owing to the large number of Zernike moments used, it is computationally more efficient to select a subset of them that can discriminate as well as the original set. The subset is determined using stepwise discriminant analysis. The performance of a subset is examined through its comparison to the original set. The results are shown of using such a scheme to classify scaled, rotated, and shifted binary images and images that have been perturbed with random noise. In addition to the neural network approach, the Fisher's classifier is also used, which is a parametric classifier. A comparative study of their performances shows that the neural network approach produces better classification accuracy than the Fisher's classifier. When a suitable subset of Zernike moments is used, the classifiers perform well, just like the original set. The performance of the classifiers is also examined. The computational time is greatly reduced when a suitable subset of Zernike moments is used.

In this paper, Zernike moment features extracted from rice grains are used in classifying normal and damaged rice. Genetic algorithm (GA) is used to reduce the number of features while maximizing the classification performance. The GA chromosome fitness is evaluated using a multilayer perceptron (MLP) trained by backpropagation learning algorithm.

A new technique of fusing genetic algorithms with Fuzzy ARTMAP is proposed. This method selects the appropriate autoregressive model order for EEG signals and consequently classifies these signals into their respective different mental tasks. The experimental results show that this method outperforms other statistical autoregressive model order selection methods like Akaike Information Criterion, Final Prediction Error and reflection coefficient.

Two common artifacts that corrupt evoked responses are noise and background electroencephalogram (EEG). In this paper, a two-level principal component analysis (PCA) is used to reduce these artifacts from single trial evoked responses. The first level PCA is applied to reduce noise from these VEP signals while the second level PCA reduces EEG. The method is used to analyse the object recognition and decision-making capability during visual responses. The analysis is extended to study the differences in visual response between alcoholics and non-alcoholics using single trial P3 visual evoked potential (VEP) signals. The analysis shows that alcoholics respond slower and weaker to visual stimulus as compared to non-alcoholics.

The advantages of autoregressive (AR) modelling over the classical Fourier transform methods have been centre staged in previous years. But a problem with the AR method lies with the appropriate model order selection. We address this problem by studying the performance of three different types of order selection criteria for AR models to represent electroencephalogram signals. We perform this by extracting the EEG signals for different mental tasks and obtaining the appropriate model order given by the different criteria. From this, we derive the spectral density function. Using the spectral values, we train a neural network and classify the tasks into their respective categories. In this way, we show the difference in the performance level of the different model order selection criteria for EEG signals.