SIGNAL AND DATA PROCESSING
Year:2016 | Volume:13 | Issue:3 (SERIAL 29) |Papers Count:10

In this paper, with due respect to the original data and based on the extraction of new features by smaller dimensions, a new feature reduction technique is proposed for Hyper-Spectral data classification. For each pixel of a Hyper-Spectral image, a specific rational function approximation is developed to fit its own spectral response curve (SRC) and the coefficients of the numerator and denominator polynomials of this function are considered as new extracted features. The method focuses on geometrical nature of SRCs and relies on the fact that the sequence discipline-ordinance of reflectance coefficients in spectral response curve-contains some information which has not been addressed by many other existing methods based on the statistical analysis of data. Maximum likelihood classification results demonstrate that our method provides better classification accuracies in comparison with many competing feature extraction algorithms. In addition, the proposed algorithm has the possibility of being applied to all pixels of image individually and simultaneously as well.

The most important method for behavior recognition of recurrent maps is to plot bifurcation diagram. In conventional method used for plotting bifurcation diagram, a couple of time series for different values of model parameter have been generated and these points have been plotted with due respect to it after transient state. It does not have enough accuracy necessary for period detection and essential for discrimination between long periodic behaviors from chaotic behaviors; on the other hand because of being 2-dimensinal, it will not be possible to investigate the effect if the initial condition is in the basin of attraction. In this research, a new bifurcation diagram is presented which is called: Qualitative Bifurcation Diagram (QBD). QBD provides accurate determination of periodicity. Results of our algorithm implementation on logistic map, represents its ability on determining long periods and period windows. Bifurcation diagram of logistic map does not obey mosaic tiling patterns (patterns that are created by arrangement not interaction) as a disciplinein addition to having the dynamic order. Some benefits of QBD are: long period discrimination, period window detection, computation time reduction, period presentation instead of amplitude show. In the following we have an analytical survey to Lyapunov exponent – as a usual measurement tool for chaotic behavior – and important notes are expressed. Finally, Recurrent Quantification Analysis (RQA) and QBD are compared.

In this paper, a novel shape descriptor for shape-based object retrieval is proposed. A growing process is introduced in which a contour is reconstructed from the bounding circle of the shape. In this growing process, circle points move toward the shape in normal direction until they get to the shape contour. Three different shape descriptors are extracted from this process: the first descriptor is defined as the number of steps that every circle point should pass which is called Growing Steps. The second descriptor is considered as the boundary distance of the circle points at the end of the growing process. The third descriptor is the curvature of the growing lines created by moving points. Invariance to translation is the intrinsic property of these features. By selecting a fixed starting point and tracing the boundary in a fixed direction (clock-wise or counter clock-wise), a set of descriptors could be collected invariant to rotation. Finally, normalizing the descriptors makes them invariant to scale. Support vector machines based on one-shot score are applied in the retrieval stage. Experimental results show that the suggested method has high performance for shape retrieval. It achieves 89. 16% retrieval rate on MPEG-7 CE-Shape-1 dataset.

Although researches in the field of Persian speech recognition claim a thirty-year-old history in Iranwhich bas achieved considerable progresses, due to the lack of well-dermed experimental framework, outcomes from many of these researches are not comparable to each other and their accurate assessmentwon't be possible. The experimental framework includes ASR toolkit and speech database which consists oftraining, development and test datasets. In recent years, as a state-of-the-art open-source ASR toolkit; Kaldi bas been very well-received and welcomed in the community of the world-ranked speech researchersaround the world. considering all aspectsJ Kaldi is the best option among all of the other ASR toolkits to establish a framework to do research in all languages, including Persian. In this paper, we chose Fardat as the speech database which is the counterpart of TIMIT for Persian language because not only it has got a standard form but Ws also accessible for all researchers around the world. Similar to the recipe on TIMIT database, we defined these three sets on the Farsdat: Training, Development and Test sets. After a survey on Kaldi's components and featuresJ we applied most of state-of­ the-art ASR techniques in the Kaldi on the Farsdat based on three sets defmition. The best phone error rate on development and test set have been 20. 3o/e and 19. 8o/e. All of the codes and the recipe that was written by author have been submitted to Kaldi repository and they are accessible for free, so aU the reported results will be easily repUcable if you have access to Farsdat database.

In this paper, according to the detection and tracking of the moving vehicles at junctions, a rapid method is proposed which is based on intelligent image processing. In the detection part, the Gaussian mixture model has been used to obtain the moving parts. Then, the targets have been detected using HOG features extracted from training images, Ada-boost Cascade Classifier and the trained SVM. At the tracking part, a number of key points on the image of the vehicle were identified at first. The center of mass of the object and the edges were used to obtain these key points because these points are primarily important and more common in tracking rigid bodies. Then, these points were tracked in consecutive frames using definitive adaptive procedures. Also, the Kalman filter has been used to estimate new locations when the detector is not able to detect the targets. The major advantage of this method in comparison with the previous methods is its resistance against vehicle's overlapping and changes in Illuminations, so that the detection accuracy is 90. 80% on overloaded traffic scenes and 88. 75% on the tracking vehicles.

Question answering systems are produced and developed to provide exact answers to the question posted in natural language. One of the most important parts of question answering systems is question classification. The purpose of question classification is predicting the kind of answer needed for the question in natural language. The literature works can be categorized as rule-based and learning-based methods. This paper proposes a novel architecture for hybrid classification of questions. The results of the classifiers were combined by five methods of Weighted Voting, Behavior Knowledge space, Naive Bayes, Decision Template and Dempster-Shafer. The method uses a combination of two classifiers based on machine learning (Support Vector Machine and Sparse Representation) and one rule-based classifier. The learning-based classification uses the set of features extracted from the questions. The features are extracted on the basis of the lexical and syntactic structure of the questions. The results from the classifiers were combined by the methods that are common in the combination of one-class classifiers and the Obtained results indicate the improvement of the classification operations in comparison with the present methods.

Credibility assessment screening by a small system and receiving optimum result in minimum time is a basic need in critical gates, Therefore the aim of this research is automatic detection of stress in guilty persons through skin conductance response and photoplethysmograph signals which are convenient and ease-of-use sensors, In this paper, a set of database with interview protocol (including control and relevant questions) in mock crime (Stealing jewels) is provided, 40 subjects participated in the experiments, 28 time-frequency features are extracted from two mentioned signals, The function of dimension reduction algorithms including principal component analysis, Kernel based PCA, linear discriminant analysis, cluster based LDA is evaluated to select optimum features, Support Vector Machine, Bayesian and AdaBoost are used as classifiers, The evaluation of algorithms on database is based on LOO method, Maximum accuracy (81, 08%) is obtained through principal components analysis as feature selection method and Bayesian as classifier,

According to the researches, it turns out that human's activities are the results of the internal-neural activities of their brain. The reflection of such activities which are propagated throughout the scalp can then be acquired and processed. In this regard, brain signals can be acquired and recorded by EEG (Electroencephalography). Researchers have applied different technqiues for acquiring, pre-processing, feature extrcation and reduction and classifying EEG signal. According to published papers by Iranian researchers until 2015, it has been found that most studies have been performed in medical applications and brain computer interface fields. Sampling and receiving EEG signals have been performed more in the central region than other regions. Statistical technqiues have more been used for feature extraction than other technqiues. Finally, the support vector machines are mostly used in the classification of brain signals. At the end, a study on anxiety and depression detection on fifty cases was performed in medical field. Simulation results show that our approach achieve an accuracy of up to 97 percents.

Spectral unmixing of hyperspectral images is one of the most important research fields in remote sensing. Recently, the direct use of spectral libraries in spectral unmixing is on increase by increasing the availability of the libraries. In this way the spectral unmixing problem is converted into a sparse regression problem that is time-consuming. This is due to the existence of irrelevant spectra in the library. So these spectra should be removed in some way. In the mentioned approach which is called sparse unmixing, we do not need an endmember extraction algorithm and determination of the number of endmembers priori. Since spectral libraries usually contain highly correlated spectra, the sparse unmixing approach leads to non-admissible solutions. On the other hand, most of the proposed solutions are not noise-resistant and do not reach to a sufficiently high sparse solution. In this paper, with the purpose of overcoming the problems above, at first a machine learning approach for spectral library pruning is introduced. The spectral library is clustered using k-means algorithm based on a simple and efficient new feature space. Subtractive clustering is used for determining the cluster centers of k-means algorithm. Three distance measures, Spectral angle distance, spectral distance similarity and spectral correlation similarity tested to select the best for k-means. Then the training data needed to learn a classifier are extracted by adding different noise levels to the clustered spectra. The label of the training data is determined based on the results of spectral library clustering. After learning the classifier, each pixel of the image is classified using it. This classified image will be used for pruning the spectral library. For testing the impact of classifier type on the result of unmixing, three classifiers, decision tree classifier, neural networks and k-nearest neighbours are compared. For pruning the library, the spectra with the labels that none of the image pixels belong to, are removed from spectral library. In this way, the candidate spectra present in the image are extracted. Now, a genetic algorithm will be used for sparse unmixing. Experimental results show that spectral library pruning enhances the performance of sparse unmixing algorithms. Moreover, using k-nearest neighbor in image classification step, gives better results in the final unmixing process. Genetic algorithm that used for sparse unmixing compared with OMP and SUnSAL algorithms, works well in noisy images.

This paper presents a dynamic approach to Skin Detection-to separate the skin pixels from non-skin pixels-in colored images. The static methods which use a fixed skin color model, will fail if there are illumination variations or different skin colors in an image. Because of contextual information the proposed algorithm will be less sensitive to the uncontrolled illumination conditions. In addition, the selection of discriminant features and the fusion of them and Bayesian classification increase the accuracy of the proposed method in comparison to the reference methods.