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Classification and identification of objects are complex and challenging in pattern recognition and artificial intelligence if a shaky and nonshaky camera captures the videos at different distances during the day and nighttime. This work presents a model for classifying a given video as a static, uniform, or arbitrarily moving videos so that the complexity of the problem can be reduced. To avoid the threat of different distances between the objects and the camera, the proposed work introduces new steps for estimating the depth of the objects in the video frames. We explore locally weighted linear regression for feature extraction from depth information based on the notion that the regression line fits almost all the points for uniformity and does not fit for arbitrary moving. The extracted features are fed to a random forest classifier to classify static, uniform, or arbitrary moving video. The results on a large dataset, which includes videos captured day and night, show that the proposed method successfully classifies static, uniform and arbitrary videos with 0.86, 1.00 and 0.67 F-measures, respectively. Overall, our method obtains 87% accuracy for classification of static, uniform and arbitrary video, which is superior to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The degree of insulation aging of power cables is closely related to their partial discharge (PD) level, so the analysis of PD signals can be used to realize the cable condition detection. However, after performing online detection of PDs on power cables, the collected signals always contain interference signals due to the influence of electromagnetic interference in the field. In order to identify each type of local discharge signal from the interference signal, this paper proposes a clustering identification algorithm for local discharge signals, which mainly involves pulse extraction, feature parameter extraction and clustering identification process. The algorithm first extracts the pulse signal by combining the amplitude–time threshold method and the time domain energy method, then obtains the feature vector of the signal according to the synchronous multi-channel method, designs a fuzzy C-mean clustering algorithm based on subtractive clustering to determine the initial clustering center to cluster the samples and finally analyzes and checks the clustering results according to the phase resolved PD (PRPD) of a single class of signals and the fit of the two-parameter Weibull distribution function. The clustering results were analyzed and examined. The experimental results show that the proposed algorithm can extract pulse signals efficiently and accurately, and the synchronous multi-channel method can characterize pulse signals better. Meanwhile, the algorithm can determine the optimal number of classes adaptively according to the clustering effectiveness function and adopt subtractive clustering to initialize the clustering center, which can approach the optimal solution faster, and can effectively cluster a variety of discharge signals, so as to realize the type identification of single-class discharge signals. [ABSTRACT FROM AUTHOR]

Numerous research based on offline Tamil recognition deals only with few Tamil characters since it becomes extremely complicated in distinguishing small variations in large handwritten document. The writer's complexity affects the overall formation of the characters. Such types of complexities are due to discontinuation of structures, unnecessary over loops, variation in shapes as well as irregular curves. This complex issue results in enhanced error value rate. Therefore, to conquer such issues, this paper proposes a novel approach to enhance the offline Tamil handwritten character recognition by utilizing four principal steps: pre-processing, segmentation, feature extraction and classification. For optimal segmentation of Tamil characters, this paper utilizes the Tsallis entropy approach-based atom search (TEAS) optimization algorithm. Then a Newton algorithm based deep convolution extreme learning (DELM) approach is utilized for the extraction and classification of input images. Finally, experiments are carried out for numerous Tamil handwritten recognition-based approaches. The proposed Tamil character recognition utilizes the datasets of isolated Tamil handwritten characters established by HP lab India to evaluate the efficiency of the system. [ABSTRACT FROM AUTHOR]

Conventional pattern recognition methods employed for differentiating the types of insulation defects in power cables usually rely on the manual extraction of partial discharge features, which is inefficient and easily affected by subjective uncertainty. This work addresses this problem by proposing a new framework based on the automatic features extraction of partial discharge signal. The method first applies a sliding time window to convert partial discharge signals in the time domain into two-dimensional images that serve directly as the input to the convolutional neural networks (CNNs). Then a nonlinear encoder is employed to automatically extract the features of the partial discharge image data as the input of CNNs for classification. In addition, we address the overfitting problem associated with the few-shot by applying a deep convolutional generative adversarial network (DCGAN) to augment the original training dataset. Experimental results demonstrate the validity of the proposed algorithm; it increases the classification accuracy by 4.18% relative to that achieved with manually extracted features; the overall accuracy of the proposed algorithm training with the augmented dataset is 3.175% higher than that with the original experimental dataset. [ABSTRACT FROM AUTHOR]

At present, image recognition processing technology has been playing a decisive role in the field of pattern recognition, of which automatic recognition of bank notes is an important research topic. Due to the limitation of the size of bill layout and printing method, many invoice layouts are not clear, skewed or distorted, and even there are irregular handwritten signature contents, which lead to the problem of recognition of digital characters on bill surface. In this regard, this paper proposes a data acquisition and recognition algorithm based on improved BP neural network for ticket number identification, which is based on the theory of image processing and recognition, combined with improved bill information recognition technology. First, in the pre-processing stage of bill image, denoising and graying of bill image are processed. After binarization of bill image, the tilt detection method based on Bresenham integer algorithm is used to correct the tilted bill image. Secondly, character localization and feature extraction are carried out for par characters, and the target background is separated from the interference background in order to extract the desired target characters. Finally, the improved BP neural network-based bill digit data acquisition and recognition algorithm is used to realize the classification and recognition of bill characters. The experimental results show that the improved method has better classification and recognition effect than other data acquisition and recognition algorithms. [ABSTRACT FROM AUTHOR]

High-dimensional data is ubiquitous in scientific research and industrial production fields. It brings a lot of information to people, at the same time, because of its sparse and redundancy, it also brings great challenges to data mining and pattern recognition. Dimensionality reduction can reduce redundancy and noise, reduce the complexity of learning algorithms, and improve the accuracy of classification, it is an important and key step in pattern recognition system. In this paper, we overview the classical techniques for dimensionality reduction and review their properties, and categorize these techniques according to their implementation process. We deduce each algorithm in detail and intuitively show their underlying mathematical principles. Thereby, the focus is to uncover the optimization process for each technique. We compare the characteristics and limitations of each technique and summarize the scope of application, discussing a number of open problems and a perspective of research trend in future. [ABSTRACT FROM AUTHOR]

The online handwriting recognition of Tibetan characters is still in its infancy. For further research, an online handwriting database of large Tibetan character set was developed, and a recognition research was carried out on this database as a baseline result. The Northwest Minzu University Online Tibetan Handwriting Database (NMU-OLTHWDB) contains 7240 different types of characters, and the sample number in each type is 5000. The total number of samples is 7 2 4 0 × 5 0 0 0. The database covers Tibetan Character Collection, Information Technology Tibetan Coded Character set (Extension Set A), and Information Technology Tibetan Coded Character set (Extension Set B). The characters in the database are composed of 170 types of different components. We studied the online handwritten Tibetan recognition software also, and the character feature extraction, classifier training, and the statistics and analysis of the recognition results on the test set were mainly introduced. The character features included the direction attribute coefficients and spatial combination, and the feature matrix was compressed by Linear Discriminate Analysis (LDA). A quick classifier was designed by a modified quadratic discriminate function (QMQDF), and was trained with 4500 sets of samples. In the large character set, the recognition rates of top 1, top 3, top 5, and top 10 were 75.2%, 89.56%, 93.02%, and 95.96%, respectively. Moreover, an online handwriting recognition system for Tibetan large character set was designed with good performance. [ABSTRACT FROM AUTHOR]

Mastitis is the major cause of loss in dairy farming. Somatic cells are one of most important standards to detect this infection. This paper proposes a novel image processing algorithm to recognize four types of somatic cells in bovine milk automatically. First, cloud model uses to segment cell images. Second, a variety of features are extracted from regions of interest. Finally, most differential features are selected using ReliefF algorithm and performances of two classifiers, Back propagation networks (BPN) and support vector machine (SVM), are compared. The experimental results are obtained using a large set of images from different sources. The results of our proposed method is not only efficient in accuracy and speed, but also robust to illumination in bovine mastitis via optical microscopy. [ABSTRACT FROM AUTHOR]

In pattern recognition, irreducible testors have been used for feature selection. A number of exhaustive algorithms that find irreducible testors have been reported in the literature. One of the latest and more efficient algorithms reported is YYC, an incremental algorithm that finds all the irreducible testors from a training matrix. Its efficiency relies on building a smaller number of feature combinations by finding compatible sets from the top of the matrix to the current row. Nevertheless, as the number of sets currently found grows, YYC execution becomes too slow. This work proposes two improvements of YYC algorithm, incorporated in a pre-processing phase; additionally, a parallel version is implemented. The paper presents some experimental results using synthetic and real data. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach of Gabor feature based on bi-directional two-dimensional principal component analysis ((2D)2PCA) for somatic cells recognition. Firstly, Gabor features of different orientations and scales are extracted by the convolution of Gabor filter bank. Secondly, dimensionality reduction of the feature space applies (2D)2PCA in both row and column. Finally, the classifier uses Support Vector Machine (SVM) to achieve our goal. The experimental results are obtained using a large set of images from different sources. The results of our proposed method are not only efficient in accuracy and speed, but also robust to illumination in bovine mastitis via optical microscopy. [ABSTRACT FROM AUTHOR]

In many real-world image based pattern recognition tasks, the extraction and usage of task-relevant features are the most crucial part of the diagnosis. In the standard approach, either the features are given by common sense like edges or corners in image analysis, or they are directly determined by expertise. They mostly remain task-specific, although human may learn the life time, and use different features too, although same features do help in recognition. It seems that a universal feature set exists, but it is not yet systematically found. In our contribution, we try to find such a universal image feature set that is valuable for most image related tasks. We trained a shallow neural network for recognition of natural and non-natural object images before different backgrounds, including pure texture and handwritten digits, using a Shannon information-based algorithm and learning constraints. In this context, the goal was to extract those features that give the most valuable information for classification of the visual objects, hand-written digits and texture datasets by a one layer network and then classify them by a second layer. This will give a good start and performance for all other image learning tasks, implementing a transfer learning approach. As result, in our case we found that we could indeed extract unique features which are valid in all three different kinds of tasks. They give classification results that are about as good as the results reported by the corresponding literature for the specialized systems, or even better ones. [ABSTRACT FROM AUTHOR]

Writer recognition is to identify a person on the basis of handwriting, and great progress has been achieved in the past decades. In this paper, we concentrate ourselves on the issue of off-line text-independent writer recognition by summarizing the state of the art methods from the perspectives of feature extraction and classification. We also exhibit some public datasets and compare the performance of the existing prominent methods. The comparison demonstrates that the performance of the methods based on frequency domain features decreases seriously when the number of writers becomes larger, and that spatial distribution features are superior to both frequency domain features and shape features in capturing the individual traits. [ABSTRACT FROM AUTHOR]

To effectively study vibration characteristics of tracks under different track structures, wavelet transforms of the vibration data are used for pattern classification of vibration feature. First, acceleration data of the track are collected with running speed of 150km/h at 26 positions respectively on a slab tangent track, ballast tangent track and ballast curve track by a wireless sensor network (WSN). Then they are analyzed using the power spectral densities (PSDs) and wavelet-based energy spectrum analysis. The paper elaborates on the reasons for the differences of vibration energy and excitation frequencies due to the mechanism of different frequency bands and the corresponding track structures. Based on these, the instantaneous frequencies, vibration energies and durations in the low, medium, and high frequency bands are selected as the features for three track structures. A function curve representing the features is proposed to detect the abnormal track structure by a correlation analysis. Finally, the proposed method of pattern classification has been validated by experimental testings. [ABSTRACT FROM AUTHOR]

Human action recognition is very important and significant research work in numerous fields of science, for example, human-computer interaction, computer vision and crime analysis. In recent years, relative geometry features have been widely applied to the description of relative relation of body motion. It brings many benefits to action recognition such as clear description, abundant features etc. But the obvious disadvantage is that the extracted features severely rely on the local coordinate system. It is difficult to find a bijection between relative geometry and skeleton motion. To overcome this problem, many previous methods use relative rotation and translation between all skeleton pairs to increase robustness. In this paper we present a new motion representation method. It establishes a motion model based on the relative geometry with the aid of special orthogonal group SO(3). At the same time, we proved that this motion representation method can establish a bijection between relative geometry and motion of skeleton pairs. After the motion representation method in this paper is used, the computation cost of action recognition reduces from the two-way relative motion (motion from A to B and B to A) to one-way relative motion (motion from A to B or B to A) between any skeleton pair, namely, permutation problem is simplified into combinatorics problem . Finally, the experimental results of the three motion datasets are all superior to present skeleton-based action recognition methods. [ABSTRACT FROM AUTHOR]

Sport type classification and posture identification based on visual meaning of posture semantic in still images are challenging tasks. The difficulty of these tasks comes from the complex image content consisting of a player's posture, the color and texture of a player's clothes as well as complexity of the background. Player detection is one of the most important tasks in posture identification. For sport type classification without object segmentation, the new set of features, based on 64-bins color histogram, DCT coefficients, and Cb and Cr components, is introduced. To achieve high accuracy, an appropriate feature extraction technique should be also realized. For posture identification, three algorithms, concerning player region detection and suitable features for posture identification, are proposed namely blurred background elimination, irrelevant region elimination, and trimming players region. The DFT coefficients, based on image resizing and slicing techniques, are used as significant features in posture identification. Our proposed features were compared with Edge Histogram and Region-based Shape (EH and RS), two of MPEG-7 descriptors. The experimental results showed that our proposed features yielded better performance with 85.76% of accuracy in sport classification and 86.66% of accuracy in posture identification. [ABSTRACT FROM AUTHOR]

Traditional methods for automatic recognition of container code in visual images are based on segmentation and recognition of isolated characters. However, when the segment fails to separate each character from the others, those methods will not function properly. Sometimes the container code characters are printed or arranged very closely, which makes it a challenge to isolate each character. To address this issue, a new framework for automatic container code recognition (ACCR) in visual images is proposed in this paper. In this framework, code-character regions are first located by applying a horizontal high-pass filter and scan line analysis. Then, character blocks are extracted from the code-character regions and further classified into two categories, i.e. single-character block and multi-character block. Finally, a segmentation-based approach is implemented for recognition of the characters in single-character blocks, and a hidden Markov model (HMM)-based method is proposed for the multi-character blocks. The experimental results demonstrate the effectiveness of the proposed method, which can successfully recognize the container code with closely arranged characters. [ABSTRACT FROM AUTHOR]

A common problem in Electroencephalogram (EEG) analysis is how to separate EEG patterns from noisy recordings. Independent component analysis (ICA), which is an effective method to recover independent sources from sensor outputs without assuming any a priori knowledge, has been widely used in such biological signals analysis. However, when dealing with EEG signals, the mixing model usually does not satisfy the standard ICA assumptions due to the time-variable structures of source signals. In this case, EEG patterns should be precisely separated and recognized in a short time window. Another issue is that we usually over-separate the signals by ICA due to the over learning problem when the length of data is not sufficient. In order to tackle these problems mentioned above, we try to exploit both high order statistics and temporal structures of source signals under condition of short time windows. We utilize a temporal-independent component analysis (tICA) method to formulate the blind separation problem into a new framework of analyzing the mutual independence of the residual signals. Furthermore, in order to find better features for classification, both temporal and spatial features of EEG recordings are extracted by integrating tICA together with some other algorithm like Common Spatial Pattern (CSP) for feature extraction. Computer simulations are given to evaluate the efficiency and performance of tICA based on EEG data recorded not from the normal people but from some special populations suffering from neurophysiological diseases like stroke. To the best of our knowledge, this is the first time that EEG characteristics of stroke patients are explored and reported using ICA algorithm. Superior separation performance and high classification rate evidence that the tICA method is promising for EEG analysis. [ABSTRACT FROM AUTHOR]

The human eye is rich in physical and behavioral attributes that can be used for automatic person recognition. The physical attributes such as the iris attracted early attention and yielded significant recognition results, but like most physical biometrics, they have several disadvantages such as intrusive acquisition, vulnerability to spoofing attacks, etc. Consequently, during the last decade the behavioral attributes extracted from human eyes have steadily gained interest from the automatic person recognition research community. In this first of its kind survey, we present the studies utilizing the behavioral attributes of human eyes for automatic person recognition. We have proposed a unique classification based on the type of stimuli used to elicit behavioral attributes. In addition, for each approach we have carefully examined the common steps involved in automatic person recognition from database acquisition, feature extraction to classification. Lastly, we also present a comparison of the recognition results obtained by each approach. [ABSTRACT FROM AUTHOR]

Bag of Features (BoF) has gained a lot of interest in computer vision. Visual codebook based on robust appearance descriptors extracted from local image patches is an effective means of texture analysis and scene classification. This paper presents a new method for local feature description based on gray-level difference mapping called Mean Local Mapped Pattern (M-LMP). The proposed descriptor is robust to image scaling, rotation, illumination and partial viewpoint changes. The training set is composed of rotated and scaled images, with changes in illumination and view points. The test set is composed of rotated and scaled images. The proposed descriptor more effectively captures smaller differences of the image pixels than similar ones. In our experiments, we implemented an object recognition system based on the M-LMP and compared our results to the Center-Symmetric Local Binary Pattern (CS-LBP) and the Scale-Invariant Feature Transform (SIFT). The results for object classification were analyzed in a BoF methodology and show that our descriptor performs better compared to these two previously published methods. [ABSTRACT FROM AUTHOR]

Texture is an important feature that aids in identifying objects of interest or region of interest irrespective of the source of the image. In this paper, a novel and simple isopattern-based texture feature is introduced. Spatial gray scale dependencies represented by bit plane is analyzed for specific patterns and are accumulated in bins. These are scaled by half-normal weighting function to provide isopattern texture feature. The ability of this texture feature in capturing textural variations of the images despite the presence of illumination, scale and rotation is demonstrated by conducting texture analysis on Brodatz, OuTex texture datasets and its classification accuracy on Kylberg dataset. The results of these two experimentation indicate that the proposed textural feature picks variation in texture significantly and has a better texture classification accuracy of 98.26% when compared with the state-of-the-art features like Gabor, GLCM and LBP. [ABSTRACT FROM AUTHOR]

As thousands of features are available in many pattern recognition and machine learning applications, feature selection remains an important task to find the most compact representation of the original data. In the literature, although a number of feature selection methods have been developed, most of them focus on optimizing specific objective functions. In this paper, we first propose a general graph-preserving feature selection framework where graphs to be preserved vary in specific definitions, and show that a number of existing filter-type feature selection algorithms can be unified within this framework. Then, based on the proposed framework, a new filter-type feature selection method called sparsity score (SS) is proposed. This method aims to preserve the structure of a pre-defined l1 graph that is proven robust to data noise. Here, the modified sparse representation based on an l1-norm minimization problem is used to determine the graph adjacency structure and corresponding affinity weight matrix simultaneously. Furthermore, a variant of SS called supervised SS (SuSS) is also proposed, where the l1 graph to be preserved is constructed by using only data points from the same class. Experimental results of clustering and classification tasks on a series of benchmark data sets show that the proposed methods can achieve better performance than conventional filter-type feature selection methods. [ABSTRACT FROM AUTHOR]

Dimension reduction methods including feature selection and feature extraction have played an important role in data mining and pattern recognition. In this study, we propose a novel unsupervised feature selection approach based on sparse representation theory, namely Sparsity Score (SS). Due to the sparse representation procedure, SS not only owns the global property of Variance Score (VS) and the local property of Laplacian Score (LS), but also possesses the discriminating nature. Experimental results, based on three well-known face datasets (Yale, ORL and CMU PIE), reveal that SS performs well in the evaluation of the feature significance, and it significantly outperforms VS and LS. [ABSTRACT FROM AUTHOR]

In this paper, we propose an integrated approach for human detection in surveillance video. In our approach, the moving object is extracted by background subtraction; and the background model is updated by the first-order recurrence filter. Then, two complementary features are extracted for moving object classification. They are contour-based description: Fourier descriptor and region-based description: histogram of oriented gradient. As the binary classifier (support vector machine) is able to provide the posterior probability, we effectively integrate two types of features to achieve better performance. Experimental results show that the proposed approach is effective and outperforms some existing technique. [ABSTRACT FROM AUTHOR]

Recently, many dimensionality reduction (DR) algorithms have been developed, which are successfully applied to feature extraction and representation in pattern classification. However, many applications need to re-project the features to the original space. Unfortunately, most DR algorithms cannot perform reconstruction. Based on the manifold assumption, this paper proposes a General Manifold Reconstruction Framework (GMRF) to perform the reconstruction of the original data from the low dimensional DR results. Comparing with the existing reconstruction algorithms, the framework has two significant advantages. First, the proposed framework is independent of DR algorithm. That is to say, no matter what DR algorithm is used, the framework can recover the structure of the original data from the DR results. Second, the framework is space saving, which means it does not need to store any training sample after training. The storage space GMRF needed for reconstruction is far less than that of the training samples. Experiments on different dataset demonstrate that the framework performs well in the reconstruction. [ABSTRACT FROM AUTHOR]

To improve the accuracy of visual speech recognition systems, selection of visual features is of fundamental importance. Prominent features, which are of maximum relevance for speech classification, need to be selected from a large set of extracted visual attributes. Existing methods apply feature reduction and selection techniques on image pixels constituting region-of-interest (ROI) to reduce data dimensionality. We propose application of feature selection methods on geometrical features to select the most dominant physical features. Two techniques, Minimum Redundancy Maximum Relevance (mRMR) and Correlation-based Feature Selection (CFS), have been applied on the extracted visual features. Experimental results show that recognition accuracy is not compromised when a few selected features from the complete visual feature set are used for classification, thereby reducing processing time and storage overheads considerably. Results are compared with performance of principal components obtained by application of Principal Component Analysis (PCA) on our dataset. Our set of selected features outperforms the PCA transformed data. Results show that the center and corner segments of the mouth are major contributors to visual speech recognition. Teeth pixels are shown to be a prominent visual cue. It is also seen that lip width contributes more towards visual speech recognition accuracy as compared to lip height. [ABSTRACT FROM AUTHOR]

Edges are prominent features in images. The detection and analysis of edges are key issues in image processing, computer vision and pattern recognition. Wavelet provides a powerful tool to analyze the local regularity of signals. Wavelet transform has been successfully applied to the analysis and detection of edges. A great number of wavelet-based edge detection methods have been proposed over the past years. The objective of this paper is to give a brief review of these methods, and encourage the research of this topic. In practice, an image is usually of multi-structure edge, the identification of different edges, such as steps, curves and junctions play an important role in pattern recognition. In this paper, more attention is paid on the identification of different types of edges. We present the main idea and the properties of these methods. [ABSTRACT FROM AUTHOR]

For the importance of communication between human and machine interface, it would be valuable to develop an implement which has the ability to recognize emotional states. In this paper, we proposed an approach which can deal with the daily dependence and personal dependence in the data of multiple subjects and samples. 30 features were extracted from the physiological signals of subject for three states of emotion. The physiological signals measured were: electrocardiogram (ECG), skin temperature (SKT) and galvanic skin response (GSR). After removing the daily dependence and personal dependence by the statistical technique of MANOVA, six machine learning methods including Bayesian network learning, naive Bayesian classification, SVM, decision tree of C4.5, Logistic model and K-nearest-neighbor (KNN) were implemented to differentiate the emotional states. The results showed that Logistic model gives the best classification accuracy and the statistical technique of MANOVA can significantly improve the performance of all six machine learning methods in emotion recognition system. [ABSTRACT FROM AUTHOR]

Nowadays many techniques are being used to increase the reliability of human identification systems. Iris is a part of human body that is desirable for biometric identification and has favorable factors. We have focused on the reality that iris is a fractal phenomenon in this paper. During the production of new fractals, some features will be extracted by Chaos Game mechanism. These features are useful and effective in iris identification. There are three steps for iris identification with fractal and Chaos Game Theory. The first step is making a new fractal. The second step includes extracting features during the first step. Finally, the iris identification based on extracted features is the third step. We have named this technique Iris Identification based-Fractal and Chaos Game Theory (Iris-IFCGT). This technique has some fractal properties like stability against zoom, removing part of the iris image, no sensitivity on rotation and so on as well as desirable speed which helps preventing time consuming process of pattern recognition. [ABSTRACT FROM AUTHOR]

To design an effective and robust fingerprint recognition method is still an open issue. Some texture-based methods such as directional energy method and wavelet method are available nowadays. However, directional energy method is insufficient to capture the detail information of fingerprint and it is also improper to directly use wavelet method to extract the feature since the complex and rich edge information of fingerprint. In this work we propose a texture-based method called DFB-Wavelet for fingerprint recognition via combining directional filter banks (DFB) and wavelet. The region of interest (ROI) composed of nonoverlapping square blocks, is decomposed into eight directions by employing DFB. Wavelet signatures are calculated as the features of a fingerprint image from each directional subband of DFB. The feature matching is performed on the global normalized Euclidean distance between the input fingerprint features and the templates features. Experimental results show that DFB-Wavelet method has the higher accuracy compared to the traditional texture-based methods. [ABSTRACT FROM AUTHOR]

As violence in movies has harmful influence on children, in this paper, we propose a vision-based algorithm to detect violent scenes in movies. Under our definition of violence, the task of violent scene detection is decomposed into action scene detection and bloody frame detection. While previous approaches addressed only the shot level of video structure, our approach works on a more semantic-complete scene structure of video. The input video (digital movie) is first segmented into several scenes. Based on the filmmaking characteristics of action scene, some features of the scene are extracted to be fed into the support vector machine for classification. Finally, the face, blood and motion information are integrated to determine whether the action scene has violent content. Experimental results show that the proposed approach works reasonably well in detecting most of the violent scenes. Compared with related work, our approach is computationally simple yet effective. [ABSTRACT FROM AUTHOR]

Most face recognition approaches require a prior training where a given distribution of faces is assumed to further predict the identity of test faces. Such an approach may experience difficulty in identifying faces belonging to distributions different from the one provided during the training. A face recognition technique that performs well regardless of training is, therefore, interesting to consider as a basis of more sophisticated methods. In this work, the Census Transform is applied to describe the faces. Based on a scanning window which extracts local histograms of Census Features, we present a method that directly matches face samples. With this simple technique, 97.2% of the faces in the FERET fa/fb test were correctly recognized. Despite being an easy test set, we have found no other approaches in literature regarding straight comparisons of faces with such a performance. Also, a window for further improvement is presented. Among other techniques, we demonstrate how the use of SVMs over the Census Histogram representation can increase the recognition performance. [ABSTRACT FROM AUTHOR]

Identifying the type of an approaching aircraft, should it be a helicopter, a fighter jet or a passenger plane, is an important task in both military and civilian practices. The task in question is normally done by using radar or RF signals. In this study, we suggest an alternative method that introduces the use of a still image instead of RF or radar data. The image was transformed to a binary black and white image, using a Matlab script which utilizes Image Processing Toolbox commands of Matlab, in order to extract the necessary features. The extracted image data of four different types of aircraft was fed into a three-layered feed forward artificial neural network for classification. Satisfactory results were achieved as the rate of successful classification turned out to be 97% on average. [ABSTRACT FROM AUTHOR]

It is difficult but crucial for minutiae extraction and pseudo minutiae deletion of low quality fingerprint images in auto fingerprint identification systems. Traditional methods based on thinning images or gray-level images are, however, susceptible to noise. Reference 14 indicated that principal curves based fingerprint minutiae extraction was feasible to overcome the drawback, but the extended polygonal line (EPL) principal curves algorithm used in the paper extracted the principal curves ineffectively. As the fingerprint data sets are usually large, the original EPL principal curves algorithm is time-consuming. Meanwhile, scattered fingerprint data lead to the deviation of fingerprint skeleton. In this paper, the algorithm is modified, and a fingerprint minutiae extraction and pseudo minutiae detection method based on principal curves is proposed. Experimental results show that the modified EPL principal curves algorithm outperforms the original EPL algorithm both in efficiency and quality, and the proposed minutiae extraction method outperforms the methods proposed by Miao under noise conditions. [ABSTRACT FROM AUTHOR]

In this paper, we propose a feature-based scheme for detecting different genres of video shot transitions based on spatio-temporal analysis and model parameter estimation. In feature extraction, the histogram difference and its modified versions are calculated from the effectiveness of detecting cuts and reducing the impact of fleeting lights. We propose a hybrid algorithm composed of adaptive thresholding, parameter calculation, and transition duration refinement to measure model parameters. Some properties of the associated model parameters of each transition are computed as features. A feature measuring the time gap between two consecutive shots is also adopted. After feature extraction, a fuzzy classifier integrates these features to distinguish nontransitions, cuts, and dissolve-type features from one to another. Many test videos having different types of shots are used for performance evaluation. The experimental results demonstrate that the proposed scheme not only detects cuts, dissolves, and fades well, but also accurately locates the duration of each dissolve-type transition. In addition, the proposed scheme outperforms some existing methods in terms of cut and dissolve detection. [ABSTRACT FROM AUTHOR]

The literature on the topic has shown a strong correlation between the degree of precision of face localization and the face recognition performance. Hence, there is a need for precise facial feature detectors, as well as objective measures for their evaluation and comparison. In this paper, we will present significant improvements to a previous method for precise eye center localization, by integrating a module for mouth localization. The technique is based on Support Vector Machines trained on optimally chosen Haar wavelet coefficients. The method has been tested on several public databases; the results are reported and compared according to a standard error measure. The tests show that the algorithm achieves high precision of localization. [ABSTRACT FROM AUTHOR]

Human face analysis has attracted a large number of researchers from various fields, such as computer vision, image processing, neurophysiology or psychology. One of the particular aspects of human face analysis is encompassed by facial expression recognition task. A novel method based on phase congruency for extracting the facial features used in the facial expression classification procedure is developed. Considering a set of image samples comprising humans expressing various expressions, this new approach computes the phase congruency map between the samples. The analysis is performed in the frequency space where the similarity (or dissimilarity) between sample phases is measured to form discriminant features. The experiments were run using samples from two facial expression databases. To assess the method's performance, the technique is compared to the state-of-the art techniques utilized for classifying facial expressions, such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA), and Gabor jets. The features extracted by the aforementioned techniques are further classified using two classifiers: a distance-based classifier and a Support Vector Machine-based classifier. Experiments reveal superior facial expression recognition performance for the proposed approach with respect to other techniques. [ABSTRACT FROM AUTHOR]

A new facial biometric scheme is proposed in this paper. Three steps are included. First, a new nontensor product bivariate wavelet is utilized to get different facial frequency components. Then a modified 2D linear discriminant technique (M2DLD) is applied on these frequency components to enhance the discrimination of the facial features. Finally, support vector machine (SVM) is adopted for classification. Compared with the traditional tensor product wavelet, the new nontensor product wavelet can detect more singular facial features in the high-frequency components. Earlier studies show that the high-frequency components are sensitive to facial expression variations and minor occlusions, while the low-frequency component is sensitive to illumination changes. Therefore, there are two advantages of using the new nontensor product wavelet compared with the traditional tensor product one. First, the low-frequency component is more robust to the expression variations and minor occlusions, which indicates that it is more efficient in facial feature representation. Second, the corresponding high-frequency components are more robust to the illumination changes, subsequently it is more powerful for classification as well. The application of the M2DLD on these wavelet frequency components enhances the discrimination of the facial features while reducing the feature vectors dimension a lot. The experimental results on the AR database and the PIE database verified the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper the problem of nonlinear feature extraction based on the optimization of the Fisher criterion is analyzed. A new nonlinear feature extraction method is proposed. The method does not make use of numerical algorithms and it has an analytical (closed-form) solution. Moreover, no assumptions on the class probability distribution functions are imposed. The proposed method is applied to some standard pattern recognition problems and compared with other classical methodologies already proposed in the literature. The performance of the proposed method turned out to be superior when compared with the other methods studied. [ABSTRACT FROM AUTHOR]

Most of the traditional methods for shape classification are based on contour. They often encounter difficulties when dealing with classes that have large nonlinear variability, especially when the variability is structural or due to articulation. It is well-known that shape representation based on skeletons is superior to contour based representation in such situations. However, approaches to shape similarity based on skeletons suffer from the instability of skeletons, and matching of skeleton graphs is still an open problem. Using a new skeleton pruning method, we are able to obtain stable pruned skeletons even in the presence of significant contour distortions. We also propose a new method for matching of skeleton graphs. In contrast to most existing methods, it does not require converting of skeleton graphs to trees and it does not require any graph editing. Shape classification is done with Bayesian classifier. We present excellent classification results for complete shapes. [ABSTRACT FROM AUTHOR]

In this paper we propose a technique for detecting and correcting the skew of text areas in a document. The documents we work with may contain several areas of text with different skew angles. First, a text localization procedure is applied based on connected components analysis. Specifically, the connected components of the document are extracted and filtered according to their size and geometric characteristics. Next, the candidate characters are grouped using a nearest neighbor approach to form words and then based on these words text lines of any skew are constructed. Then, the top-line and baseline for each text line are estimated using linear regression. Text lines in near locations, having similar skew angles, are grown to form text areas. For each text area a local skew angle is estimated and then these text areas are skew corrected independently to horizontal or vertical orientation. The technique has been extensively tested on a variety of document images and its accuracy and robustness is compared with other existing techniques. [ABSTRACT FROM AUTHOR]

In a classification problem, quite often the dimension of the measurement vector is large. Some of these measurements may not be important for separating the classes. Removal of these measurement variables not only reduces the computational cost but also leads to better understanding of class separability. There are some methods in the existing literature for reducing the dimensionality of a classification problem without losing much of the separability information. However, these dimension reduction procedures usually work well for linear classifiers. In the case where competing classes are not linearly separable, one has to look for ideal "features" which could be some transformations of one or more measurements. In this paper, we make an attempt to tackle both, the problems of dimension reduction and feature extraction, by considering a projection pursuit regression model. The single hidden layer perceptron model and some other popular models can be viewed as special cases of this model. An iterative algorithm based on backfitting is proposed to select the features dynamically, and cross-validation method is used to select the ideal number of features. We carry out an extensive simulation study to show the effectiveness of this fully automatic method. [ABSTRACT FROM AUTHOR]

In many pattern recognition problems, it is desirable to reduce the number of input features by extracting important features related to the problems. By focusing on only the problem-relevant features, the dimension of features can be greatly reduced and thereby can result in a better generalization performance with less computational complexity. In this paper, we propose a feature extraction method for handling classification problems. The proposed algorithm is used to search for a set of linear combinations of the original features, whose mutual information with the output class can be maximized. The mutual information between the extracted features and the output class is calculated by using the probability density estimation based on the Parzen window method. A greedy algorithm using the gradient descent method is used to determine the new features. The computational load is proportional to the square of the number of samples. The proposed method was applied to several classification problems, which showed better or comparable performances than the conventional feature extraction methods. [ABSTRACT FROM AUTHOR]

A new feature extraction method for face recognition based on principal component analysis (PCA) and fractional-step linear discriminant analysis (F-LDA) is given in this paper. In order to reduce the computation complexity, PCA is first used to reduce the dimension. In addition, before using F-LDA, we transform the pooled within-class scatter matrix into an identity matrix. The proposed method is tested on AR and UMIST face databases. Experiment results show that our method gains higher classification accuracy than other existing methods used in the experiment. [ABSTRACT FROM AUTHOR]

The study investigates the perceptual feature similarity between different lexicons based on visual perception of the words and their representation through an observation sequence. We confirm that it is possible to use databases, which are similar in terms of morphological/perceptual features to improve the recognition performance. In this work, we demonstrated through experimentation, that it is possible to improve the recognition rate of handwritten Portuguese words by adding samples of French words in the training set. Experimental results show the efficiency of this strategy reducing the error rate. [ABSTRACT FROM AUTHOR]