Effective and real time face detection has been made possible by using the method of rectangle Haar-like features with AdaBoost learning and cascade of the strong classifiers since Viola and Jones' work [1]. After...
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ISBN:
(纸本)9781424445677
Effective and real time face detection has been made possible by using the method of rectangle Haar-like features with AdaBoost learning and cascade of the strong classifiers since Viola and Jones' work [1]. After that, Rainer Lienhart had improved Viola and Jones' work by extending set of Haar-like features [3]. However, it still has drawbacks;the detection results often have high false positives. In [2], A. Hadid et al. have used Local Binary pattern (LBP) method for face description and they applied effectively in face detection problem. However, it is slow. Therefore, it is difficult to apply in real time applications. In this work we proposed an approach to combine a boosted of Haar-like Features and LBP to achieve a good trade-off between two extreme. The system, which is built from proposed model, is conducted on MIT + CMU test set [7]. Experimental results show that our method performs favorably compared to state of the art methods.
The proceedings contain 59 papers. The topics discussed include: adaptive hybrid mean shift and particle filter;adaptive iterative receiver for CDMA systems in rayleigh fading channels;adaptive modulation and SVC-enco...
ISBN:
(纸本)9781424445684
The proceedings contain 59 papers. The topics discussed include: adaptive hybrid mean shift and particle filter;adaptive iterative receiver for CDMA systems in rayleigh fading channels;adaptive modulation and SVC-encoded video IPTV multicast over Mobile WiMAX;an application of neural networks in the connection admission control of ATM networks;an effective clustering-based approach for conceptual association rules mining;an efficient algorithm for fingerprint reference-point detection;an eigen based feature on time-frequency representation of EMG;anchoring the institutional dimension of speech acts in agents' attitudes: a logical approach;application of PAMS collaboration platform to simulation-based researches in soil science: the case of the MIcro-ORganism project;Applying type theory to formal specification of recursive multiagent systems;autonomous learning for tracking and recognition;BB84 implementation and computer reality;and boosted of Haar-like features and local binary pattern based face detection.
Due to the inherent complexity of many real-world problems, classification models have become an important tool for solving patternrecognition tasks in many disciplines such as medicine, finance and management. Accur...
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ISBN:
(纸本)9781424441549
Due to the inherent complexity of many real-world problems, classification models have become an important tool for solving patternrecognition tasks in many disciplines such as medicine, finance and management. Accuracy and transparency are two important criteria that should be satisfied by any classification model. In this paper, a transparent and relatively accurate classifier is developed using a hybrid softcomputing technique. The initial fuzzy model is first generated using a clustering method and the transparency and accuracy of the model are then simultaneously optimized using a multi-objective evolutionary technique. The proposed model is tested on two real problems; the first one is related to credit scoring problem while the other is on medical diagnosis. All the data sets used in this study are publicly available at UCI repository of machine learning database.
We describe in this paper a new hybrid approach for optimization combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) using Fuzzy Logic to integrate the results. The new evolutionary method combine...
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ISBN:
(纸本)9789899507968
We describe in this paper a new hybrid approach for optimization combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) using Fuzzy Logic to integrate the results. The new evolutionary method combines the advantages of PSO and GA to give us an improved FPSO+FGA hybrid method. Fuzzy Logic is used to combine the results of the PSO and GA in the best way possible. The new hybrid FPSO+FGA approach is compared with the PSO and GA methods with a set of benchmark mathematical functions. The proposed hybrid method is also tested with the problem of modular neural network optimization. The new hybrid FPSO+FGA method is shown to be superior with respect to both the individual evolutionary methods.
Classification based on Principal Component analysis has recently appeared in the literature in application to text-independent speaker identification. However, results have been reported for only clean speech data. I...
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Classification based on Principal Component analysis has recently appeared in the literature in application to text-independent speaker identification. However, results have been reported for only clean speech data. In this paper, we evaluate the performance of principal component classifier for text-independent speaker identification on telephone speech. We then improve its identification performance using a Vector Quantization classifier in combination, through fusion of classifier scores. An identification rate of 78.27% has been obtained on the NTIMIT database, which is well above the best identification rate ever reported in the literature obtained by using only one type of feature set.
Lately around the world the Information and Communications Technology (ICT) industry has taken interest in reducing the power consumption of ICT equipment. Communications service providers are especially interested in...
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In the recent years, rough set theory has been applied in diverse areas of research, however its application to classification problems is still a challenger. In this paper we present a new method to automatically gen...
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ISBN:
(纸本)9783642026102
In the recent years, rough set theory has been applied in diverse areas of research, however its application to classification problems is still a challenger. In this paper we present a new method to automatically generate fuzzy rules using an extension of rough sets. We use genetic algorithm to determine the granules of the knowledge to obtain the rough sets. The resulting classifier system based on the set of fuzzy rules was tested with the public databases: Iris, Wine, and Wdbc datasets, presenting accuracy rates of 100%, 100%, and 99%, respectively.
Based on moving least square, a multi-view car pose interpolation and corresponding recognition approach is proposed. This work firstly analyzes the shape characteristics of actual trace caused by ear pose varying in ...
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ISBN:
(纸本)9783642040191
Based on moving least square, a multi-view car pose interpolation and corresponding recognition approach is proposed. This work firstly analyzes the shape characteristics of actual trace caused by ear pose varying in feature space. Then according to training samples pose projection, we manage to recover the complete multi-view car pose manifold by using moving least square pose interpolation. The constructed multi-view ear pose manifolds can be easily utilized to recognize ear images captured under different views based on finding the minimal projection distance to the manifolds. The experimental results and some comparisons show the new method is superior to manifold learning method and B-Spline based recognition method.
Sample-based clustering is one of the most common methods for discovering disease subtypes as well as unknown taxonomies. By revealing hidden structures in microarray data, cluster analysis can potentially lead to mor...
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ISBN:
(纸本)9783642024801
Sample-based clustering is one of the most common methods for discovering disease subtypes as well as unknown taxonomies. By revealing hidden structures in microarray data, cluster analysis can potentially lead to more tailored therapies for patients as well as better diagnostic procedures. In this work, we present a novel method for automatically discovering clusters of samples which are coherent from a genetic point of view. Each possible cluster is characterized by a fuzzy pattern which maintains a fuzzy discretization of relevant gene expression values. Noise genes are identified and removed from the fuzzy pattern based on their probability of appearance. Possible clusters are randomly constructed and iteratively refined by following a probabilistic search and an optimization schema. Experimental results over publicly available microarray data show the effectiveness of the proposed method.
Sports coaches today have an access to a wide variety of information sources that describe the performance of their players. Cricket match data is highly available and rapidly growing in size which far exceeds the hum...
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ISBN:
(纸本)9781424429271
Sports coaches today have an access to a wide variety of information sources that describe the performance of their players. Cricket match data is highly available and rapidly growing in size which far exceeds the human abilities to analyze. Our major intention is to model an automated framework to identify specifics and correlations among play patterns, so as to haul out knowledge which can further be represented in the form of useful information in relevance to modify or improve coaching strategies and methodologies to confine performance enrichment at team level as well as individual. With this information, a coach can assess the effectiveness of certain coaching decisions and formulate game strategy for subsequent games. Since real time cricket data is too complex, Object-relational model is used to employ more sophisticated structure to store such data. Frequent pattern evaluation is imperative for sports be fond of cricket match data which facilitates recognition of main factors accounting for variances in data. While using simple apriori for interrelationship analysis, it is less time efficient because the raw data set which is too large and complex. On integrating association mining with Principal Component Analysis, the efficiency of mining algorithm is improved provided that Principal Component Analysis generates frequent patterns through statistical analysis and summarization not by repeated searching like other frequent patterns generation techniques. As the size and dimension of annotation database is large, Principal Component Analysis proceeds as a compression mechanism. Then the frequent patterns are analyzed for their interrelationship in order to generate interesting and confident rules of association.
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