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Sound-Based Multimodal Person Identification from Signature and Voice

Sound-Based Multimodal Person Identification from Signature ...

引用

international conference on Internet Monitoring and Protection, ICIMP

作者： Francis F. Li Acoustics Research Centre School of Computing Science and Engineering University of Sanford Salford Greater Manchester UK

Person identification as a security means has a variety of important applications. Many techniques and automated systems have been developed over the past few decades; each has its own advantages and limitations. There are often trade-offs amongst reliability, the ease of use, ethical/human rights issues, and acceptability in a particular application. Multimodal identification and authentication can, to some extent, alleviate the dilemmas and improve the overall performance. This paper proposes a new method of the combined use of signatures and utterances of pronounced names to identify or authenticate persons. Unlike typical signature verification methods, the dynamic features of signatures are captured as sound in this paper. The multimodal approach shows increased reliability, providing a relatively simple and potentially useful method for person identification and authentication.

关键词： Writing Biometrics Authentication Instruments Microphones pattern recognition Acoustic noise Security Humans Handwriting recognition

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Real-time detection of road markings for driving assistance applications

Real-time detection of road markings for driving assistance ...

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international conference on Computer Engineering and Systems

作者： Ioana Maria Chira Ancuta Chibulcutean Radu Gabriel Danescu Faculty of Automation and Computer Science Technical University of Cluj Napoca Cluj-Napoca Romania Computer Science Department Technical University of Cluj Napoca Cluj-Napoca Romania

For a Driving Assistance System, which is designed to improve safety on the roads, knowledge about the type of lane border markings and other painted road objects is required. Furthermore, information about the position of the painted objects can be used by other systems to create a correct perception of the road. This paper describes a Lab VIEW based system for detection, measurement and classification of painted objects. The system is able to calibrate monocular video sources, define the features of the painted objects that we look for, and detect them in real time using geometric pattern matching and edge detection. The solution can be easily deployed on a dedicated real-time computing architecture, to be used as a standalone driving assistance sensor.

关键词： Roads Feature extraction Image edge detection pattern matching Calibration Pixel Object recognition

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A Study of Pulmonary Nodule Detection in Three-Dimensional Thoracic CT Scans

A Study of Pulmonary Nodule Detection in Three-Dimensional T...

引用

international conference on Computer Modeling and Simulation, ICCMS

作者： Yang Liu Jinzhu Yang Dazhe Zhao Jiren Liu Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang China School of Information Science & Engineering Northeastern University Shenyang China

Early detection of pulmonary nodules on multi-slice computed tomography (MSCT) is an important clinical indication for early-stage lung cancer diagnosis. Currently, support vector machines (SVMs) have been widely used in pattern recognition. We are developing a nodule detection system by using the combination of SVMs. For training and testing the SVM classifier, a lung nodule simulation method in three-dimensional (3D) image space is developed to provide a better test environment. The detection method is multi-step, including 3D nodule simulation and insertion, lung fields segmentation, initial nodule candidates extraction, 3D geometry and intensity features calculation and false positives (FPs) reduction based on SVM. The proposed scheme is applied on three mixed chest MSCT datasets and evaluated using receiver operating characteristic (ROC) curve analysis. The experimental results illustrate the efficiency of the proposed method and the sensitivity of SVM classifier is found to be 86%.

关键词： Computed tomography Support vector machines Support vector machine classification Lungs Testing Cancer detection pattern recognition Computational modeling Solid modeling Image segmentation

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Fast Polar Harmonic Transforms

Fast Polar Harmonic Transforms

引用

international conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Zhuo Yang Sei-ichiro Kamata Graduate School of Information Production and System Waseda University Japan

Polar Harmonic Transform (PHT) is termed to represent a set of transforms those kernels are basic waves and harmonic in nature. PHTs consist of Polar Complex Exponential Transform (PCET), Polar Cosine Transform (PCT) and Polar Sine Transform (PST). They are proposed to represent invariant image patterns for two dimensional image retrieval and pattern recognition tasks. They are demonstrated to show superiorities comparing with other methods on describing rotation invariant patterns for images. Kernel computation of PHTs is also simple and has no numerical stability issue. However in order to increase the computation speed, fast computation method is needed especially for real world applications like limited computing environments, large image databases and realtime systems. This paper presents Fast Polar Harmonic Transforms (FPHTs) including Fast Polar Complex Exponential Transform (FPCET), Fast Polar Cosine Transform (FPCT) and Fast Polar Sine Transform (FPST) that are deduced based on mathematical properties of trigonometric functions. The proposed FPHTs are averagely over 6 ~ 8 times faster than PHTs that significantly boost computation process. The experimental results on both synthetic and real data are given to illustrate the effectiveness of the proposed fast transforms.

关键词： Transforms Kernel Harmonic analysis Delta modulation Equations pattern recognition Computational complexity

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Improved k-medoids clustering based on cluster validity index and object density

Improved k-medoids clustering based on cluster validity inde...

引用

IEEE international Advance computing conference, IACC

作者： Bharat Pardeshi Durga Toshniwal Department of Electronics and Computer Engineering Indian Institute of Technology Roorkee Roorkee India

ISBN: (纸本)9781424447909

Clustering is the process of classifying objects in to different groups by partitioning sets of data into a series of subsets called clusters. Clustering has taken its roots from algorithms like k-means and k-medoids. However conventional k-medoids clustering algorithm suffers from many limitations. Firstly, it needs to have prior knowledge about the number of cluster parameter k. Secondly, it also initially needs to make random selection of k representative objects and if these initial k medoids are not selected properly then natural cluster may not be obtained. Thirdly, it is also sensitive to the order of input dataset. First limitation was removed by using cluster validity index. Aiming at the second and third limitations of conventional k-medoids, we have proposed an improved k-medoids algorithm. In this work instead of random selection of initial k objects as medoids we have proposed a new technique for the initial representative object selection. The approach is based on density of objects. We find out set of objects which are densely populated and choose medoids from each of this obtained set. These k data objects selected as initial medoids are further used in clustering process. The validity of the proposed algorithm has been proved using iris and diet structure dataset to find the natural clusters in this datasets.

关键词： Clustering algorithms Data mining Partitioning algorithms Data engineering Iris Image segmentation Image analysis pattern analysis Market research pattern recognition

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Selection of Gabor filters for improved texture feature extraction

Selection of Gabor filters for improved texture feature extr...

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IEEE international conference on Image Processing

作者： Weitao Li KeZhi Mao Hong Zhang Tianyou Chai Research Center of Automation Northeastern University Shenyang China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Computing Science University of Alberta Edmonton Canada

Texture feature has been widely used in object recognition, image content analysis and many others. Among various approaches to texture feature extraction, Gabor filter has emerged as one of the most popular ones. Gabor filter-based feature extractor is in fact a Gabor filter bank defined by its parameters including frequencies, orientations and smooth parameters of Gaussian envelope. In the literature, different parameter settings have been suggested, and filter banks created by these parameter settings work well in general. From the perspective of pattern classification, however, filter banks thus designed may not be ideal. In the present study, we propose a new approach to Gabor filter bank design, by incorporating feature selection, i.e. filter selection, into the design process. The merits of incorporating filter selection in filter bank design are twofold. Firstly, filter selection produces a compact Gabor filter bank and hence reduces computational complexity of texture feature extraction. Secondly, Gabor filter bank thus designed produces low-dimensional feature representation with improved sample-to-feature ratio, and this in turn leads to improved performance of texture classification. Experiment results on benchmark datasets and a real application have demonstrated the effectiveness of the proposed method.

关键词： Feature extraction Ores pattern recognition Accuracy Frequency modulation Training Petroleum

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Auto-classification of insect images based on color histogram and GLCM

Auto-classification of insect images based on color histogra...

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international conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Le-Qing Zhu Zhen Zhang College of Computer Science and Information Engineering Zhejiang Gongshang University Hangzhou China Research Institute of Forest Ecology Environment and Protection Chinese Academy of Forestry Beijing China

Aims to provide general technicians who manage pects in production with a convenient way to recognize them, a novel method to classify insects by analyzing color histogram and GLCM (Gray-Level Co-occurrence Matrices) of wing images is proposed. The wing image of lepidopteran insect is preprocessed to get the ROI (Region of Interest); then the color image is first converted from RGB(Red-Green-Blue) to HSV (Hue-Saturation-Value) space, and the 1D color histograms of ROI are generated from hue and saturation distributions. Afterward, the color image is converted to grayscale one, rotated and transformed to a standard position, and their GLCM features are extracted. Matching is first undergone by computing the correlation of the histogram vectors between testing and template images; if the correlation is higher than certain threshold, then their GLCM features are further matched. The winner-take-all policy is adopted in deciding most matched species in k nearest neighbors. The method is tested at the lepidopteran insect database with 100 species. The recognition rate is as high as 71.1%. An ideal time performance is also achieved. The experimental results testify the efficiency of proposed method.

关键词： Insects Histograms Feature extraction Image color analysis Image recognition Correlation pattern recognition

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Identifying important characteristics in the KDD99 intrusion detection dataset by feature selection using a hybrid approach

Identifying important characteristics in the KDD99 intrusion...

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international conference on Telecommunications (ICT)

作者： Nelcileno Araújo Ruy de Oliveira Ed'Wilson Ferreira Ailton Akira Shinoda Bharat Bhargava Institute of Computing Federal University of Mato Grosso do Sul Cuiaba Brazil Institute of Computing Federal University of Mato Grosso Cuiabá MT Brazil Department of Informatics Federal Institute of Mato Grosso Cuiabá MT Brazil Department of Electrical Engineering State University Júlio de Mesquita Filho Ilha Solteira SP Brazil Department of Computer Science Purdue University West Lafayette IN USA

Intrusion detection datasets play a key role in fine tuning Intrusion Detection Systems (IDSs). Using such datasets one can distinguish between regular and anomalous behavior of a given node in the network. To build this dataset is not straightforward, though, as only the most significant features of the collected data for detecting the node's behavior should be considered. We propose in this paper a technique for selecting relevant features out of KDD99 using a hybrid approach toward an optimal subset of features. Unlike existing work that only detect attack or no attack conditions, our approach efficiently identifies which sort of attack each register in the dataset refers to. The evaluation results show that the optimized subset of features can improve performance of typical IDSs.

关键词： Intrusion detection Computer vision Data mining Data security Telecommunication computing Computer science Intelligent networks Informatics pattern recognition Databases

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Feature Selection for Fault Diagnosis Using Fuzzy-ARTMAP Classification and Conflict Intersection

Feature Selection for Fault Diagnosis Using Fuzzy-ARTMAP Cla...

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international conference on Broadband, Wireless computing, Communication and Applications (BWCCA)

作者： M. Benkaci A. Doncescu B. Jammes Laboratoire d''Analyse et d''Architecture des Systèmes du C.N.R.S. Toulouse France UPS INSA INP ISAE UTM LAAS Université de Toulouse Toulouse France

In automotive industry the safety of cars behavior is monitoring using computers. The information acquired on the bus communication is often redundant and not relevant. Therefore in the case of faults detection and isolation based on machine learning model, we need to reduce the number of variables according with their relevance and allowing taking decision in real time. In this paper, we propose a new approach for feature selection using fuzzy-ARTMAP classification and conflict characterization in fault diagnosis process. This approach is realized in two stages. In the first one, we classify the unfaulty functioning data of system using the fuzzy-ARTMAP classification. In the second stage, a conflict is accounted between features of test data based on the hyper-cubes resulted in the first stage. Two features are in conflict if her intersection does not belong to the model elaborated by fuzzy-ARTMAP classification. This approach is applied with success in automotive application in which the relevant features are detected and isolated.

关键词： Classification algorithms Feature extraction Fault detection Machine learning Artificial neural networks pattern recognition Databases

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An intelligent system for diagnosing sleep stages using wavelet coefficients

An intelligent system for diagnosing sleep stages using wave...

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international Joint conference on Neural Networks (IJCNN)

作者： Maryam Vatankhah Mohammad-R. Akbarzadeh-T. Ali Moghimi Departments of Electrical Engineering and Computer Engineering Ferdowsi University of Mashhad Iran Mashhad Branch ungyo researcher club members Islamic Azad University Center for Applied Research on Intelligent Systems and Soft Computing Departments of Electrical Engineering and Computer Engineering Ferdowsi University of Mashhad Iran Department of Biology Faculty of Sciences University of Mashhad Iran

Human sleep is divided into two segments, Rapid Eye Movement (REM) sleep and Non-REM (NREM) sleep. NREM sleep is further divided into 4 stages. Sleep staging attempts to identify these stages based on the signals collected in PSG. Significant information can be derived from the EEG signals collected during PSG. Wavelet coefficients are extracted from EEG signals. In order to reduce the amount of data set, the statistical features are calculated from wavelet coefficients. For performing decision making, six ANFIS classifiers and SVM classifier are used to differentiate between REM and Non-REM sleep stages. That is to say, pattern varies under the different sleep stages. Therefore, healthy humans with a regular night's sleep will follow these sleep stages in a particular pattern.

关键词： Support vector machines Time frequency analysis

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