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Extracting High Level Semantics by Means of Speech, Audio, and Image Primitives in Surveillance Applications

Extracting High Level Semantics by Means of Speech, Audio, a...

引用

IEEE International conference on Image Processing

作者： Lutz Goldmann Amjad Samour Mustafa Karaman Thomas Sikora Communication Systems Group Technical University Berlin Berlin Germany

Traditional surveillance systems are usually based on visual information only. With the emerging multimedia analysis techniques, interests are changing towards systems that incorporate multiple sensors and different modalities, which leads to new ways of analyzing this multimedia data and more sophisticated applications. This paper shortly reviews the ideas of traditional surveillance systems and explains actual research interests in this domain. Then, it focuses on the typical structure, goals, and applications of multimedia surveillance systems. These issues are supported by short descriptions of selected analysis steps of such a system currently under development. Some experimental results are given to illustrate the extracted semantics and to assess the performance of the individual steps.

关键词： Speech Surveillance Multimedia systems Data mining Information analysis Vehicles computer vision pattern recognition Smart cameras Data analysis

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Notice of Violation of IEEE Publication Principles: A Low Cost Embedded Color vision System Based on SX52

Notice of Violation of IEEE Publication Principles: A Low Co...

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International conference on Information and Automation (ICIA)

作者： Dong Li Yunsheng Jiang Guiyou Chen School of Engineering and Technology Shandong University of Technology Zibo Shandong China School of Control Science and Engineering Shandong University Jinan Shandong China

Notice of Violation of IEEE Publication Principles "A Low Cost Embedded Color vision System Based on SX52" by Dong Li, Yunsheng Jiang, Guiyou Chen, in the Proceedings of the 2006 IEEE International conference on Information Acquisition, August 20-23, 2006 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. This paper is a duplication of the original text from the paper cited below. The original text was copied without attribution and without permission. Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article: "A Simple Low-cost Color vision System" by Anthony Rowe, Charles Rosenberg, Illah Nourbakhsh, in the Tech Sketch paper presented at the IEEE computer vision and pattern recognition 2001 We describe a low cost embedded vision system in this paper. The goal of the system is to provide simple vision capabilities to small embedded systems in the form of an intelligent sensor. The system utilizes a low cost CMOS color camera module, a frame buffer chip and all image data is processed by a low cost microcontroller SX52. The system includes the essential functionality of color blob tracking, and improves upon it with tracking speeds of up to 50 frames per second. Other improvements were also made to facilitate communication with slower speed processors, as is often the case in a variety of robotics applications, including miniature robotics, hobby robotics and aerial robots.

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Driver Fatigue Detection Based Intelligent Vehicle Control

Driver Fatigue Detection Based Intelligent Vehicle Control

引用

International conference on pattern recognition

作者： Zutao Zhang Jia-shu Zhang Sichuan Key Laboratory of Signal and Information Processing Southwest Jiaotong University Chengdu China

ISBN: (纸本)0769525210

Driver fatigue problem is one of the important factors that cause traffic accidents. Therefore the vision-based driver fatigue detection is the most prospective commercial applications of HCI. However, it is a challenging issue due to a variety of factors such as head and eyes moving fast, external illuminations interference and realistic lighting conditions, etc. This tends to significantly limit its scope of application. In this paper, we present an intelligent vehicle control based on driver fatigue detection. Firstly, the face is located using Haar algorithm and eye location is found with projection technique. After finding eye templates, we propose a new real time eye tracking method based on unscented Kalman filter. Thirdly, driver fatigue can be detected whether the eyes are closed over 5 consecutive frames using vertical projection matching. Finally, if driver fatigue is confirmed, the vehicle cruise control is start-up with slow speed, and maintains set slow speed such as 5 km/h. The experimental results show that intelligent vehicle control based on driver fatigue detection will be availability in traffic

关键词： Fatigue Vehicle detection Intelligent vehicles Eyes Road accidents Human computer interaction Lighting Interference Face detection Vehicle driving

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Feature Point Extraction in Face Image by Neural Network

Feature Point Extraction in Face Image by Neural Network

引用

SICE Annual conference

作者： Yasuyuki Takahashi Stephen Karungaru Minoru Fukumi Norio Akamatsu The University of Tokushima Tokushima Japan

Conventionally, manual operations that specify positions of feature points such as eyes and nose are needed when morphing is carried out for a face image. In this work, the feature points are therefore extracted by using face area detection and a feature points decision methods to automate positional specification of feature points. As a result, the morphing of a face image can be carried out without manually specifying feature points. Face area detection is achieved by a threshold method using the YIQ color system. Feature points decision method extracts feature points by using a 3 layer perceptron type neural network (back-propagation). The attribute of the feature of eyes is defined to be a value of A in the color system LAB. In the same way, the attribute of feature points of the lip is defined as a value of B in the color system LAB. The extraction experiment of feature points was conducted from 120 face images by using the neural network, and the effectiveness of the present method was verified

关键词： Feature extraction Neural networks Face detection Eyes Nose pattern recognition Skin Face recognition computer vision Aging

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Fast Dichotomic Multiple Search Algorithm for Shortest Cirular Path

Fast Dichotomic Multiple Search Algorithm for Shortest Cirul...

引用

International conference on pattern recognition

作者： M. de la Gorce N. Paragios MAS Ecole Centrale de Paris Malabry France

Circular shortest path algorithms for polar objects segmentation have been proposed in the works of B. Appleton and C. Sun (2003, 2005) and C. Sun and S. Pallottino (2002) to address discrete case and extended in the work of B. Appleton and H. Talbot (2005) to the continuous domain for closed global optimal geodesic calculation. The best method up to date relies on a branch and bound approach and runs in O(u 1.6 v) on average while O(u 1.6 v) in worst case for a u times v discrete trellis warped in the direction of v. We propose an new algorithm called dichotomic multiple search (DMS) which finds the global minimum with a O(ulog 2 (u)v) worst case scenario complexity. Our algorithm relies on the fact that two minimal paths never cross more than once. This allows to sequentially partition the trellis in a dichotomic manner. Each computed circular minimal path with chosen starting point allows cutting the trellis into two sub trellis. The algorithm is then recursively applied on each sub trellis. Application to object segmentation is presented

关键词： Geophysics computing Partitioning algorithms Active contours Object segmentation computer vision Extremities Application software Image segmentation Cost function Grid computing

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Image Classification and Retrieval using Correlation

Image Classification and Retrieval using Correlation

引用

Canadian conference on computer and Robot vision, CRV

作者： I. Ahmad M.T. Ibrahim School of Computer Science University of Windsor Windsor ONT Canada Department of Computer Science COMSATS Institute of Information Technology Islamabad Pakistan

Image retrieval methods aim to retrieve relevant images from an image database that are similar to the query image. The ability to effectively retrieve non-alphanumeric data is a complex issue. The problem becomes even more difficult due to the high dimension of the variable space associated with the images. Image classification is a very active and promising research domain in the area of image management and retrieval. In this paper, we propose a new image classification and retrieval scheme that automatically selects the discriminating features. Our method consists of two phases: (i) classification of images on the basis of maximum cross correlation and (ii) retrieval of images from the database against a given query image. The proposed retrieval algorithm recursively searches similar images on the basis of their correlation against a given query image from a set of registered images in the database. The algorithm is very efficient, provided that the mean images of all of the classes are computed and available in advance. The proposed method classifies the images on the basis of maximum correlation so that the images with more similarities and, hence, exhibiting maximum correlation with each other are grouped in the same class and, are retrieved accordingly.

关键词： Image classification Image retrieval Content based retrieval Information retrieval Image databases computer science pattern recognition Image registration Information technology Space technology

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A Multiple-Perspective, Interactive Approach for Web Information Extraction and Exploration

A Multiple-Perspective, Interactive Approach for Web Informa...

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International conference on Data Engineering workshops

作者： Naureen Moon Ya-Wen Hsu R. Singh Department of Computer Science San Francisco State University San Francisco CA USA

While increasing amounts of complex information are becoming available on the web, there is, beyond keywordbased search and listing of results, a paucity of user interface paradigms and implementations that support interaction, exploration, and assimilation of information. This paper describes our design of a novel framework to address this deficiency. The proposed framework supports both direct search behavior as well as more exploratory search strategies through multiple-perspective visualization and interaction with search results. The approach is developed around the twin themes of supporting data context and facilitating effective interactions between users and data. The system supports data context through determination of semantic correlations between web pages and extraction of the spatio-temporal data contained therein. A multipleperspective environment is then used to display semantic and spatio-temporal relationships as well as to provide intuitive views of the data, specifically through web page thumbnail, map, and timeline modules. The environment supports direct interactions with the data through a reflective interface by which user selections in any one panel highlight the corresponding information in other panels. In this environment, visual cues and explicit facilities to model space and time aid in recognition, querying, and exploration of information as well as in representation and reasoning with complex relationships (such as spatio-temporal, causal, evolutionary) in the data. Experimental studies of a quantitative and qualitative nature demonstrate the efficacy of the system in facilitating both information extraction and discovery.

关键词： Data mining Web pages Moon User interfaces pattern analysis Navigation computer science Data visualization Displays Indexing

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Co-clustering Documents and Words Using Bipartite Isoperimetric Graph Partitioning

Co-clustering Documents and Words Using Bipartite Isoperimet...

引用

IEEE International conference on Data Mining (ICDM)

作者： Manjeet Rege Ming Dong Farshad Fotouhi Machine Vision and Pattern Recognition Laboratory Database and Multimedia Systems GroupDepartment of Computer Science Wayne State University Detroit MI USA Database and Multimedia Systems Group Database and Multimedia Systems GroupDepartment of Computer Science Wayne State University Detroit MI USA

In this paper, we present a novel graph theoretic approach to the problem of document-word co-clustering. In our approach, documents and words are modeled as the two vertices of a bipartite graph. We then propose isoperimetric co-clustering algorithm (ICA) - a new method for partitioning the document-word bipartite graph. ICA requires a simple solution to a sparse system of linear equations instead of the eigenvalue or SVD problem in the popular spectral co-clustering approach. Our extensive experiments performed on publicly available datasets demonstrate the advantages of ICA over spectral approach in terms of the quality, efficiency and stability in partitioning the document-word bipartite graph.

关键词： Bipartite graph Partitioning algorithms Independent component analysis Equations Eigenvalues and eigenfunctions Stability Data mining Random variables Mutual information Machine vision

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Noise Clustering Using a New Distance

Noise Clustering Using a New Distance

引用

International conference on Information and Automation (ICIA)

作者： Xiao-hong Wu College of Electrical & Information Engineering Jiangsu University Zhenjiang China

Based on a new distance, a novel noise-resistant fuzzy clustering algorithm, called alternative noise clustering (ANC) algorithm, is proposed. ANC is an extension of the noise clustering (NC) algorithm proposed by Dave. By replacing the Euclidean distance used in the objective function of NC algorithm, a new distance (non-Euclidean distance) is introduced in NC algorithm. Based on robust statistical point of view and influence function, the non-Euclidean distance is more robust than the Euclidean distance. So the ANC algorithm is more robust than the NC algorithm. Moreover, with the new distance ANC can deal with noises or outliers better than NC and fuzzy c-means (FCM). The better performance of the proposed algorithm is shown by performing experiments on data sets

关键词： Clustering algorithms Euclidean distance Noise robustness Partitioning algorithms Fuzzy sets Educational institutions Digital images computer vision pattern recognition Resists

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Sampled Two-Dimensional LDA for Face recognition with One Training Image per Person

Sampled Two-Dimensional LDA for Face Recognition with One Tr...

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International conference on Innovative Computing, Information and Control (ICICIC)

作者： Hongtao Yin Ping Fu Shengwei Meng Department of Automatic Test and Control Harbin Institute of Technology China

The two-dimensional linear discriminant analysis (2DLDA) is one of the most successful face recognition methods. However, it cannot be directly applied to the face recognition where only one sample image per person is available for training. In this paper, we present a new method based on 2DLDA to deal with the single training sample problem. The method derive a set of sub-images from a single face image by sampling, therefore obtaining multiple training samples for each class, and then apply 2DLDA to the set of newly produced samples. The proposed algorithms are compared with both the E(PC) 2 A algorithm and the SVD perturbation algorithm which is proposed for addressing the single training sample problem. Experimental results on the ORL face database show that the proposed approach is feasible and has higher recognition performance than E(PC) 2 A and SVD perturbation algorithms

关键词： Linear discriminant analysis Face recognition Image databases Image coding Automatic testing Automatic control Image sampling Face detection computer vision pattern recognition

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