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Automated multi-camera surveillance for the prevention and investigation of bank robberies in Austria: A case study

Automated multi-camera surveillance for the prevention and i...

引用

3rd International Conference on Imaging for Crime Detection and Prevention, ICDP 2009

作者： Zambanini, S. Blauensteiner, P. Kampel, M. Pattern Recognition and Image Processing Group Vienna University of Technology Favoritenstr. 9/1832 1040 Vienna Austria

ISBN: (纸本)9781849192071

In this paper we present the recently granted tripleB-ID project that aims to detect suspicious actions and criminal offences in bank foyers. Based on the situation in Austria and on the actual needs of our partner bank, we deduce scenarios of interest and analyse the technical challenges. As a preliminary work, we furthermore present a fully automated method for the mapping between a static and a dynamic camera and show how this can be used to easily and fast set up a master-slave camera system.

关键词： Mapping

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Multispectral imaging for analyzing ancient manuscripts

Multispectral imaging for analyzing ancient manuscripts

引用

17th European Signal processing Conference, EUSIPCO 2009

作者： Lettner, Martin Sablatnig, Robert Pattern Recognition and Image Processing Group Institute of Computer Aided Automation Vienna University of Technology 1040 Vienna Austria

In this paper we propose a method for segmenting characters in multispectral images of ancient documents. Due to the low quality of the document images the main idea of our study is to combine the multispectral behavior and contextual spatial information. Therefore we utilize a Markov Random Field model using the spectral information of the images and stroke properties to include spatial dependencies of the characters. The whole process is parameter free since we calculate the stroke properties and the Gaussian parameters for the imaging model automatically. The study shows the effectiveness of using multispectral data for a computer aided analysis of ancient text documents. We compare the results of the proposed segmentation method to traditional methods based on color images as well as gray level images. © EURASIP, 2009.

关键词： Computer aided analysis

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Automated multi-camera surveillance for the prevention and investigation of bank robberies in Austria: A case study

Automated multi-camera surveillance for the prevention and i...

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3rd International Conference on Imaging for Crime Detection and Prevention (ICDP 2009)

作者： S. Zambanini P. Blauensteiner M. Kampel Pattern Recognition and Image Processing Group University of Technology Vienna Vienna Austria

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People detection in complex scene using a cascade of boosted classifiers based on Haar-like-features

People detection in complex scene using a cascade of boosted...

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IEEE Symposium on Intelligent Vehicle

作者： M. Siala N. Khlifa F. Bremond K. Hamrouni Research Unit in Signal Processing Image Processing and Pattern Recognition(ENIT Tunisia) PULSAR Team (INRIA Sophia Antipolis Nice) Research Unit in Signal Processing Image Processing and Pattern Recognition(ENIT Tunisia) Tunisia

ISBN: (纸本)9781424435036

Pedestrian detection in a real scene is an interesting application for video surveillance systems. This paper presents our contribution to improve the work of Viola and Jones, originally designed to detect faces. This work uses a cascade of classifiers based on Adaboost using Haar features. It improves the learning step by including a decision tree presenting the different poses and possible occlusions. The method has been tested on real and complex sequences and has given a good detection despite occlusions and poses variation.

关键词： Layout Face detection Object detection Support vector machines Motion detection Support vector machine classification Humans Testing Robustness Detectors

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Spatial and Spectral Based Segmentation of Text in Multispectral images of Ancient Documents

Spatial and Spectral Based Segmentation of Text in Multispec...

引用

International Conference on Document Analysis and recognition

作者： Martin Lettner Robert Sablatnig Pattern Recognition and Image Processing Group Institute of Computer Aided Automation University of Technology Vienna Austria

ISBN: (纸本)9781424445004

In this paper we propose a character segmentation method for multispectral images of ancient documents. Due to the low quality of the images the main idea of this study is to combine the multispectral behavior and contextual spatial information. Therefore we utilize a Markov random field model using the spectral information of the images and stroke properties to include spatial dependencies of the characters. Since the stroke properties and the Gaussian parameters for the imaging model are evaluated automatically the proposed segmentation method requires no training phase. We compared the method to state of the art character segmentation methods and demonstrate the effectiveness of combining spectral and spatial features for the segmentation of characters in multispectral images.

关键词： image segmentation Multispectral imaging image analysis Color Independent component analysis Text analysis Character recognition pattern recognition Markov random fields pattern analysis

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A framework for nuclear image enhancement based on the Anscomb transform and the bayesian thresholding

A framework for nuclear image enhancement based on the Ansco...

引用

IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Imen Mbazaa Najla Gribaa Nawres Khlifa Kamel Hamrouni Research Unit of Signal Processing Image Processing and Pattern Recognition National Engineering School of Sfax (ENIS) Tunisia

Scintigraphic imagery is an important data source since it provides morphological and functional clinical informations. However, scintigraphic images present very bad quality because of several degradation factors. In fact, the recorded data are embedded in noise modelled as the realisation of Poisson process. The aim of this paper is to provide a framework able to enhance nuclear images quality by Poisson intensity estimation. This framework consists, in a first step, of performing variance-stabilizing step for the Poisson process thanks to the Anscombe transformation. So the obtained data can be considered as contaminated by a white Gaussian noise. In a second step, it uses a Bayesian technique inspired of Pizurica approach, known in the literature for exhibiting good results as for white Gaussian noise. In fact, the complex wavelet packets were exploited regarding to Pizurica algorithm.

关键词： image enhancement Bayesian methods Gaussian noise Noise reduction Wavelet packets Wavelet transforms image processing image quality Pixel Cameras

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recognition Driven Page Orientation Detection

Recognition Driven Page Orientation Detection

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IEEE International Conference on image processing

作者： Yves Rangoni Faisal Shafait Joost van Beusekom Thomas M. Breuel Image Understanding and Pattern Recognition (IUPR) Research Group German Research Center for Artificial Intelligence Kaiserslautern Germany Image Understanding and Pattern Recognition (IUPR) Research Group Technical University of Kaiserslautern Kaiserslautern Germany

In document image recognition, orientation detection of the scanned page is necessary for the following procedures to work correctly as they assume that the text is well oriented. Several methods have been proposed, but most of them rely on heuristics of the script such as the graphical asymmetry between ascenders and descenders for Roman script. The literature shows that as soon as this assumption is not fulfilled, e.g. plain capital text, noisy or degraded characters, etc. they fail. For a large-scale digitalization process, a low error and rejection rate are expected in order to reduce the amount of human intervention. We propose a recognition Driven Page Orientation Detection (RD-POD) which does not depend on external criteria or assumption on the shape of the script. It uses the OCR engine for estimating the right orientation with a few lines of the document image. The RD-POD is highly robust and accurate, and is able to detect multiple orientations. Experimental evaluation shows that our method outperforms the current state-of-the-art on UW-1 dataset with an accuracy of 99.7%. Further tests on other three large and public datasets (MARG, ICDAR07, Google 1000 books) show accuracies of above 99% on each of them.

关键词： image recognition Noise shaping Degradation Large-scale systems Humans Shape Optical character recognition software Search engines Robustness Testing

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Server-Side Prediction of Source IP Addresses Using Density Estimation

Server-Side Prediction of Source IP Addresses Using Density ...

引用

International Conference on Availability, Reliability and Security, ARES

作者： Markus Goldstein Matthias Reif Armin Stahl Thomas Breuel Research Group Image Understanding and Pattern Recognition (IUPR) German Research Center for Artificial Intelligence Kaiserslautern Germany Research Group Image Understanding and Pattern Recognition (IUPR) Technical University of Kaiserslautern Kaiserslautern Germany

Source IP addresses are often used as a major feature for user modeling in computer networks. Particularly in the field of distributed denial of service (DDoS) attack detection and mitigation traffic models make extensive use of source IP addresses for detecting anomalies. Typically the real IP address distribution is strongly undersampled due to a small amount of observations. Density estimation overcomes this shortage by taking advantage of IP neighborhood relations. In many cases simple models are implicitly used or chosen intuitively as a network based heuristic. In this paper we review and formalize existing models including a hierarchical clustering approach first. In addition, we present a modified k-means clustering algorithm for source IP density estimation as well as a statistical motivated smoothing approach using the Nadaraya-Watson kernel-weighted average. For performance evaluation we apply all methods on a 90 days real world dataset consisting of 1.3 million different source IP addresses and try to predict the users of the following next 10 days. ROC curves and an example DDoS mitigation scenario show that there is no uniformly better approach: k-means performs best when a high detection rate is needed whereas statistical smoothing works better for low false alarm rate requirements like the DDoS mitigation scenario.

关键词： Computer crime Traffic control Telecommunication traffic pattern recognition Predictive models Smoothing methods Machine learning Quality of service Routing Density functional theory

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Color-based and context-aware skin detection for online video annotation

Color-based and context-aware skin detection for online vide...

引用

IEEE Workshop on Multimedia Signal processing

作者： Christian Liensberger Julian Stottinger Martin Kampel Institute of Computer Aided Automation Pattern Recognition and Image Processing Group University of Technology Vienna Austria

By analyzing the low level features of images only, skin detection in visual data cannot be solved. To compensate for this major drawback of many approaches, we combine a state of the art recognition algorithm with color model based skin detection. Detected faces in videos are the basis for adaptive skin-color models, which are propagated throughout the video, providing a more precise and accurate model in its recognition performance than pure color based approaches. The approach is able to run in real-time and does not need prior data-specific training. We received challenging online videos from an online service provider and use additional videos from public Web platforms covering a grand variety of different skin-colors, illumination circumstances, image quality and difficulty levels. In an extensive evaluation we estimated the best performing parameters and decide on the best model propagation techniques. We show that adaptive model propagation outperforms static low level detection.

关键词： Skin Face detection image analysis Lighting Internet Automation pattern recognition image processing image color analysis pattern analysis

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MULTISPECTRAL IMAGING FOR ANALYZING ANCIENT MANUSCRIPTS

MULTISPECTRAL IMAGING FOR ANALYZING ANCIENT MANUSCRIPTS

引用

European signal processing conference

作者： Martin Lettner Robert Sablatnig Pattern Recognition and Image Processing Group Institute of Computer Aided Automation Vienna University of Technology 1040 Vienna Austria

ISBN: (纸本)9781617388767

关键词： MULTISPECTRAL Computer aided analysis grayscale images Imaging Spatial Dependency image information color image field simulation

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