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Printing Technique Classification for Document Counterfeit Detection

Printing Technique Classification for Document Counterfeit D...

引用

International Conference on Computational Intelligence and Security

作者： Christoph H. Lampert Lin Mei Thomas M. Breuel Image Understanding and Pattern Recognition Laboratory German Research Center for Artificial Intelligence Kaiserslautern Germany

ISBN: (纸本)1424406048

The detection of counterfeit in printed documents is currently based mainly on built-in security features or on human expertise. We propose a classification system that supports non-expert users to distinguish original documents from PC-made forgeries by analyzing the printing technique used. Each letter in a document is classified using a support vector machine that has been trained to distinguish laser from inkjet printouts. A color-coded visualization helps the user to interpret the per-letter classification results

关键词： Printing Counterfeiting Forgery Security Humans Laser transitions Spatial databases Data mining Ink Artificial intelligence

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Distance measures for layout-based document image retrieval

Distance measures for layout-based document image retrieval

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International Workshop on Document image Analysis for Libraries

作者： J. van Beusekom D. Keysers F. Shafait T.M. Breuel Image Understanding and Pattern Recognition (IUPR) Research Group German Research Center for Artificial Intelligence (DFKI) Technical University of Kaiserslautern Kaiserslautern Germany

Most methods for document image retrieval rely solely on text information to find similar documents. This paper describes a way to use layout information for document image retrieval instead. A new class of distance measures is introduced for documents with Manhattan layouts, based on a two-step procedure: First, the distances between the blocks of two layouts are calculated. Then, the blocks of one layout are assigned to the blocks of the other layout in a matching step. Different block distances and matching methods are compared and evaluated using the publicly available MARG database. On this dataset, the layout type can be determined successfully in 92.6% of the cases using the best distance measure in a nearest neighbor classifier. The experiments show that the best distance measure for this task is the overlapping area combined with the Manhattan distance of the corner points as block distance together with the minimum weight edge cover matching

关键词： image retrieval Databases Optical character recognition software Algorithm design and analysis Information retrieval pattern recognition Artificial intelligence Nearest neighbor searches Area measurement Current measurement

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Background variability modeling for statistical layout analysis

Background variability modeling for statistical layout analy...

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International Conference on pattern recognition

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

Geometric layout analysis plays an important role in document image understanding. Many algorithms known in literature work well on standard document images, achieving high text line segmentation accuracy on the UW-III dataset. These algorithms rely on certain assumptions about document layouts, and fail when their underlying assumptions are not met. Also, they do not provide confidence scores for their output. These two problems limit the usefulness of general purpose layout analysis methods in large scale applications. In this contribution, we propose a statistically motivated model-based trainable layout analysis system that allows assumption-free adaptation to different layout types and produces likelihood estimates of the correctness of the computed page segmentation. The performance of our approach is tested on a subset of the Google 1000 books dataset where it achieved a text line segmentation accuracy of 98.4% on layouts where other general-purpose algorithms failed to do a correct segmentation.

关键词： Training data Solid modeling image analysis pattern analysis Stochastic processes pattern recognition Artificial intelligence image segmentation Large-scale systems Testing

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Pixel-Accurate Representation and Evaluation of Page Segmentation in Document images

Pixel-Accurate Representation and Evaluation of Page Segment...

引用

International Conference on pattern recognition

作者： F. Shafait D. Keysers T.M. Breuel Image Understanding and Pattern Recognition (IUPR) Research Group German Research Center for Artificial Intelligence (DFKI) Technical University of Kaiserslautern Kaiserslautern Germany

ISBN: (纸本)0769525210

This paper presents a new representation and evaluation procedure of page segmentation algorithms and analyzes six widely-used layout analysis algorithms using the procedure. The method permits a detailed analysis of the behavior of page segmentation algorithms in terms of over- and undersegmentation at different layout levels, as well as determination of the geometric accuracy of the segmentation. The representation of document layouts relies on labeling each pixel according to its function in the overall segmentation, permitting pixel-accurate representation of layout information of arbitrary layouts and allowing background pixels to be classified as "don't care". Our representations can be encoded easily in standard color image formats like PNG, permitting easy interchange of segmentation results and ground truth

关键词： Pixel image segmentation Algorithm design and analysis Color pattern recognition image analysis pattern analysis Measurement Artificial intelligence Labeling

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A discriminative learning approach for orientation detection of Urdu document images

A discriminative learning approach for orientation detection...

引用

IEEE International Conference on Multi Topic

作者： Sheikh Faisal Rashid Syed Saqib Bukhari Faisal Shafait Thomas M. Breuel Image Understanding and Pattern Recognition (IUPR) Research Group Technical University of Kaiserslautern Kaiserslautern Germany German Research Center for Artificial Intelligence Kaiserslautern Germany

Orientation detection is an important preprocessing step for accurate recognition of text from document images. Many existing orientation detection techniques are based on the fact that in Roman script text ascenders occur more likely than descenders, but this approach is not applicable to document of other scripts like Urdu, Arabic, etc. In this paper, we propose a discriminative learning approach for orientation detection of Urdu documents with varying layouts and fonts. The main advantage of our approach is that it can be applied to documents of other scripts easily and accurately. Our approach is based on classification of individual connected component orientation in the document image, and then the orientation of the page image is determined via majority count. A convolutional neural network is trained as discriminative learning model for the labeled Urdu books dataset with four target orientations: 0, 90, 180 and 270 degrees. We demonstrate the effectiveness of our method on dataset of Urdu documents categorized into the layouts of book, novel and poetry. We achieved 100% orientation detection accuracy on a test set of 328 document images.

关键词： Neural networks Optical character recognition software Shape pattern recognition image recognition Books Cellular neural networks Learning Artificial intelligence Text recognition

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Improvements to Uncalibrated Feature-Based Stereo Matching for Document images by Using Text-Line Segmentation

Improvements to Uncalibrated Feature-Based Stereo Matching f...

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IAPR International Workshop on Document Analysis Systems, DAS

作者： Muhammad Zeshan Afzal Martin Kramer Syed Saqib Bukhari Faisal Shafait Thomas M. Breuel Image Understanding and Pattern Recognition Group Technical University of Kaiserslautern Kaiserslautern Germany Multimedia Analysis and Data Mining Research Group German Research Center for Artificial Intelligence Kaiserslautern Germany

Document images prove to be a difficult case for standard stereo correspondence approaches. One of the major problem is that document images are highly self-similar. Most algorithms try to tackle this problem by incorporating a global optimization scheme, which tends to be computationally expensive. In this paper, we show that incorporation of layout information into the matching paradigm, as a grouping entity for features, leads to better results in terms of robustness, efficiency, and ultimately in a better 3D model of the captured document, that can be used in various document restoration systems. This can be seen as a divide and conquer approach that partitions the search space into portions given by each grouping entity and then solves each of them independently. As a grouping entity text-lines are preferred over individual character blobs because it is easier to establish correspondences. Text-line extraction works reasonably well on stereo image pairs in the presence of perspective distortions. The proposed approach is highly efficient and matches obtained are more reliable. The claims are backed up by showing their practical applicability through experimental evaluations.

关键词： Feature extraction Robustness Three dimensional displays image segmentation Cameras Solid modeling Stereo vision

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Car plate detection using cascaded tree-style learner based on hybrid object features

Car plate detection using cascaded tree-style learner based ...

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IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006

作者： Wu, Qiang Zhang, Huaifeng Jia, Wenjing He, Xiangjian Yang, Jie Hintz, Tom Computer Vision Research Group University of Technology Sydney PO Box 123 Broadway NSW 2007 Australia Inst. of Image Process. and Pattern Recognition Shanghai Jiaotong Univ. China

ISBN: (纸本)0769526888

Car plate detection is a key component in automatic license plate recognition system. This paper adopts an enhanced cascaded tree style learner framework for car plate detection using the hybrid object features including the simple statistical features and Harr-like features. The statistical features are useful for simplifying the process on cascade classifier. The cascaded tree-style detector design will further reduce the false alarm and the false dismissal while retaining a high detection ratio. The experimental results obtained by the proposed algorithm exhibit the encouraging performance. © 2006 IEEE.

关键词： Object recognition

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A simple and effective approach for border noise removal from document images

A simple and effective approach for border noise removal fro...

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IEEE International Conference on Multi Topic

作者： Faisal Shafait Thomas M. Breuel Image Understanding and Pattern Recognition Research Group German Research Center for Artificial Intelligence Kaiserslautern Germany Department of Computer Science Technical University of Kaiserslautern Kaiserslautern Germany

When digitizing bound material like books or magazines, marginal noise appears along the page border. This noise consists of undesired text parts from the neighboring page and/or speckles that result from the binarization process. When a keyword based search is performed in a digitized collection, textual noise in particular poses problems since the returned search results might correspond to textual noise instead of actual contents of the page. Manually removing marginal noise for each page is not feasible in large scale digitization projects. In this paper, we present a simple and effective approach for removing both textual and non-textual noise by finding borders of noise regions using projection profile analysis. We demonstrate the effectiveness of our approach by evaluating it quantitatively on the widely used University of Washington (UW3) dataset. The results show that our approach reduces the noise ratio from 70% to 20% while retaining more than 99% of actual page contents. Comparison with state-of-the-art approaches shows that our algorithm performs comparable to them, while being simple to understand and easy to implement. We also provide an open source implementation of our method as part of the OCRopus OCR system.

关键词： Books Optical character recognition software Filters Signal to noise ratio Optical noise Character recognition Bars Cleaning image analysis pattern recognition

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Real-time multi-resolution digital elevation map creation using the sensor model of a stereovision sensor

Real-time multi-resolution digital elevation map creation us...

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International Conference on Intelligent Transportation

作者： Szilárd Mandici Sergiu Nedevschi Image Processing and Pattern Recognition Research Group Technical University of Cluj-Napoca Romania

ISBN: (纸本)9781509018901

This paper presents an approach for constructing high quality Digital Elevation Maps (DEMs) from dense stereo data. As opposed to classical DEM creation algorithms, the proposed solution uses the sensor models of the stereovision sensor to correct the raw data. The direct and inverse sensor models are found from experiments. The algorithm decomposes the 2D cells containing vertical objects or object boundaries in order to model the objects more precisely. The approach distinguishes between 2 types of cells: cells containing horizontal and vertical objects. For the vertical object cells, connected components in the histogram of heights are found while in the horizontal object cells, the most representative height is determined using a mode-seeking approach similar to mean shift. The 3D reconstructed points are assigned to the cells around it by considering the probability of correspondence based on the 2D Gaussian component of the direct sensor model. In order to eliminate the noise, a similarity measure is computed between each 3D point and cell based on the calculated DEM cell descriptor and is discarded if the similarity score is smaller than a predefined threshold. The algorithm is implemented on the GPU in order to achieve a real-time execution speed and it is evaluated extensively in comparison with other DEM creation algorithms using the latest KITTI stereo benchmark data. The obtained DEM is denser, more accurate and the object boundaries are modeled more precisely.

关键词： Three-dimensional displays Robot sensing systems image reconstruction Two dimensional displays Histograms Solid modeling Mathematical model

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Bayes Optimal DDoS Mitigation by Adaptive History-Based IP Filtering

Bayes Optimal DDoS Mitigation by Adaptive History-Based IP F...

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International Conference on Networking (ICN)

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

Distributed denial of service (DDoS) attacks are today the most destabilizing factor in the global internet and there is a strong need for sophisticated solutions. We introduce a formal statistical framework and derive a Bayes optimal packet classifier from it. Our proposed practical algorithm "adaptive history-based IP filtering" (AHIF) mitigates DDoS attacks near the victim and outperforms existing methods by at least 32% in terms of collateral damage. Furthermore, it adjusts to the strength of an ongoing attack and ensures availability of the attacked server. In contrast to other adaptive solutions, firewall rulesets used to resist an attack can be precalculated before an attack takes place. This ensures an immediate response in a DDoS emergency. For evaluation, simulated DDoS attacks and two real-world user traffic datasets are used.

关键词： Adaptive filters Computer crime Information filtering Information filters Web and internet services Web server IP networks Traffic control Law Legal factors

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