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A novel method for boundary condition computation in soft tissue deformation

A novel method for boundary condition computation in soft ti...

引用

2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009

作者： Shengzheng, Wang Jie, Yang Kai, Xie Merchant Marine College Shanghai Maritime University 200135 Shanghai China Inst. of Image Processing and Pattern Recognition Shanghai Jiaotong University 200240 Shanghai China Methodist Hospital Research Institute Cornell University Houston TX 77030 United States

ISBN: (纸本)9780769535074

Simulating the soft tissue deformation based on bone-related planning is one of the most difficult issues in generating realistic motion of soft tissues. The difficulty of this problem arises from unclear boundary conditions. In facial soft tissue deformation, the boundary conditions of the finite element modeling are related to the bone movements. In order to better map the bone movements to the soft tissue model, a fast and robust computational strategy for boundary conditions is presented in this paper. First, a collision detection algorithm is employed to query contact position after repositioning of bones. Then, an improved 3D discrete distance field algorithm is proposed to perform imposed displacement computation of contact vertices. Finally, boundary conditions are calculated in terms of the imposed displacements. This approach can automatically handle boundary conditions of the complex geometrical modeling without prior assumption or knowledge about the position of contacts. The effectiveness of our method is demonstrated on complex soft tissue deformation. © 2008 IEEE.

关键词： Boundary conditions

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Use Fukunaga-Koontz Transform to Solve Occlusion Problems in Multitarget Tracking

Use Fukunaga-Koontz Transform to Solve Occlusion Problems in...

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Chinese Conference on pattern recognition (CCPR)

作者： Yan Zhang Fanglin Wang Shengyang Yu Third Research Institute of Ministry of Public Security Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

For multitarget tracking problems, occlusions between targets are quite tough tasks. We present a novel algorithm to solve such problems. For the two targets in occlusions, Fukunaga-Koontz transform is exploited to achieve the projection matrix, with which the two targets are projected into a low dimensional space where they are quite distinguishing. To solve the problem of the change of target appearance, the eigenspace model is used as the probabilistic observation model, with which the algorithm can learn the changes of the target appearance online. These two procedures are evaluated in the particle filter based tracking framework. Experimental results demonstrated the effectiveness of our algorithm.

关键词： Target tracking Yttrium Particle filters Particle tracking Application software Equations Security image processing pattern recognition Computer vision

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Three dimensional palmprint recognition

Three dimensional palmprint recognition

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IEEE International Conference on Systems, Man and Cybernetics

作者： Wei Li Lei Zhang David Zhang Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China Biometrics Research Center Department of Computing Hong Kong Polytechnic University Hong Kong China

Palmprint has been widely studied as its high accuracy and low cost. Most of the previous studies are based on two dimensional (2D) image of the palmprint. However, 2D image can be easily forged, which will threaten the security of palmprint authentication system. Furthermore, 2D image can be easily affected by noise, such as scrabbling and dirty in the palm. To overcome these shortcomings, we develop a three dimensional (3D) palmprint identification system. The structured-light imaging technology is adopted to collect the 3D palmprint data, from which the stable mean curvature image (MCI) is extracted. Then the competitive coding (CompCode) technique is used to code the 3D palmprint pattern according the MCI. By using score level fusion of MCI and its CompCode, promising recognition performance is achieved on our established 3D palmprint database.

关键词： Biometrics Authentication pattern recognition image recognition Data acquisition Time measurement Surface reconstruction image reconstruction Costs Data security

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Principal line based ICP alignment for palmprint verification

Principal line based ICP alignment for palmprint verificatio...

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IEEE International Conference on image processing

作者： Wei Li Lei Zhang David Zhang Jingqi Yan Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China Biometrics Research Center Department of Computing Hong Kong Polytechnic University Hong Kong China

image alignment is a crucial step in palmprint verification. However, most of the existing palmprint alignment methods use only some key points between fingers or in palm boundary to extract the region of interest (ROI), which is consequently used for feature extraction and matching. Such alignment methods can only give a coarse alignment of the palmprint images. This paper presents a new effective refinement method for palmprint alignment by adapting the iterative closest point (ICP) method to the palmprint principal lines. The proposed method offers a more accurate alignment of palmprints by correcting efficiently the shifting, rotation and scaling variations introduced in data acquisition. The experimental results show that the proposed method can greatly improve the palmprint verification accuracy in real time.

关键词： Iterative closest point algorithm Biometrics Fingers Feature extraction Data mining Iterative methods Authentication Shape image processing pattern recognition

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Fast Discriminative Linear Models for Scalable Video Tagging

Fast Discriminative Linear Models for Scalable Video Tagging

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International Conference on Machine Learning and Applications (ICMLA)

作者： Roberto Paredes Adrian Ulges Thomas Breuel Instituto Tecnológico de Informática Universidad Politecnica de valencia Spain Image Understanding and Pattern Recognition Research Group German Research Center for Artificial Intelligence Kaiserslautern Germany Image Understanding and Pattern Recognition Research Group Technical University of Kaiserslautern Kaiserslautern Germany

While video tagging (or "concept detection") is a key building block of research prototypes for video retrieval, its practical use is hindered by the computational effort associated with learning and detecting thousands of concepts. Support vector machines (SVMs), which can be considered the standard approach, scale poorly since the number of support vectors is usually high. In this paper, we propose a novel alternative that offers the benefits of rapid training and detection. This linear-discriminative method is based on the maximization of the area under the ROC. In quantitative experiments on a publicly available dataset of Web videos, we demonstrate that this approach offers a significant speedup at a moderate performance loss compared to SVMs, and also outperforms another well-known linear-discriminative method based on a Passive-Aggressive Online Learning (PAMIR).

关键词： Tagging Machine learning Feature extraction Vocabulary pattern recognition Prototypes Support vector machines Detectors Videoconference Artificial intelligence

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A discriminative learning approach for orientation detection of Urdu document images

A discriminative learning approach for orientation detection...

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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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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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A Novel Method for Boundary Condition Computation in Soft Tissue Deformation

A Novel Method for Boundary Condition Computation in Soft Ti...

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Computer Science and Information Engineering, CSIE, WRI World Congress on FLAGGED

作者： Shengzheng Wang Jie Yang Kai Xie Merchant Marine College Shanghai Maritime University Shanghai China Institute of Image Processing & Pattern Recognition Shanghai Jiaotong University Shanghai China The Methodist Hospital Research Institute Cornell university Houston TX USA

关键词： Boundary conditions Biological tissues Deformable models Computational modeling Finite element methods Bones Surgery Detection algorithms Solid modeling Skull

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Automated Ground Truth Data Generation for Newspaper Document images

Automated Ground Truth Data Generation for Newspaper Documen...

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International Conference on Document Analysis and recognition

作者： Thomas Strecker Joost van Beusekom Sahin Albayrak Thomas M. Breuel DAI Labor Technical University Berlin Germany Image Understanding and Pattern Recognition (IUPR) Research Group Technical University of Kaiserslautern Germany German Research Center for Artificial Intelligence (DFKI) Kaiserslautern Germany

ISBN: (纸本)9781424445004

In document image understanding, public datasets with ground-truth are an important part of scientific work. They are not only helpful for developing new methods, but also provide a way of comparing performance. Generating these datasets, however, is time consuming and cost-intensive work, requiring a lot of manual effort. In this paper we both propose a way to semi-automatically generate ground-truthed datasets for newspapers and provide a comprehensive dataset. The focus of this paper is layout analysis ground truth. The proposed two step approach consists of a module which automatically creates layouts and an image matching module which allows to map the ground truth information from the synthetic layout to the scanned version. In the first step, layouts are generated automatically from a news corpus. The output consists of a digital newspaper (PDF file) and an XML file containing geometric and logical layout information. In the second step, the PDF files are printed, scanned and aligned with the synthetic image obtained by rendering the PDF. Finally, the geometric and logical layout ground truth is mapped onto the scanned image.

关键词： image segmentation pattern recognition Text analysis image analysis pattern analysis image recognition Artificial intelligence Focusing image matching XML

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A New Framework for recognition of Heavily Degraded Characters in Historical Typewritten Documents Based on Semi-Supervised Clustering

A New Framework for Recognition of Heavily Degraded Characte...

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International Conference on Document Analysis and recognition

作者： Stefan Pletschacher Jianying Hu Apostolos Antonacopoulos Pattern Recognition and Image Analysis (PRImA) Research Laboratory School of Computing Science and Engineering University of Sanford Greater Manchester UK IBM Thomas J. Watson Research Center Yorktown Heights NY USA

This paper presents a new semi-supervised clustering framework to the recognition of heavily degraded characters in historical typewritten documents, where off-the-shelf OCR typically fails. The constraints are generated using typographical (collection-independent) domain knowledge and are used to guide both sample (glyph set) partitioning and metric learning. Experimental results using simple features provide encouraging evidence that this approach can lead to significantly improved clustering results compared to simple K-means clustering, as well as to clustering using a state-of-the art OCR engine.

关键词： Character recognition Degradation Optical character recognition software pattern recognition image recognition Text analysis Engines image analysis pattern analysis image texture analysis

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