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A New Approach of Color image Quantization

通讯和计算机（中英文版） 2005年第3期2卷 72-76页

作者： HU Xinrong WANG Tianzhen LI Dehua Institute for Pattern Recognition and Artificial Intelligence State Education Commission Laboratory for Image Processing and Intelligence Control HuaZhong Univ. of Science＆Technology WuHan 430074 China Dept. of Computer Science ＆Technology Wuhan Institute of Science＆Technology WuHan 430074 China Institute of linage Processing WuHan Univ. of Technology Wuhan 430070China

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Multimedia Content Representation, Classification and Security 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Bilge Gunsel Anil K. Jain A. Murat Tekalp Bülent Sankur

ISBN: (数字)9783540393931

ISBN: (纸本)9783540393924

We would like to welcome you to the proceedings of MRCS 2006, Workshop on Multimedia Content Representation, Classi?cation and Security, held Sept- ber 11–13, 2006, in Istanbul, Turkey. The goal of MRCS 2006 was to provide an erudite but friendly forum where academic and industrial researchers could interact, discuss emerging multimedia techniques and assess the signi?cance of content representation and security techniques within their problem domains. We received more than 190 submissions from 30 countries. All papers were subjected to thorough peer review. The ?nal decisions were based on the cri- cisms and recommendations of the reviewers and the relevance of papers to the goals of the conference. Only 52% of the papers submitted were accepted for inclusion in the program. In addition to the contributed papers, four distinguished researchers agreed to deliver keynote speeches, namely: – Ed Delp on multimedia security – Pierre Moulin on data hiding – John Smith on multimedia content-based indexing and search – Mar´ ?o A. T. Figueiredo on semi-supervised learning.

关键词： Computer Applications Multimedia Information Systems Information Storage and Retrieval Computer Communication Networks Information Systems Applications (incl. Internet) image processing and Computer Vision

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Online learning of multi-feature weights for robust object tracking

Online learning of multi-feature weights for robust object t...

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IEEE International Conference on image processing

作者： Tao Zhou Harish Bhaskar Kai Xie Jie Yang Xiangjian He Pengfei Shi Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China Dept. of Elec. & Comp. Engg. Khalifa Univ. of Science Technology and Research Abu Dhabi U.A.E Faculty of Engineering and Information Technology University of Technology Sydney Australia

Sparse Representation based Classification (SRC) and its potential in object tracking have been explored in recent years. However, the trade-off between the discriminative ability of the overly emphasized sparse representation and the lack of insight on correlation of visual information has raised questions over the general applicability of such methods in object tracking. In addition, the need for the optimization of a series of l 1 -regularized least square norm, increases the computational complexity thereby limiting their usage in real-time applications. In this paper, a novel approach to robust object tracking is proposed. First, the variations in the appearance of the tracked target is modelled using PCA basis vectors, and further, a l 2 -regularized least square method is used to solve the proposed representation model. In order to improve the robustness of feature representation in object tracking applications, weights are associated with multiple trackers; each formulated using a different feature, and adapted via an online learning scheme. Finally, a decision fusion criterion is imposed to generate an optimized output through the weighted combination of different tracking results. Experiments on challenging video sequences have demonstrated the superior accuracy and robustness of the proposed method in comparison to thirteen other state-of-the-art baselines.

关键词： Target tracking Robustness Object tracking Lighting Clutter Computed tomography Visualization

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IEEE International Symposium on Biomedical Imaging

作者： V Cheplygina A. van Opbroek M. A. Ikram M. W. Vernooij M. de Bruijne Biomedical Imaging Group Rotterdam Erasmus Medical Center The Netherlands Pattern Recognition Laboratory Delft University of Technology The Netherlands Dept. Epidemiology and Radiology Erasmus Medical Center The Netherlands The Image Section University of Copenhagen Denmark

ISBN: (纸本)9781479923519

Supervised classification is widely used for image segmentation. To work effectively, these techniques need large amounts of labeled training data, that is representative of the test data. Different patient groups, different scanners or different scanning protocols can lead to differences between the images, thus representative data might not be available. Transfer learning techniques can be used to account for these differences, thus taking advantage of all the available data acquired with different protocols. We investigate the use of classifier ensembles, where each classifier is weighted according to the similarity between the data it is trained on, and the data it needs to segment. We examine 3 asymmetric similarity measures that can be used in scenarios where no labeled data from a newly introduced scanner or scanning protocol is available. We show that the asymmetry is informative and the direction of measurement needs to be chosen carefully. We also show that a point set similarity measure is robust across different studies, and outperforms state-of-the-art results on a multi-center brain tissue segmentation task.

关键词： Training image segmentation Protocols Brain Biomedical imaging Biomedical measurement Training data

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Transfer learning by asymmetric image weighting for segmentation across scanners

arXiv

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arXiv 2017年

作者： Cheplygina, Veronika van Opbroek, Annegreet Arfan Ikram, M. Vernooij, Meike W. de Bruijne, Marleen Biomedical Imaging Group Rotterdam Depts. Radiology and Medical Informatics Erasmus Medical Center Rotterdam Netherlands Pattern Recognition Laboratory Dept. Intelligent Systems Delft University of Technology Delft Netherlands Dept. Epidemiology and Radiology Erasmus Medical Center Rotterdam Netherlands The Image Section Dept. Computer Science University of Copenhagen Copenhagen Denmark

Supervised learning has been very successful for automatic segmentation of images from a single scanner. However, several papers report deteriorated performances when using classifiers trained on images from one scanner to segment images from other scanners. We propose a transfer learning classifier that adapts to differences between training and test images. This method uses a weighted ensemble of classifiers trained on individual images. The weight of each classifier is determined by the similarity between its training image and the test image. We examine three unsupervised similarity measures, which can be used in scenarios where no labeled data from a newly introduced scanner or scanning protocol is available. The measures are based on a divergence, a bag distance, and on estimating the labels with a clustering procedure. These measures are asymmetric. We study whether the asymmetry can improve classification. Out of the three similarity measures, the bag similarity measure is the most robust across different studies and achieves excellent results on four brain tissue segmentation datasets and three white matter lesion segmentation datasets, acquired at different centers and with different scanners and scanning protocols. We show that the asymmetry can indeed be informative, and that computing the similarity from the test image to the training images is more appropriate than the opposite direction. Copyright © 2017, The Authors. All rights reserved.

关键词： Scanning

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Restoring magnetic anomalies

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Archaeological Prospection 1996年第4期3卷 185-197页

作者： Eder-Hinterleitner, A. Neubauer, W. Melichar, P. Institute for Automation Dept. For Pattern Recognition and Image Processing Vienna A-1040 Treitlstr. 3/1832 Austria Institute for Prehistory University of Vienna Vienna A-1190 F. Kleing. 1 Austria Central Institute for Meteorology and Geodynamics Vienna A-1190 Hohe Warte 38 Austria

A caesium magnetometer attached to a handcart enables accurate magnetic prospection of large areas within short times. The roughness of the earth's surface, minor differences in handling the handcart and a lot of other reasons are possible sources for various errors during a survey. Standard image preprocessing methods, such as median or low-pass filtering, remove not only errors but also important information and deform the shape of magnetic anomalies. We present four special preprocessing methods to remove errors automatically by protecting and restoring the real value and shape of anomalies and compare them with standard preprocessing methods. The four methods are equalizing subgrid shifts, equalizing line shifts, removing spikes and removing displacement errors. © 1996 John Wiley and Sons, Ltd.

关键词： image preprocessing image restoration Magnetics Prospection

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On texture in document images

On texture in document images

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Conference on Computer Vision and pattern recognition (CVPR)

作者： A.K. Jain S.K. Bhattacharjee Y. Chen Pat tern Recognition & Image Processing Laboratory Computer Science Department Michigan State University East Lansing MI USA Dept. of Comput. Sci. Michigan State Univ. East Lansing MI USA

A multichannel filtering-based texture segmentation method is applied to a variety of document image processing problems: text-graphics separation, address-block location, and bar code localization. In each of these segmentation problems, the text context or bar code in the image is considered to define a unique texture. Thus, all three document analysis problems can be posed as texture segmentation problems. Two-dimensional Gabor filters are used to compute texture features. Both supervised and unsupervised methods are used to identify regions of text or bar code in the document images. The performance of the segmentation and classification scheme for a variety of document images demonstrates the generality and effectiveness of the approach.< >

关键词： Gabor filters image segmentation Pixel Frequency image recognition image processing Laboratories Computer science Application software Document image processing

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Road Condition Detection and Crowdsourced Data Collection for Accident Prevention: A Deep Learning Approach

Road Condition Detection and Crowdsourced Data Collection fo...

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Workshops on image processing Theory, Tools and Applications, IPTA

作者： Md Saroar Jahan Mominul Islam Md Sanjid Hossain Jhuma Kabir Mim Mourad Oussalah Nasrin Akter Centre for Machine Vision and Signal Processing (CMVS) Faculty of ITEE University of Oulu Oulu Finland Dept. of CSE Daffodil International University Dhaka Bangladesh Computer Vision and Pattern Recognition LUT Univerity Lappeenranta Finland CMVS Faculty of ITEE University of Oulu Oulu Finland

Bangladesh is one of the countries struggling to prevent road accidents, which is a global cause for concern. An early warning system that indicates road conditions can contribute to the prevention task. For this purpose, a deep-learning based approach using a Convolutional Neural Network (CNN) to learn from random road images the safety factor is developed. This results in a three-class categorization: (i) Severely risky roads, (ii) Mildly risky roads, and (iii) Normal roads. The application of deep learning techniques in this study yields an accuracy of 95.5% in detecting problematic road conditions. Furthermore, based on the study’s findings, a mobile application has been developed. The app enables real-time crowdsourced data collection of road conditions and provides a platform for users to share this information in real-time with other drivers, thereby, contributing to prevent accidents and raise awareness among drivers and users by pinpointing the location of the risky road. Finally, crowdsourced data has been reused to update the trained model, which further improves the classifier accuracy.

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A Parallel-based Lifting Algorithm and VLSI Architecture for DWT

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Journal of Electronics(China) 2006年第2期23卷 244-248页

作者： Xiong Chengyi Tian Jinwen Liu Jian Gao Zhirong The State Key Lab of Education Commission for Image Processing and Intelligent Control Institute of Pattern Recognition ＆ Artificial Intelligence Huazhong Univ. of Science ＆ Tech. Wuhan 430074 China College of Electronic Info. Eng. South- Center Univ. for Nationalities Wuhan 430074 China Dept of Computer Science Wuhan Univ. of Science ＆ Eng. Wuhan 430074 China

A novel Parallel-Based Lifting Algorithm （PBLA） for Discrete Wavelet Transform （DWT）, exploiting the parallelism of arithmetic operations in all lifting steps, is proposed in this paper. It leads to reduce the critical path latency of computation, and to reduce the complexity of hardware implementation as well. The detailed derivation on the proposed algorithm, as well as the resulting Very Large Scale Integration （VLSI） architecture, is introduced, taking the 9/7 DWT as an example but without loss of generality. In comparison with the Conventional Lifting Algorithm Based Implementation （CLABI）, the critical path latency of the proposed architecture is reduced by more than half from （4Tm ＋ 8Ta）to Tm ＋ 4Ta, and is competitive to that of Convolution-Based Implementation （CBI）, but the new implementation will save significantly in hardware. The experimental results demonstrate that the proposed architecture has good performance in both increasing working frequency and reducing area.

关键词： Discrete Wavelet Transform （DFT） Lifting scheme Parallel Very Large Scale Integration（VLSI）

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A Region-Based Representation of images in MARS

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Journal of VLSI Signal processing Systems for Signal, image, and Video Technology 1998年第1-2期20卷 137-150页

作者： Servetto, Sergio D. Rui, Yong Ramchandran, Kannan Huang, Thomas S. Beckman Inst. Adv. Sci. and Technol. Univ. Illinois at Urbana-Champaign Urbana IL 61801 United States Universidad Nacional de La Plata Argentina Univ. Illinois at Urbana-Champaign United States Comp. Res. Adv. Applications Group IBM Argentina Argentina Image Formation and Processing Group Beckman Institute UIUC United States Department of Computer Science UNLP Argentina Dept. of Elec. and Comp. Engineering UIUC United States Multimedia Commun. Res. Department Bell Laboratories Murray Hill NJ United States Info. Sciences Research Department AT and T Labs. Florham Park NJ United States Department of Computer Science UIUC United States Southeast University China Tsinghua University China University of Illinois Urbana-Champaign IL United States Image Formation and Processing Group Beckman Inst. Advance Sci. Technol. UIUC United States Vis. Technol. Grp. of Microsoft Res. Redmond WA United States City College of New York United States Columbia University United States AT and T Bell Labs. United States Ctr. for Telecommunications Research Columbia University United States Elec. and Comp. Eng. Department United States Beckman Institute Coordinated Science Laboratory IL United States IEEE Signal Processing Society United States IEEE IMDSP Technical Committee United States IEEE Transactions on Image Proc. United States National Taiwan University Taipei Taiwan Massachusetts Inst. of Technology Cambridge MA United States Department of Electrical Engineering MIT United States School of Electrical Engineering United States Lab. for Info. and Signal Processing Purdue University United States Dept. of Elec. and Comp. Engineering United States Coordinated Science Laboratory United States Image Formation and Processing Group Beckman Inst. Adv. Sci. and Technol. United States MIT Lincoln Laboratory IBM Thomas J. Watson Research Center Rheinishes Landes Museum Bonn Germany Swiss Institutes of Technology Zurich Switzerland Swiss Institutes of Technology Lausanne S

We study the problem of representing images within a multimedia Database Management System (DBMS), in order to support fast retrieval operations without compromising storage efficiency. To achieve this goal, we propose new image coding techniques which combine a wavelet representation, embedded coding of the wavelet coefficients, and segmentation of image-domain regions in the wavelet domain. A bitstream is generated in which each image region is encoded independently of other regions, without having to explicitly store information describing the regions. Simulation results show that our proposed algorithms achieve coding performance which compares favorably, both perceptually and objectively, to that achieved using state-of-the-art image/video coding techniques while additionally providing region-based support.

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