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检索条件"机构=Key Lab. of Intelligent Computing and Signal Processing"

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A consistent pixel-wise blur measure for partially blurred images

A consistent pixel-wise blur measure for partially blurred i...

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作者： Fang, Xianyong Shen, Feng Guo, Yanwen Jacquemin, Christian Zhou, Jian Huang, Shanchun Key Lab. of Intelligent Computing and Signal Processing Anhui University Hefei230601 China State Key Lab. for Novel Software Technology Nanjing University Nanjing210023 China AMI LIMSI-CNRS Orsay91403 France

ISBN: (纸本)9781479957514

Despite numerous efforts on blur measurement of partially blurred images, there still lacks an effective blur measure that is both pixel-wise and locally sharp consistent. The paper proposes a novel method with two contributions to overcome this limitation: 1) A new pixel-based blur metric, Multi-resolution Singular Value (MSV), which leverages the average singular value of high frequency bands to measure the blur of each pixel, and 2) a locally continuous strategy, maximum-likelihood estimation (MLE) based refinement, that ensures local continuity by imposing the local sharp consistency on pixel blur in a local correcting process. Experimental results show that our method is effective to smoothly measure the partially blurred images without local discontinuity. © 2014 IEEE.

关键词： Pixels

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Teaching reform and innovation of the course - Digital image processing experiments

Teaching reform and innovation of the course - Digital image...

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International Conference on Computer Science and Education

作者： Zhao, Haifeng Tang, Jin Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei 230039 China

ISBN: (纸本)9781424460052

Digital image processing is an interdisciplinary course, which needs students have a strong background in mathematics. To help them change from passive learning to active learning, teaching reform and innovation of the course "Digital Image processing Experiments" is discussed in this paper. The combined platform of experiments includes TI DSP experimental box and three different kinds of programming languages. The six experimental projects cover the basic theories of digital image processing. The result can offer a significant reference for the teaching innovations in the other related specialties. ©2010 IEEE.

关键词： MATlab

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The implementation of the web mining based on XML technology

The implementation of the web mining based on XML technology

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2009 International Conference on Computational Intelligence and Security, CIS 2009

作者： Zheng, Cheng Fang, Yong Shen, Yaping Key Lab. of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei 230039 China School of Computer Science and Technology Anhui University Hefei 230039 China

ISBN: (纸本)9780769539317

The rapid development of the Web technology makes the Web mining become the focus of the current data mining, and the XML technology also becomes the standard of the data exchange on the Web. The paper introduces the Web mining technology, implements a frame of the Web mining based on the XML technology according to the advantage of XML in the data description, describes the implementation process of specific Web mining, and puts forward a promoting scheme on solving XML documents with VTD which solves the difficult mining problem on the Web caused by the most of the non-structure information. © 2009 IEEE.

关键词： XML

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Segmentation of brain MR images based on t-mixture model

Segmentation of brain MR images based on t-mixture model

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3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009

作者： Haifeng, Zhao Xingming, Xu Sibao, Chen Bin, Luo Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei 230039 China

ISBN: (纸本)9781424429028

As Magnetic Resonance Imaging (MRI) is an important technology of radiological evaluation and computeraided diagnosis, the accuracy of the MR image segmentation directly influences the validity of following processing. The paper concerns medical image segmentation based on t-mixture model because of merits of the model. By analyzing the features of MR images, the main procedure of white matter segmentation of brain MR Images based on t-mixture model is outlined follows. The parameters of t-mixture model for the image are firstly estimated. Then the posterior probabilities of the pixels of the image are computed. At last, the image is segmented according to the Bayes decision rule for minimum error. Experimental results show that t-mixture model fits for medical image segmentation. ©2009 IEEE.

关键词： Magnetic resonance imaging

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Improving Transferability Reversible Adversarial Examples Based on Flipping Transformation 9th

Improving Transferability Reversible Adversarial Examples Ba...

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9th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023

作者： Fang, Youqing Jia, Jingwen Yang, Yuhai Lyu, Wanli Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China

ISBN: (纸本)9789819959679

Adding subtle perturbations to an image can cause the classification model to misclassify, and such images are called adversarial examples. Adversarial examples threaten the safe use of deep neural networks, but when combined with reversible data hiding (RDH) technology, they can protect images from being correctly identified by unauthorized models and recover the image lossless under authorized models. Based on this, the reversible adversarial example (RAE) is rising. However, existing RAE technology focuses on feasibility, attack success rate and image quality, but ignores transferability and time complexity. In this paper, we optimize the data hiding structure and combine data augmentation technology, which flips the input image in probability to avoid overfitting phenomenon on the dataset. On the premise of maintaining a high success rate of white-box attacks and the image’s visual quality, the proposed method improves the transferability of reversible adversarial examples by approximately 16% and reduces the computational cost by approximately 43% compared to the state-of-the-art method. In addition, the appropriate flip probability can be selected for different application scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Reversible Data Hiding for 3D Mesh Model Based on Block Modulus Encryption and Multi-MSB Prediction 10th

Reversible Data Hiding for 3D Mesh Model Based on Block Modu...

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10th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2024

作者： Fu, Zihao Gao, Yang Lyu, Wanli Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China

ISBN: (纸本)9789819787456

Reversible data hiding in encrypted domain (RDH-ED) fortifies data security and privacy safeguards while upholding the original data’s integrity and accessibility. Current research on RDH-ED focuses on 2D images, while research on 3D mesh models is still immature. This paper introduces an RDH-ED method using block modulus encryption and multi-MSB prediction. Initially, the original mesh model is partitioned into non-overlapping subblocks of equal size, and then the vertices in each subblock are encrypted with the same key for additive modulus encryption, ensuring that the spatial correlation present in the original mesh subblocks remains preserved within the encrypted subblocks. Subsequently, the subblocks are disrupted one by one using the 3D Arnold Transform to enhance security. The vertices in each embeddable subblock are divided into a reference vertex and several embeddable vertices, where the multi-MSB prediction strategy is employed to allocate embedding room for each embeddable vertex. Finally, the secret information is embedded into the allocated room. Since the proposed method almost completely preserves the spatial correlation within each subblock, the achieved embedding rate surpasses that of all previous outstanding methods that rely on vacating room after encryption (VRAE). The experimental findings demonstrate that the proposed approach achieves an average embedding rate of 45.55 bits per vertex (bpv), surpassing the state-of-the-art method that achieves 25.63 bpv. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Mesh generation

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A VQ digital watermark algorithm based on T-mixture models segmentation

A VQ digital watermark algorithm based on T-mixture models s...

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2008 IEEE International Conference Neural Networks and signal processing, ICNNSP

作者： Bin, Luo Wei, Gu Hui, Guo Key Lab. of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei Anhui 230039 China University of New South Wales Sydney NSW 2052 Australia

ISBN: (纸本)9781424423118

To improve codebook quality in the process of vector quantization, the paper proposes a novel codebook generation algorithm which is based on image segmentation using t-mixture models and greedy EM algorithm. Additionally, in the initial codebook generating procedure, the PNN algorithm is used to reduce LBG algorithm's sensitivity to initial codebook. Experimental results show that the proposed algorithm not only significantly improves codebook quality, but also preserves most details of the original image. An even more important feature is that the proposed algorithm is robust to common image processing operations such as cropping, sharpening and filtering. © 2008 IEEE.

关键词： Image segmentation

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Adversarial Example Generation Method Based on Probability Histogram Equalization 42

Adversarial Example Generation Method Based on Probability H...

引用

42nd Chinese Control Conference, CCC 2023

作者： Fang, Youqing Jia, Jingwen Yang, Yuhai Lyu, Wan-Li School of Computer Science and Technology Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei230601 China

ISBN: (纸本)9789887581543

CNNs (Convolutional Neural Networks) have a good performance on most classification tasks, but they are vulnerable when meeting adversarial examples. Research and design of highly aggressive adversarial examples can help enhance the security and robustness of CNNs. The transferability of adversarial examples is still low in black-box settings. Therefore, an adversarial example method based on probability histogram equalization, namely HE-MI-FGSM (Histogram Equalization Momentum Iterative Fast Gradient Sign Method) is proposed. In each iteration of the adversarial example generation process, the original input image is randomly histogram equalized, and then the gradient is calculated to generate adversarial perturbations to mitigate overfitting in the adversarial example. The effectiveness of the method is verified on the ImageNet dataset. Compared with the advanced method I-FGSM (Iterative Fast Gradient Sign Method) and MI-FGSM (Momentum I-FGSM), the attack success rate in the adversarial training network increased by 27.9% and 7.7% on average, respectively. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Iterative methods

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Semi-supervised geometric mean of Kullback-Leibler divergences for subspace selection

Semi-supervised geometric mean of Kullback-Leibler divergenc...

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2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11

作者： Chen, Si-Bao Wang, Hai-Xian Zhang, Xing-Yi Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei 230039 China

ISBN: (纸本)9781612841816

Subspace selection is widely adopted in many areas of pattern recognition. A recent result, named maximizing the geometric mean of Kullback-Leibler (KL) divergences of class pairs (MGMD), is a successful method for subspace selection, which can significantly reduce the class separation problem. However, in many applications, lab.led data are very limited while unlab.led data can be easily obtained. The estimation of divergences of class pairs is unstable using inadequate lab.led data. To take advantage of unlab.led data for subspace selection, semi-supervised MGMD (SSMGMD) is proposed using graph Laplacian as normalization. Quasi-Newton method is adopted to solve the optimization problem. Experiments on synthetic data and real image data show the validity of SSMGMD. © 2011 IEEE.

关键词： Newton-Raphson method

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An epipolar resticted inter-mode selection for stereoscopic video encoding

An epipolar resticted inter-mode selection for stereoscopic ...

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28th Picture Coding Symposium, PCS 2010

作者： Yang, Guolei Liang, Luhong Gao, Wen Peking University China Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9781424471348

Fast stereoscopic video encoding becomes a highly desired technique because the stereoscopic video has been realizable for applications like TV broadcasting and consumer electronics. The stereoscopic video has high inter-view dependency subject to epipolar restriction, which can be used to reduce the encoding complexity. In this paper, we propose a fast inter-prediction mode selection algorithm for stereoscopic video encoding. Different from methods using disparity estimation, candidate modes are generated by sliding a window along the macro-block line restricted by the epipolar. Then the motion information is utilized to rectify the candidate modes. A selection failure handling algorithm is also proposed to preserve coding quality. The proposed algorithm is evaluated using independent H.264/AVC encoders for left and right views and can be extended to MVC. Experimental results show that encoding times of one view are reduced by 41.4% and 24.4% for HD and VGA videos respectively with little quality loss. © 2010 IEEE.

关键词： Encoding (symbols)

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