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Iterative data adaptive anisotropic image filtering

Iterative data adaptive anisotropic image filtering

引用

2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012

作者： Yang, Chang-Cai Zheng, Xin-Yi Tian, Jin-Wen Shang, Ke Tian, Xin Hao, Wei Institute for Pattern Recognition and Artificial Intelligence Science and Technology on Multi-Spectral Information Processing Laboratory Huazhong University of Science and Technology Wuhan 430074 China Electrical Engineering and Renewable Energy School China Three Gorges University Yichang 443002 China Institute of Intelligent Vision and Image Information College of Computer and Information Technology China Three Gorges University Yichang 443002 China

ISBN: (纸本)9781467314855

This paper presents a novel data-adaptive anisotropic filtering technique built on top of an iterative scheme. This new technique can preserve the original significant structures while suppressing noises to the largest extent. To achieve this goal, dominant orientation information of all gradients is used to control the local kernel adaptively. This results in elongated and elliptical contours spread along the directions of the local edge structure. With these locally adapted kernels, the smoothing is mostly effective along the edges, rather than across them. Therefore details in the original input can be preserved. The performance of the proposed data-adaptive anisotropic filter together with its iterative scheme are compared with the conventional Gaussian filter, anisotropic diffusion filter, the structure-adaptive anisotropic filter, and the iterative kernel regression scheme. The experimental results on both simulated Gaussian and Poission noisy images indicate that the proposed method performs the best, and the iterative scheme can significantly improve the performance of the noise suppressing while delivering high computation efficiency. © 2012 IEEE.

关键词： Iterative methods

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Mobile robot localization under varying illumination

Mobile robot localization under varying illumination

引用

16th International Conference on pattern recognition, ICPR 2002

作者： Jogan, Matjaž Leonardis, Aleš Wildenauer, Horst Bischof, Horst Faculty of Computer and Information Science University of Ljubljana Tržaška 25 Ljubljana1001 Slovenia Pattern Recognition and Image Processing Group Institute for Computer Aided Automation Vienna University of Technology Favoritenstrasse 9/1832 ViennaA-1040 Austria Institute for Computer Graphics and Vision Graz University of Technology Inffeldgasse 16 2. OG GrazA-8010 Austria

ISBN: (纸本)0769516963

Methods for mobile robot localization that use eigen spaces of panoramic snapshots of the environment are in general sensitive to changes in the illumination of the environment. Therefore, we propose an approach which achieves a reliable localization under severe illumination conditions. The method uses gradient filtering of the eigen space. After testing the approach on images obtained by a mobile robot, we show that it outperforms the standard eigen space-based recognition method. © 2002 IEEE.

关键词： Mobile robots

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Transmit power allocation for an extended V-BLAST system

Transmit power allocation for an extended V-BLAST system

引用

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Seung Hoon Nam Kwang Bok Lee Pattern Recognition and Image Processing Laboratory Samsung Advanced Institute of Science and Technology Suwon South Korea School of Electrical Engineering and Computer Science Seoul National University Seoul South Korea

ISBN: (纸本)0780375890

Vertical Bell Laboratories Layered Space-Time (V-BLAST) is a promising system that realizes the enormous capacity of multiple-input multiple-output (MIMO) communications. We present an extension of V-BLAST, and propose an effective transmit power allocation scheme for the extended system. The proposed transmit power allocation scheme minimizes the bit error rate (BER) averaged over all detection stages, and requires small feedback overhead from the receiver to the transmitter. Simulation results show that the extended V-BLAST system with the proposed transmit power allocation scheme provides a significant reduction in the BER compared to the conventional V-BLAST system. When the minimum mean square error (MMSE) nulling is adopted, the extended V-BLAST system is found to achieve the BER performance comparable to that of the maximum likelihood (ML) detection for the conventional V-BLAST architecture.

关键词： Bit error rate MIMO Feedback Transmitters Maximum likelihood detection Transmitting antennas Space technology computer science Mean square error methods Information theory

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Learning bronchiole-sensitive airway segmentation cnns by feature recalibration and attention distillation 23rd

Learning bronchiole-sensitive airway segmentation cnns by fe...

引用

23rd International Conference on Medical image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Qin, Yulei Zheng, Hao Gu, Yun Huang, Xiaolin Yang, Jie Wang, Lihui Zhu, Yue-Min Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province College of Computer Science and Technology Guizhou University Guiyang China UdL INSA Lyon CREATIS CNRS UMR 5220 INSERM U1206 Lyon France

ISBN: (纸本)9783030597092

Training deep convolutional neural networks (CNNs) for airway segmentation is challenging due to the sparse supervisory signals caused by severe class imbalance between long, thin airways and background. In view of the intricate pattern of tree-like airways, the segmentation model should pay extra attention to the morphology and distribution characteristics of airways. We propose a CNNs-based airway segmentation method that enjoys superior sensitivity to tenuous peripheral bronchioles. We first present a feature recalibration module to make the best use of learned features. Spatial information of features is properly integrated to retain relative priority of activated regions, which benefits the subsequent channel-wise recalibration. Then, attention distillation module is introduced to reinforce the airway-specific representation learning. High-resolution attention maps with fine airway details are passing down from late layers to previous layers iteratively to enrich context knowledge. Extensive experiments demonstrate considerable performance gain brought by the two proposed modules. Compared with state-of-the-art methods, our method extracted much more branches while maintaining competitive overall segmentation performance. © Springer Nature Switzerland AG 2020.

关键词： Distillation

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Face recognition Research Based on Anti-Symmetrical Wavelet and Eigenface

Face Recognition Research Based on Anti-Symmetrical Wavelet ...

引用

International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Qian Yin Zhi-Yong Yuan Ying Kong Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China School of Computer Science Wuhan University of China Wuhan China School of Computer Science & Technology Beijing Institute of Technology Beijing China

In this paper, a new human face recognition method based on anti-symmetrical biorthogonal wavelet transformation (ASBWT) and eigenface was proposed. First the anti-symmetrical biorthogonal wavelet is chosen to degrade the face image dimension, meanwhile complete the process of face location and segmentation; And then human face is reverted through the face space of eigenface, the traditional average human face is replaced in the within-class scatter matrix. This within-class scatter matrix is used to calculate within-class and between-class distance proportion as a rule function, calculate the twice eigenface through discrete Karhunen-Loeve transform (DKLT), and use singular value decomposition (SVD) method to calculate the eigenvector. Finally we compute the weights and classify the face images. The results show that the proposed method has higher recognition rate and more robust than the traditional eigenface analysis method.

关键词： Face recognition Scattering Matrix decomposition image coding Humans Matrices Degradation Robustness Wavelet analysis Fourier transforms

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Voronoi-Based Sleeping Configuration in Wireless Sensor Networks with Location Error

Voronoi-Based Sleeping Configuration in Wireless Sensor Netw...

引用

IEEE International Conference on Networking, Sensing and Control

作者： Xinyu Chen Michael R. Lyu Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China Department of Computer Science and Engineering Chinese University of Hong Kong Sha Tin Hong Kong China

In wireless sensor networks, to obtain a long network lifetime is a fundamental issue while without sacrificing crucial aspects of quality of service (area coverage, sensing reliability, and network connectivity). In this paper, we present a Voronoi-based sleeping configuration to deal with different sensing radii and location error. With our proposed sleeping candidate condition, redundant sensors are optionally identified and scheduled to sleep in order to extend the system lifetime while maintaining adequate sensor redundancy to tolerate sensor failures, energy depletions, and location error. Simulation results show that there is a tradeoff among energy conservation, area coverage, and fault tolerance, which varies between different sleeping candidate conditions.

关键词： Wireless sensor networks Sensor systems Surveillance Quality of service Redundancy Energy conservation Fault tolerance Sensor phenomena and characterization Batteries image processing

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A Pseudoinverse Incremental Algorithm for Fast Training Deep Neural Networks with Application to Spectra pattern recognition

A Pseudoinverse Incremental Algorithm for Fast Training Deep...

引用

International Joint Conference on Neural Networks

作者： Ke Wang Ping Guo Qian Yin A-Li Luo Xin Xin School of Computer Science and Technology Beijing Institute of Technology Image Processing and Pattern Recognition Laboratory Beijing Normal University Key Laboratory of Optical Astronomy National Astronomical Observatories Chinese Academy of Sciences

ISBN: (纸本)9781509006212

Deep learning scheme has received significant attention during these years, particularly as a way of building hierarchical representations from unlabeled data for a variety of signal and information processing tasks. However, deep neural networks suffer from slow learning speed since most used training algorithms are based on variations of the gradient descent algorithms which require iterative optimization and thus are time-consuming. In addition, a series of control parameters need to be specified empirically which lacks of the theoretical guidance, and current learning algorithms for deep networks are not very suitable to incremental learning scenario. To address these issues, we propose a fast learning scheme in this paper. The basic idea of our approach is to pre-train basic units such as auto-encoders of the deep architecture in an analytical way without any iterative optimization procedure. This scheme is also extended to an incremental learning version. The experimental result shows the superiority of our approach over the state-of-the-art gradient descent based algorithms. To demonstrate the impact of our algorithm on complicated real world applications, we give an example of its performance in astronomical spectra pattern recognition.

关键词： astronomical spectra Spectra pattern recognition learning (artificial intelligence) recognition (Psychology) Neural network algorithms control parameter Optimization Procedure descent Fasting Functional training Road

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NiuEM: A Nested-iterative Unsupervised Learning Model for Single-particle Cryo-EM image processing

NiuEM: A Nested-iterative Unsupervised Learning Model for Si...

引用

2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

作者： Hu, Rui Cai, Jiaming Zheng, Wangjie Yang, Yang Shen, Hong-Bin Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Jiao Tong University Department of Bioinformatics and Biostatistics Shanghai200240 China

ISBN: (纸本)9781728162157

Cryo-electron microscopy (cryo-EM) has become a mainstream technology for solving spatial structures of biomacromolecules, while the processing of cryo-EM images is a very challenging task. One of the great challenges is the high noise in the images. A common method is to cluster the images with close projecting angles to get mean images, which are used for 3D reconstruction. However, due to the extremely low signal-to-noise-ratio, common clustering methods often fail to obtain high-quality mean images, leading to poorly reconstructed structures. In this study, we present a new unsupervised learning framework, called NiuEM, to discriminate images captured from different angles and yield cluster-mean images. NiuEM first generates pseudo-labels and then exploits both contrastive loss and cross-entropy loss for training convolutional layers to learn feature representations. Moreover, the pseudo-labels are updated iteratively to enhance the reliability of labels. We assess the performance of NiuEM on four data sets via both visualized and quantitative experiments. Especially, two kinds of metrics are adopted to measure the performance, regarding the clustering quality and the resolution of reconstructed 3D models, respectively. The experimental results show that NiuEM achieves very competitive clustering accuracy in the comparison with the state-of-the-art image clustering methods. Moreover, the cluster mean images yielded by NiuEM lead to better initial 3D models compared with the mainstream reconstruction tools. © 2020 IEEE.

关键词： image reconstruction

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An efficient algorithm and implementation for residue to binary number conversion

An efficient algorithm and implementation for residue to bin...

引用

International Conference on Communications, Circuits and Systems (ICCCAS)

作者： Chengyi Xiong Zhirong Gao Jinwen Tian Key Laboratory of Education Commission for Image Processing and Intelligent Control Institute of Pattern Recognition & Artificial Intelligence Huazhong University of Science and Technology Wuhan China Department of Computer Science Wuhan University of Science & Engineering Wuhan China Key Laboratory of Education Commission for Image Processing Huazhong University of Science & Technology China

The residue number system (RNS) has computational advantages in addition and multiplication compared with weighted number systems, such as the binary number system (BNS), since operations on residue digits are performed independently and these processes can be performed in parallel. Thus they are widely used in digital signal processing etc. Since residue to binary conversion is critical and difficult for the practicality of RNS, in this paper, a novel residue to binary (R/B) conversion algorithm for the restricted moduli set (2/sup n/ -1, 2/sup n/, 2n+1), based on exploring the periodicity of modulo (2/sup n/ /spl plusmn/ 1) operations is presented. A new 2n-bit adder based R/B converter is also proposed. The performance comparison results demonstrate that the new converter is faster and requires less area compared with the others reported in the previous literature.

关键词： Signal processing algorithms image converters Educational institutions computer science Concurrent computing Digital signal processing Arithmetic Laboratories image processing Intelligent control

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An Optimizing Parameters and Feature Selection in SVM Based on Improved Cockroach Swarm Optimization 16th

An Optimizing Parameters and Feature Selection in SVM Based ...

引用

16th International Conference on Intelligent Information Hiding and Multimedia Signal processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2020

作者： Nguyen, Trong-The Yu, Jie Nguyen, Thi-Thanh-Tan Lai, Quoc-Anh Ngo, Truong-Giang Dao, Thi-Kien Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Automotive Engineering Fujian University of Technology Fuzhou China Information Technology Faculty Electric Power University Hanoi Viet Nam Department of Pattern Recognition & Image Processing Institute of Information Technology Vietnam Academy of Science and Technology Hanoi Viet Nam Faculty of Computer Science and Engineering Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam

ISBN: (纸本)9789813367562

This study improves a classifier of the support vector machine (SVM) by optimizing its parameters by adjusting cockroach swarm optimization (CSO). Classification system design includes data inputs, pre-process, and classification. The relief technique selects the feature subset, and the kernel parameters used as input for the system. The feature selection and classification of data sets were figured out by the combination of ICSO and SVM with its optimized parameters synchronous. Simulation results of the proposed scheme are compared with other systems, e.g., GA-SVM, PSO-SVM, and CSO-SVM, which show that the proposed project can achieve better classification accuracy than the different algorithms. It is promising that the proposed scheme can be applied to high-dimensional data classification, such as image classification, signal classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Support vector machines

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