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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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Extraction of complex wavelet features for iris recognition

Extraction of complex wavelet features for iris recognition

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International Conference on pattern recognition

作者： Xiaofu He Pengfei Shi Department of Computer Science and Technology East China Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Shandong Jiaotong University Shanghai China

ISBN: (纸本)9781424421749

This paper presents a new feature extraction method for iris recognition. Since two dimensional complex wavelet transform (2D-CWT) does not only keep wavelet transformpsilas properties of multiresolution decomposition analysis and perfect reconstruction, but also adds its new merits: approximate shift invariance, good directional selectivity for 2-D image, and limited redundancy, which are useful for iris feature extraction. So, a set of high frequency 2D-CWT coefficients are selected as features for iris recognition. The phase information of the coefficients is used for feature encoding and Hamming distance is adopted for classification. Experimental results show that the proposed algorithm can get good recognition rate.

关键词： Iris recognition Feature extraction Wavelet analysis Wavelet transforms image resolution image analysis image reconstruction Two dimensional displays Frequency Encoding

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Sampling step importance in hierarchical semantic segmentation of microscopic images

Sampling step importance in hierarchical semantic segmentati...

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International Conference on System Theory, Control, and Computing (ICSTCC)

作者： Cristian Smochina Vasile Manta Walter Kropatsch Department of Computer Science and Engineering Gheorghe Asachi Technical University of Iasi Romania Pattern Recognition and Image Processing Group Institute of Computer Graphics and Algorithms Vienna University of Technology Austria

The objective of semantic segmentation in microscopic images is to extract the cellular, nuclear or tissue components. This problem is challenging due to the large variations of these components features (size, shape, orientation or texture). In this paper we improve the technique presented in [17] used to identify the epithelial nuclei (crypt) against interstitial nuclei in microscopic images taken from colon tissues. In the proposed enhanced approach, the crypt inner boundary is detected using the closing morphological pyramid instead of morphological hierarchy. The outer crypt border is determined by the epithelial nuclei, overlapped by the maximal isoline of the inner boundary. The use of sampling in building the pyramid offers computational efficiency, reduces the amount of used memory, increase the robustness and preserve the quality results. An analysis of the two approaches is performed considering the number of pixels processed to create each level. Also the relation between the levels of the hierarchical structures is established.

关键词： Cryptography image segmentation image reconstruction image resolution Microscopy Buildings Shape

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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...

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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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SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical image Segmentation 1

SliceProp: A Slice-Wise Bidirectional Propagation Model for ...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Xu, Xin Lu, Wenjing Lei, Jiahao Qiu, Peng Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Department of Vascular Surgery China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9798350358780

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual labeling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise Label Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods. © 2023 IEEE.

关键词： Medical imaging

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Face recognition System Using Ant Colony Optimization-Based Selected Features

Face Recognition System Using Ant Colony Optimization-Based ...

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IEEE Symposium on Computational Intelligence in Security and Defense Applications, CISDA

作者： Hamidreza Rashidy Kanan Karim Faez Mehdi Hosseinzadeh Image Processing and Pattern Recognition Laboratory Electrical Engineering Department Tehran Polytechnic Amirkabir University of Technology슠 Tehran Iran Department of Computer Engineering University of Isfahan Isfahan Iran

Feature selection (FS) is a most important step which can affect the performance of pattern recognition system. This paper presents a novel feature selection method that is based on ant colony optimization (ACO). ACO algorithm is inspired of ant's social behavior in their search for the shortest paths to food sources. In the proposed algorithm, classifier performance and the length of selected feature vector are adopted as heuristic information for ACO. So, we can select the optimal feature subset without the priori knowledge of features. Simulation results on face recognition system and ORL database show the superiority of the proposed algorithm

关键词： Face recognition Ant colony optimization Discrete wavelet transforms pattern recognition Artificial intelligence Particle swarm optimization Computational intelligence computer security Application software image processing

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Face recognition Research Based on Anti-Symmetrical Wavelet and Eigenface

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

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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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Editorial: Knowledge engineering, semantics, and signal processing in audio - visual information retrieval

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IEEE Transactions on Circuits and Systems for Video Technology 2007年第3期17卷 257-258页

作者： Izquierdo, Ebroul Zhang, Jian Sikora, Thomas Huang, Thomas S. Department of Electrical Engineering Queen Mary University of London London El 4NS United Kingdom Sydney Australia School of Computer Science and Engineering University of New South Wales Australia Communication Systems Group Technical University Berlin Berlin Germany ITG University of Illinois at Urbana-Champaign United States Department of Electrical and Computer Engineering Coordinated Science Laboratory United States Image Formation and Processing Group Beckman Institute for Advanced Science and Technology United States Institute's Major Research Theme Human Computer Intelligent Interaction National Academy of Engineering Chinese Academies of Engineering and Sciences China International Association of Pattern Recognition Optical Society of American United States

No abstract available

关键词： Special issues and sections Knowledge engineering Information retrieval Audio-visual systems

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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...

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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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Publishing rdf from relational database based on d2R improvement

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WSEAS Transactions on Information science and Applications 2013年第8期10卷 241-248页

作者： Chen, Ying Zhao, Xiaoming Zhang, Shiqing Department of Computer Science Taizhou University 605 Dongfang Avenue Linhai Taizou Zhejiang China Institute of Image Processing and Pattern Recognition Taizhou University 605 Dongfang Avenue Linhai Taizou Zhejiang China

As a key technology to implement Semantic Web, linked data have gradually been an academic and industrial concern. Linked data represents a practice of technologies on the web and linked structure data. The goal of linked data is to enable people to share structured data on the web as easily as they can share documents today. On the Web of data structured with linked data, users can jump from one dataset to another. Compared with the Web of document which enables one to jump from one document to another, data Web provides much more links and has semantics. Mapping relational databases to RDF is a fundamental problem for the development of linked data. This paper firstly makes an improvement with R2RML to open source software named D2R which can publish linked data, and then takes an example of the data concerning a college enrollment in Sichuan of China in 2011, demonstrating that we can publish the data in RDB into RDF as linked data through the new approach more efficient and make it browsable.

关键词： Linked data

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