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Asilomar Conference on Signals, Systems & computers

作者： Jinshan Tang Xiaoming Liu College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Image Processing and Bio-Imaging Reasearch Laboratory Systems Research Institute Department of Advanced Technologies Alcorn State University Lorman MS USA

The wavelet/scalar quantization (WSQ) [1] fingerprint image compression algorithm is effective and has been widely applied to fingerprint image compression. However, WSQ cannot control the compression ratio and its performance on fuzzy images is poor. We propose an improved WSQ fingerprint image compression algorithm to overcome the above shortcomings. Our algorithm uses a mixed quantizer so that the algorithm can treat fuzzy and non-fuzzy fingerprint images separately. In order to control the compression ration, an optimization parameters for a specific compression ratio. The proposed algorithm is compared with the traditional WSQ fingerprint image compression algorithm and the results are encouraging.

关键词： Fingerprint recognition image matching Compression algorithms

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A Functional Equation Stemming from a Characterization of Power-based Implications

A Functional Equation Stemming from a Characterization of Po...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Michał Baczyński Włodzimierz Fechner Sebastia Massanet Institute of Mathematics University of Silesia in Katowice Katowice Poland Institute of Mathematics Łódź University of Technology Łódź Poland Soft Computing Image Processing and Aggregation (SCOPIA) research group Department of Mathematics and Computer Science University of the Balearic Islands Palma Spain

The so-called family of T-power based implications has been introduced recently by using Zadeh's quantifiers modelled by powers of t-norms in its definition. Most of these operators satisfy the invariance with respect to powers of a continuous t-norm, an important property in approximate reasoning. When this family of fuzzy implication functions was characterized, the property I(x, y) · I(y, z) = I(x, z) in a concrete sub-domain played a key role. This property, which ensures that T-power based implications are unidimensional T'-preorders with T' the product t-norm, seems to be related to the invariance property. Therefore, the natural question of characterizing some classes of fuzzy implications with this property arises naturally. We provide such a characterization in a fairly general setting, generalizing earlier known results.

关键词： Generators Mathematics Cognition Additives image processing computer science Indexes

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FRSM: A Novel Fault-Tolerant Approach for Redundant-Path-Enabled Service Migration in Mobile Edge Computing 1

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29th International Conference on Web Services, ICWS 2022 Held as Part of the Services Conference Federation, SCF 2022

作者： Zhao, Jiale Dai, Mengxuan Xia, Yunni Ma, Yong He, Meibin Peng, Kai Li, Jianqi Li, Fan Fu, Xiaodong School of Computer Chongqing University Chongqing400030 China School of Computer and Information Engineering Jiangxi Normal University Nanchang330022 China Department of Big Data and AI of Jiangxi Telecom Nanchang330029 China Engineering College Huaqiao University Quanzhou362021 China Global Energy Interconnection Research Institute Co. Ltd. Beijing102209 China Key Laboratory of Fundamental Synthetic Vision Graphics and Image Science for National Defense Sichuan University Chengdu610065 China Data Science Research Center Faculty of Science Kunming University of Science and Technology Kunming650031 China

ISBN: (数字)9783031235795

ISBN: (纸本)9783031235788

Mobile Edge Computing (MEC) provides users with low-latency, highly responsive services by deploying Edge Servers (ESs) near applications. MEC allows any edge-hosted application or service to be migrated between different edge resource providers without being locked into a single provider. Nevertheless, due to its complexity and dynamics, the real edge computing environment is prone to errors and failures, reducing the reliability of edge service migration. This paper proposes a novel fault-tolerant method for redundant path service migration. The method utilizes sliding-window-based model and identifies a set of service migration paths, enabling the evaluation of the time-varying failure rate of ESs. The method combines resubmission and replication mechanisms and decides the edge service migration scheme by selecting multiple redundant migration paths. We also conduct extensive simulations and show that our proposed method outperforms traditional solutions in several metrics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mobile edge computing

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Outlier detection by weighted mercer-kernel based fuzzy clustering algorithm

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Iranian Journal of Electrical and computer Engineering 2005年第2期4卷 129-136页

作者： Shen, Hongbin Yang, Jie Dong, Yifei Wang, Shitong College of Computer and Information Engineering Hohai University Changzhou Jiangsu China Department of Image Processing and Pattern Recognition Jiaotong University Shanghai China School of Computer Science and Engineering University of New South Wales Australia School of Information Southern Yantze University Wuxi Jiangsu China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University China IEEE Institute of National Automation Department of Information Southern Yangtse University Polytechnic University of Hongkong Advanced Computing Research Center Bristol University United Kingdom

Outliers are data values that lie away from the general cluster of other data values. Detecting the outliers of a dataset is an important research topic for data cleaning and finding new useful knowledge in many research areas, i.e. data mining, pattern recognition, etc. In the past decades, many useful algorithms were proposed in the literature. In this paper, a new fuzzy kernel-clustering algorithm with outliers (FKCO) is presented to locate critical areas that are often represented by only a few outliers. Theoretic analysis also shows that FKCO can converge to a local minimum of the objective function. Finally, based on the information theory, a new criterion for finding outliers is also proposed. Simulations of different types of datasets demonstrate the feasibility of this new method. © 2005 JD.

关键词： Fuzzy control

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Efficient and effective similar shape retrieval

Efficient and effective similar shape retrieval

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IEEE International Conference on Multimedia Computing and Systems (ICMCS)

作者： Jia Wang W. Chang R. Acharya Image and Video Processing Group LG Electronics Research Center of America Inc. NJ USA Department of Computer Engineering Rochester Institute of Technology Rochester NY USA Department of Electrical and Computer Engineering State University of New York Buffalo NY USA

We present the design and implementation of a content based retrieval system which achieves efficient and effective similar shape retrieval using a modified geometric hashing technique. The system contains four main components: a feature acquisition module, a query manager, a search engine, and a hash table. The hash table houses the feature information. The query manager accepts the user query and extracts contours from the query image. The feature acquisition module transforms and quantizes the image contours and formulates the hash table entries. Given a visual query, the search agent derives a list of potentially similar shape images by searching the hash table using a majority voting algorithm. Extensive experiments have been conducted to demonstrate that the proposed designs offer a viable and practical approach to similar shape retrieval.

关键词： Shape Information retrieval image retrieval Spatial databases Content based retrieval Search engines Visual databases image databases Architecture Data mining

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Multi-Scale Recurrent Network for Single image Deraining 8

Multi-Scale Recurrent Network for Single Image Deraining

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8th International Conference on Digital Home, ICDH 2020

作者： Li, Youxing Lan, Rushi Sun, Long Li, Ji Luo, Xiaonan Pang, Cheng Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China Guilin University of Electronic Technology National Local Joint Engineering Research Center of Satellite Navigation and Location Service Guilin541004 China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin541004 China

ISBN: (纸本)9781728192345

Rain streaks noise is one of the greatest influence of outdoor scene tasks. In order to improve the effect of the outdoor scene tasks, we need to reduce the impact of the rain streaks noise while ensuring that other important details are preserved. To handle this issue, we proposed a multi-scale recurrent network (MSReNet) for single image rain removal. By divided MSReNet into two stages: (i) removal rain streaks in the first stage and (ii) restoring background-truth details in the second stage, our network can availably remove huge rain streaks and restore some important background details. Extensive experiments on both synthetic and real images demonstrated that the proposed MSReNet prominently exceeds many recent state-of-the-art methods. Taking the proposed method effectiveness, it is also attractive as a preprocessing process for some visual tasks. © 2020 IEEE.

关键词： Rain

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Deep Learning based Fault Diagnosis in a Grid-Connected Photovoltaic Systems 19

Deep Learning based Fault Diagnosis in a Grid-Connected Phot...

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19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022

作者： Hichri, Amal Hajji, Mansour Mansouri, Majdi Bouzrara, Kais Nounou, Hazem Nounou, Mohamed Higher Institute of Applied Sciences and Technology of Kasserine Kasserine Tunisia Electrical and Computer Engineering Program Texas A&m University at Qatar Doha23874 Qatar Prince Sultan University Department of Mathematics and Sciences Riyadh11586 Saudi Arabia Signal Processing and Image National Engineering School of Monastir Research Laboratory of Automation 5019 Tunisia Chemical Engineering Program Texas A&m University at Qatar Doha23874 Qatar

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

Photovoltaic (PV) system fault diagnosis is crucial because it helps PV system operators reduce energy and income losses. It also decreases the risk of fire and electric shock from PV system failures. Thus, the implementation of fault diagnosis tool is required. The most well-known techniques are machine learning models. Although these techniques provided well in terms of diagnosis and classification accuracy, they rely on manual feature extraction, which is time consuming, expensive, and requires diagnostic skill. Manual feature extraction is a problem that has to be addressed. This paper therefore presents a PV system fault diagnosis techniques that can automatically extract features from raw data in order to classify PV system faults. The implemented methods include long short-term memory (LSTM) bidirectional long short-term memory (BiLSTM) networks, which are deep learning based algorithms. Finally, the presented fault diagnosis approaches showed a good diagnosis performances when applied to an emulated GCPV system. © 2022 IEEE.

关键词： Failure analysis

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Corrigendum to “Skeleton-driven 2D distance field metamorphosis using intrinsic shape parameters” [Graphical Models 66 (2004) 102–126]

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Graphical Models 2004年第4期66卷 261-261页

作者： WuJun Che XunNian Yang GuoZhao Wang Department of Mathematics Institute of Computer Graphics and Image Processing Zhejiang University Hangzhou 310027 PR China

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Motion Analysis of Endovascular Stent-Grafts by MDL Based Registration

Motion Analysis of Endovascular Stent-Grafts by MDL Based Re...

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International Conference on computer Vision (ICCV)

作者： Georg Langs Nikos Paragios Rene Donner Pascal Desgranges Alain Rahmouni Hicham Kobeiter GALEN Group Laboratoire de Mathématiques Appliquées aux Systàmes Ecole Centrale de Paris France Institute for Computer Graphics and Vision Graz University of Technology Austria Pattern Recognition and Image Processing Group University of Technology Vienna Austria University of Paris XII Paris France

The endovascular repair of a traumatic rupture of the thoracic aorta - that would otherwise lead to the death of the patient - is performed by delivering a stent-graft into the vessel at the rupture location. The age range of the affected patients is large and the stent-graft will stay in the body for the remaining life. The technique is relatively new, and no experience with regard to long-term effects, and durability exists. To predict long-term complications, such as ruptures or destructive interactions with surrounding tissue during the life of the patient, it is important to understand the - rather intense and constant - movement of the stent- graft during the cardiac cycle. A computed tomography with heart gating (gated CT) acquires sequences that show the region of the stent-graft at different time points. We analyze the motion of stent-grafts with a model based approach. Stent-grafts are represented as sparse sets of axis points extracted from the gated CT, and motion patterns are captured by a minimum description length based group-wise registration of the stent-graft at different time points. No parameterization or a priori definition of the topology is necessary, and highly variable elasticity properties in the data volume can by accounted for by the sparse statistical model, that captures correlations and motion components of the stent-graft. We report results for deformation models and registration accuracy for 5 patients.

关键词： Motion analysis Computed tomography Biomedical imaging Heart Buildings computer graphics Pattern recognition image processing Hospitals Data mining

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Analysis of Video Quality Datasets via Design of Minimalistic Video Quality Models

arXiv

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arXiv 2023年

作者： Sun, Wei Wen, Wen Min, Xiongkuo Lan, Long Zhai, Guangtao Ma, Kede The Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science City University of Hong Kong Kowloon Hong Kong The Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China The Institute of Image Communication and Information Processing The MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science The Shenzhen Research Institute City University of Hong Kong Kowloon Hong Kong

Blind video quality assessment (BVQA) plays an indispensable role in monitoring and improving the end-users’ viewing experience in various real-world video-enabled media applications. As an experimental field, the improvements of BVQA models have been measured primarily on a few human-rated VQA datasets. Thus, it is crucial to gain a better understanding of existing VQA datasets in order to properly evaluate the current progress in BVQA. Towards this goal, we conduct a first-of-its-kind computational analysis of VQA datasets via designing minimalistic BVQA models. By minimalistic, we restrict our family of BVQA models to build only upon basic blocks: a video preprocessor (for aggressive spatiotemporal downsampling), a spatial quality analyzer, an optional temporal quality analyzer, and a quality regressor, all with the simplest possible instantiations. By comparing the quality prediction performance of different model variants on eight VQA datasets with realistic distortions, we find that nearly all datasets suffer from the easy dataset problem of varying severity, some of which even admit blind image quality assessment (BIQA) solutions. We additionally justify our claims by comparing our model generalization capabilities on these VQA datasets, and by ablating a dizzying set of BVQA design choices related to the basic building blocks. Our results cast doubt on the current progress in BVQA, and meanwhile shed light on good practices of constructing next-generation VQA datasets and models. Code is available at https://***/sunwei925/***. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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