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检索条件"任意字段=2nd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2013"

共 144 条记录，以下是41-50 订阅

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Coronary Artery Segmentation in Angiograms with pattern recognition Techniques - A survey

Coronary Artery Segmentation in Angiograms with Pattern Reco...

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2nd international conference on Advanced Computer Science applications and Technologies (ACSAT)

作者： Tayebi, Rohollah Moosavi Sulaiman, Puteri Suhaiza Binti Wirza, Rahmita Dimon, Mohd Zamrin Kadiman, Suhaini Abdullah, Lilly Nurliyana Binti Mazaheri, Samaneh Univ Putra Malaysia Fac Comp Sci & Informat Technol Serdang 43400 Malaysia Islamic Azad Univ Fac Comp Engn Shahr E Qods Branch Karaj Iran Univ Teknol MARA Fac Med Surg Cluster Cardiothorac Unit Shah Alam Malaysia IJN Natl Heart Inst Shah Alam Malaysia

ISBN: (纸本)9781479927586

Medical image processing is nowadays one of the best tools to make an informative model from a raw image of each part of the body, and segmentation is the most important step in which used to extract significant features. Coronary artery segmentation algorithm in angiograms is a fundamental component of each cardiac image processing system. There are lots of techniques and algorithms proposed for extracting coronary arteries in angiograms. But based on our knowledge, there is not any review paper to categorize and compare them together. In this paper, we have divided these algorithms into five major classes and propose a survey for the main class, pattern recognition, which is a famous technique in this manner. We studied all the papers in the pattern recognition class and defined six categories for them: (1) Multi scale approaches (2) Region growing approaches (3) Matching filters approaches (4) Mathematical morphology approaches (5) Skeleton based approaches and (6) Ridge based approaches. Finally, we made a table to compare all the algorithms in each category against criteria such as: user interaction, angiography types, dimensionality, enhancement method, full coronary artery output, whole tree output, and 3D reconstruction ability.

关键词： Medical image processing Image segmentation Coronary artery segmentation Angiogram pattern recognition Survey

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Improved Fingerprint recognition Algorithm Application Study on Smart Home

Improved Fingerprint Recognition Algorithm Application Study...

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2nd international conference on Energy and Environmental Protection (ICEEP 2013)

作者： Chen, Shuqian Fu, Yanglie Yin, Mingyang Huaihai Inst Technol Lianyungang Jiangsu Peoples R China

ISBN: (纸本)9783037857441

Study on a new type of fingerprint identification algorithm and its application in intelligent home electric control lock problem. The traditional fingerprint recognition algorithms on fingerprint minutiae matching accuracy is low, difficult to accurately extract details, leading to lock malfunction or could not be opened. In order to overcome this problem, improved Point pattern fingerprint recognition matching algorithm, joined the matching weight coefficient on the base of pattern matching algorithm, and gives the hardware structure of fingerprint identification system, the improved algorithm is successfully applied in smart home applications, the example shows that, the improved algorithm can effectively improve the recognition rate, reduce false positives, has a certain practical value.

关键词： fingerprint identification smart home pattern matching algorithm CC2530

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Compacting Large and Loose Communities

Compacting Large and Loose Communities

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2nd IAPR Asian conference on pattern recognition (ACPR)

作者： Chandrashekar, V Kumar, Shailesh Jawahar, C. V. IIIT Hyderabad Hyderabad Andhra Pradesh India Google Inc Hyderabad Andhra Pradesh India

ISBN: (纸本)9781479921904

Detecting compact overlapping communities in large networks is an important pattern recognition problem with applications in many domains. Most community detection algorithms trade-off between community sizes, their compactness and the scalability of finding communities. Clique Percolation Method (CPM) [I] and Local Fitness Maximization (LFM) [2] are two prominent and commonly used overlapping community detection methods that scale with large networks. However, significant number of communities found by them are large, noisy, and loose. In this paper, we propose a general algorithm that takes such large and loose communities generated by any method and refines them into compact communities in a systematic fashion. We define a new measure of community-ness based on eigenvector centrality, identify loose communities using this measure and propose an algorithm for partitioning such loose communities into compact communities. We refine the communities found by CPM and LFM using our method and show their effectiveness compared to the original communities in a recommendation engine task.

关键词： Community Detection Concept Discovery Graph Mining Recommendation Engine LFM Communities Critical path method pattern recognition problem Detection algorithms COMPACTING LARGE gene Compact Compact pattern

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Application of Fuzzy c-Means Clustering for Polymer Data Mining for Making SAW Electronic Nose

Application of Fuzzy <i>c</i>-Means Clustering for Polymer D...

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2nd international conference on Frontiers in Intelligent Computing - Theory and applications (FICTA)

作者： Verma, Prabha Yadava, R. D. S. Banaras Hindu Univ Fac Sci Dept Phys Sensor & Signal Proc Lab Varanasi 221005 Uttar Pradesh India

ISBN: (纸本)9783319029313;9783319029306

Polymers provide chemical interfaces to electronic nose sensors for detection of volatile organic compounds. An electronic nose combines a properly selected set of sensors (array) with pattern recognition methods for generating information rich odor response patterns and extracting chemical identities. Each sensor in the array is functionalized by a different polymer for diversely selective sorption of target chemical analytes. Selection of an optimal set of polymers from a long list of potential polymers is crucial for cost-effective high performance development of electronic noses. In this paper we present an application of fuzzy c-means clustering on partition coefficient data available for target vapors and most prospective polymers for selection of minimum number of polymers that can create maximum discrimination between target chemical compounds. The selection method has been validated by simulating a polymer coated surface acoustic wave (SAW) sensor array for monitoring fish freshness.

关键词： Fuzzy c-means clustering polymer selection SAW electronic nose fish quality monitoring pattern recognition

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Preprocessing for local bianry pattern based face recognition

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2nd international conference on Opto-Electronics Engineering and Materials Research, OEMR 2013

作者： Xing, Ning Gui, Lan Xian School of Information Engineering East China Institute of Technology Nanchang 330013 China School of Mechanical and Electronic Engineering East China University of Technology Nanchang 330013 China

ISBN: (纸本)9783037857731

This paper studies the pre-processing methods on Local Binary pattern (LBP) for face recognition. Three methods are well investigated, such as Gaussian smoothing, histogram equalization and Sobel operator. The extensive experiments on FERET database show that the best recognition rate increase is achieved by combination of histogram equalization, Gaussian smoothing, and Sobel with LBP. The conclusion achieved in the paper is useful for real face recognition system. © (2013) Trans Tech Publications, Switzerland.

关键词： Local binary pattern

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D-pattern Evolving and Inner pattern Evolving for High Performance Text Mining

D-Pattern Evolving and Inner Pattern Evolving for High Perfo...

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2nd international conference on Frontiers in Intelligent Computing - Theory and applications (FICTA)

作者： Vignani, B. Satapathy, Suresh Chandra Anil Neerukonda Inst Technol & Sci Dept CSE Visakhapatnam Andhra Pradesh India

ISBN: (纸本)9783319029313;9783319029306

Many data mining techniques have been introduced to perform different information tasks to mine useful patterns in text documents. However, the way to use effectively and update discovered patterns is still a research issue, particularly within the domain of text mining. Text mining methods adopt term based approach and phrase based approach. Phrase based approach performs better than the term based as phrases carry more information. In this paper we have tendency to propose a new methodology to enhance the utilization of the effectively discovered patterns by including the process of D-pattern evolving and inner pattern evolving.

关键词： text mining pattern mining pattern deploying pattern evolving

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Research on Fuzzy pattern recognition based on L-series of Formation

Research on Fuzzy Pattern Recognition based on L-series of F...

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2nd international conference on Information Technology and Management Innovation (ICITMI 2013)

作者： Li, Jinhong Zhao, Kangpei Qilu Univ Technol Fac Sci Jinan 250353 Peoples R China Shandong Jiaotong Univ Coll Mech Engg Jinan 250353 Peoples R China

ISBN: (纸本)9783037858646

In this paper, the model base and objects to be identified are classed as information granules by the information granulation idea. A proper system of polar coordinates is established. The fuzzy pattern recognition problem is studied by the L-series of formation in the abstract analytic number theory. A method is given to evaluate the model base type of the information granules, which consist of massive data. Additionally, it is used to analyze the relation between process parameters and weld appearance in the welding process.

关键词： Fuzzy pattern recognition L-series of formation Information granules

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Trajectory pattern Mining: methods and applications

Trajectory Pattern Mining: Methods and Applications

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2nd international conference on Mechanical Design and Power Engineering (ICMDPE 2013)

作者： Huang, Xin Chen, Huijuan Zheng, Maogong Liu, Ping Qian, Jing Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China

ISBN: (纸本)9783038350019

With the advent of location-based social media and locationacquisition technologies, trajectory data are becoming more and more ubiquitous in the real world. A lot of data mining algorithms have been successfully applied to trajectory data sets. Trajectory pattern mining has received a lot of attention in recent years. In this paper, we review the most inuential methods as well as typical applications within the context of trajectory pattern mining.

关键词： Spatio-Temporal Database Data Mining

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A Survey on SARS-COV-2 (COVID-19) using Machine Learning Techniques 2

A Survey on SARS-COV-2 (COVID-19) using Machine Learning Tec...

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2nd international conference on Smart Electronics and Communication, ICOSEC 2021

作者： Mary, L. William Albert Antony Raj, S. SRM Institute of Science and Technology Department of Computer Applications Chennai Kattankulathur India

ISBN: (纸本)9781665433686

SARS-CoV-2, widely known as (COVID-19), is a deadly contagious disease globally. The new suspicious virus is spreading quickly. Several studies have found that SARS and COVID-19 have comparable patterns of an inflammatory outbreak. The Coronavirus causes a severe respiratory disease that affects lung functions, and the transmission can occur through direct, close, or indirect contact from infected secretions or droplets. The typical symptom includes fever, persistent cough, cold, shortness of breath, pneumonia, decreased sense of smell, insomnia, brain fog, and many organ dysfunctions. Antibody tests and RT-PCR tests are used to diagnose fatal diseases. This literature review examines the occurrence and pathogenicity of COVID-19 infection along with radiological images, machine learning classifiers, feature extraction methods and disease prediction algorithms, and pattern recognition. This overview also summarizes the ML and DL algorithms and tools used for analysis. © 2021 IEEE.

关键词： Coronavirus

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Analysis of Correlation Structure of Data Set for Efficient pattern Classification 2

Analysis of Correlation Structure of Data Set for Efficient ...

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IEEE 2nd international conference on Cybernetics (CYBCONF)

作者： Goswami, Saptarsi Chakrabarti, Amlan Chakraborty, Basabi Inst Engn & Management Kolkata India Univ Calcutta AK Choudhury Sch Informat Technol Kolkata India Iwate Prefectural Univ Fac Software & Informat Sci Takizawa Japan

ISBN: (纸本)9781479983223

pattern classification or clustering plays important role in a wide variety of applications in different areas like psychology and other social sciences, biology and medical sciences, pattern recognition and data mining. A lot of algorithms for supervised or unsupervised classification have been developed so far in order to achieve high classification accuracy with lower computational cost. However, some methods or algorithms work well for some of the data sets and perform poorly on others. For any particular data set, it is difficult to find out the most suitable algorithm without some random trial and error process. It seems that the characteristics of the data set might have some influence on the algorithm for classification. In this work, the data set characteristics is studied in terms of intra attribute relationship and a measure MVS (multivariate score) has been proposed to quantify and group different data sets on the basis of the correlation structure into strong independent, weak independent, weak correlated and strong correlated data set. The performance of different feature selection algorithms on different groups of data are studied by simulation experiments with 63 publicly available bench mark data sets. It has been verified that univariate methods lead to significant performance gain for strong independent data set compared to multivariate methods while multivariate methods have better performance for strong correlated data sets.

关键词： pattern classification algorithm correlation structure data set characteristics

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