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Computational Intelligence and Its Applications 1st ed. 2018

引用

丛书名： IFIP Advances in Information and Communication Technology

2018年

作者： Abdelmalek Amine Malek Mouhoub Otmane Ait Mohamed Bachir Djebbar

ISBN: (数字)9783319897431

ISBN: (纸本)9783319897424

this book constitutes the refereed proceedings of the 6th IFIP TC 5 international conference on Computational Intelligence and Its Applications, CIIA 2018, held in Oran, Algeria, in May 2018. the 56 full papers presented were carefully reviewed and selected from 202 submissions. they are organized in the following topical sections: data mining and information retrieval; evolutionary computation; machine learning; optimization; planning and scheduling; wireless communication and mobile computing; Internet of things (IoT) and decision support systems; pattern recognition and image processing; and semantic web services.

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CLASSIFYING SCHIZOPHRENIA BY patternS OF BOLD FLUCTUATIONS USING MULTIVARIATE pattern recognition ANALYSIS

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SCHIZOPHRENIA BULLETIN 2018年第SUPPL 1期44卷 S283-S283页

作者： Shang, Jing Sorg, Christian Baeuml, Josef G. Kambeitz, Joseph Brandl, Felix Koutsouleris, Nikolaos LMU Munich Germany

F160

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Nonlinear compressed sensing for multi-emitter X-ray imaging 11th

Nonlinear compressed sensing for multi-emitter X-ray imaging

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11th international conference on Energy Minimization Methods in Computer Vision and pattern recognition, EMMVCPR 2017

作者： Klodt, Maria Hauser, Raphael Mathematical Institute University of Oxford OxfordOX2 6GG United Kingdom

ISBN: (纸本)9783319781983

Compressed sensing is a powerful mathematical modelling tool to recover sparse signals from undersampled measurements in many applications, including medical imaging. A large body of work investigates the case with linear measurements, while compressed sensing with nonlinear measurements has been considered more recently. We continue this line of investigation by considering a novel type of nonlinearity with special structure that occurs in data acquired by multi-emitter X-ray tomosynthesis systems with spatio-temporal overlap. In [15] we proposed a nonlinear optimization model to deconvolve the overlapping measurements. In this paper we propose a model that exploits the structure of the nonlinearity and a nonlinear tomosynthesis algorithm that has a practical running time of solving only two linear subproblems at the equivalent resolution. We underpin and justify the algorithm by deriving RIP bounds for the linear subproblems and conclude with numerical experiments that validate the approach. © Springer international Publishing AG, part of Springer Nature 2018.

关键词： Compressed sensing

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4th iapr TC 9 Workshop on Multimodal pattern recognition of Social Signals in Human-Computer-Interaction, MPRSS 2016

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4th iapr TC 9 Workshop on Multimodal pattern recognition of Social Signals in Human-Computer-Interaction, MPRSS 2016

ISBN: (纸本)9783319592589

the proceedings contain 13 papers. the special focus in this conference is on Multimodal pattern recognition of Social Signals in Human-Computer-Interaction. the topics include: Bimodal recognition of Cognitive Load Based on Speech and Physiological Changes;Human Mobility-pattern Discovery and Next-Place Prediction from GPS Data;Fusion Architectures for Multimodal Cognitive Load recognition;Performance Analysis of Gesture recognition Classifiers for Building a Human Robot Interface;On Automatic Question Answering Using Efficient Primal-Dual Models;Hierarchical Bayesian Multiple Kernel Learning Based Feature Fusion for Action recognition;Audio Visual Speech recognition Using Deep Recurrent Neural Networks;Audio-Visual recognition of Pain Intensity;the SenseEmotion Database: A Multimodal Database for the Development and Systematic Validation of an Automatic Painand Emotion-recognition System;Photometric Stereo for 3D Face Reconstruction Using Non Linear Illumination Models and Recursively Measured Action Units.

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Hand Gesture recognition Systems with the Wearable Myo Armband

Hand Gesture Recognition Systems with the Wearable Myo Armba...

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international conference on Control, Engineering & Information Technology (CEIT)

作者： Engin Kaya Tufan Kumbasar Control and Automation Engineering Department Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9781538676424

the hand gesture recognition systems deal with identifying a given gesture performed by the hand. this work addresses a hand gesture recognition method to classify and recognize the numbers from 0 to 9 in Turkish Sign Language based on surface electromyography (EMG) signals collected from a wearable device, namely the Myo armband. To accomplish such a goal, we have utilized machine learning techniques to recognize the hand gestures. In this context, seven different time domain features are extracted from the raw EMG signals using sliding window approach to get distinctive information. then, the dimension of the feature matrix is reduced by using the principal component analysis to reduce the complexity of the deployed machine learning methods. the presented study includes the design, deployment and comparison of the machine learning algorithms that are k-nearest neighbor, support vector machines and artificial neural network. the results of the comparative comparison show that the support vector machines classifier based system results with the highest recognition rate.

关键词： electromyography feature extraction gesture recognition learning (artificial intelligence) neural nets pattern classification principal component analysis support vector machines wearable computers Gesture recognition Support Vector Network Wearable computers Electromyogram Neural network pattern recognition principal components analysis Feature extraction arm band Wearable

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6th international Workshop on Computational Color Imaging, CCIW 2017

6th International Workshop on Computational Color Imaging, C...

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6th international Workshop on Computational Color Imaging, CCIW 2017

ISBN: (纸本)9783319560090

the proceedings contain 23 papers. the special focus in this conference is on Computational Color Imaging. the topics include: Computational print control;video smoke removal based on smoke imaging model and space-time pixel compensation;similarities and differences in the mathematical formalizations of the retinex model and its variants;a Milano retinex implementation based on intensity thresholding;a multidistortion database for image quality;a complexity-based image analysis to investigate interference between distortions and image contents in image quality assessment;visualization of subsurface features in oil paintings using high-resolution visible and near infrared scanned images;a simple scanner for high resolution imaging of wall paintings;visualizing lost designs in degraded early modern tapestry using infra red image;a novel scanning technique for imaging of gold and silver foils used in art works;a transmission type scanning system for ultra high resolution scanning;a database of spectral filter array images that combine visible and NIR;analytical survey of highlight detection in color and spectral images;characterization by hyperspectral imaging and hypercolor gamut estimation for structural color prints;fast-calibration reflectance-transmittance model to compute multiview recto-verso prints;image contrast measure as a gloss material descriptor;artistic photo filtering recognition using CNNs and improved opponent colour local binary patterns for colour texture classification.

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Discovering latent spatial structured patterns using graph models for scene classification 6

Discovering latent spatial structured patterns using graph m...

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6th international conference on Computer Science and Network Technology (ICCSNT)

作者： Fan, Yuhua Liaocheng Univ Sch Math Sci Liaocheng Shandong Peoples R China

ISBN: (纸本)9781538604939

Despite progress in scene recognition tasks such as image classification and attribute detection, computers still be difficult to understand the scenes as a whole. Existing methods often ignore global spatial constructed pattern among different local semantic objects. this paper propose a method for discovering the Latent spatial structured patterns to describe the visual semantic characters of images to improve the performance of scene recognition tasks. Unlike the existing approaches that mainly rely on the discriminant visual feature cues, we learn the latent spatial structured pattern to model the interaction relationships by using the graph models, which consider semantics and their localization information. We first train the pLSA models to obtain the latent semantic topics. then we construct the graph models to discover the latent spatial structure patterns with combing the character vector and localization cues. Meanwhile, we treat the edge in model as link-affinity matrix to describe the interaction relationships between semantics. the extensive experiments on public datasets have demonstrated that the suggested method can significantly boost the performance of scene classification tasks.

关键词： Semantics Visualization Task analysis Computational modeling Mathematical model pattern recognition Image recognition

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Strategies for Imbalanced pattern Classification for Digital Pathology 6

Strategies for Imbalanced Pattern Classification for Digital...

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6th international conference on Informatics, Electronics and Vision (ICIEV) / 7th international Symposium in Computational Medical and Health Technology (ISCMHT)

作者： Schaefer, Gerald Loughborough Univ Dept Comp Sci Loughborough Leics England

ISBN: (纸本)9781538610237

pattern classification tasks in digital pathology, which involves the analysis of high resolution digital slides of tissue samples for medical diagnosis, are, like many other medical decision making processes, often imbalanced. this means that there are (many) more training samples of some classes available compared to others, while it is often the minority class(es) that are of medical interest. In this paper, we present strategies for addressing class imbalance in pattern classification problems including the development of cost-sensitive fuzzy classifiers and the derivation of ensemble classification methods, that is classifiers that employ multiple predictors, dedicated for imbalanced classification.

关键词： Medical diagnostic imaging pattern classification Pathology Informatics Computer science Task analysis Medical diagnosis pattern recognition Medical diagnosis task analysis computer science pathology Informatics Disease diagnosis diagnostic imaging Classifiers

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High-Level Shape Representation in Printed Gujarati Characters 6

High-Level Shape Representation in Printed Gujarati Characte...

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6th international conference on pattern recognition Applications and Methods (ICPRAM)

作者： Goswami, Mukesh M. Mitra, Suman K. Dharmsinh Desai Univ Fac Technol Nadiad Gujarat India Dhirubhai Ambani Inst Informat & Commun Tech Gandhinagar Gujarat India

ISBN: (纸本)9789897582226

this paper presents extraction and identification of the high-level stroke (HLS) from printed Gujarati characters. the HLS feature describes a character as a sequence of predefined high-level strokes. Such a high-level shape representation enables approximate shape similarity computation between characters and can easily be extended to word-level. the shape similarity based character and word matching have extensive application in word-spotting based document image retrieval and character classification. therefore, the proposed features were tested on printed Gujarati character database consisting of 12000 samples from 42 different symbol classes. the classification is performed using k-nearest neighbor with shape similarity measure. Also, a shape similarity based printed Gujarati word matching experiment is reported on a small word image database and the initial result are encouraging.

关键词： pattern recognition Character Shape Representation Shape Similarity Character recognition Gujarati Characters

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CEP-Based SLO Evaluation 1

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6th European conference on Service-Oriented and Cloud Computing, ESOCC 2017

作者： Kritikos, Kyriakos Zeginis, Chrysostomos Paravoliasis, Andreas Plexousakis, Dimitris Institute of Computer Science - FORTH Heraklion Greece Computer Science Department University of Crete Heraklion Greece

ISBN: (数字)9783319790909

ISBN: (纸本)9783319790893

Modern service-based applications (SBAs) operate in highly dynamic environments where both underlying resources and the application demand can be constantly changing which external SBA components might fail. thus, they need to be rapidly modified to address such changes. Such a rapid updating should be performed across multiple levels to better deal, in an orchestrated and globally-consistent manner, with the current problematic situation. First of all, this means that a fast and scalable event generation and detection mechanism should exist to rapidly trigger the adaptation workflow to be performed. Such a mechanism needs to handle all kinds of events occurring at different abstraction levels and to compose them so as to detect more advanced situations. To this end, this paper introduces a new complex event processing framework able to realise the respective features mentioned (processing speed, scalability) and have the flexibility to capture and sense any kind of event or event combination occurring in the SBA system. Such a framework is wrapped in the form of a REST service enabling to manage the event patterns that need to be rapidly detected. It is also well connected to other main components of the SBA management system, via a publish-subscribe mechanism, including monitoring and the adaptation engines. © 2018, Springer international Publishing AG, part of Springer Nature.

关键词： pattern recognition

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