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检索条件"任意字段=8th International Conference on Computing and Pattern Recognition, ICCPR 2019"

共 395 条记录，以下是241-250 订阅

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A Potent Model to Recognize Bangla Sign Language Digits Using Convolutional Neural Network 8

A Potent Model to Recognize Bangla Sign Language Digits Usin...

引用

8th international conference on Advances in computing & Communications (ICACC)

作者： Islam, Md. Sanzidul Mousumi, Sadia Sultana Sharmin Rabby, A. K. M. Shahariar Azad Hossain, Sayed Akhter Abujar, Sheikh Daffodil Int Univ Dept Comp Sci & Engn Dhaka 1205 Bangladesh

Hearing impaired people have own language called Sign Language but it is difficult for understanding to general people. Sign language is the basic method of communication for deaf people during their everyday of life. Sign digits are also a major part of sign language. So machine translator is necessary to allow them to communicate with general people. For making their language understandable to general people, computer vision based solutions are well known nowadays. In this research work we aims at constructing a model in deep learning approach to recognize Bangla Sign Language (BdSL) digits. In this approach there used Convolutional Neural Network (CNN) to train particular signs with a respective training dataset (Eshara-Lipi) for acquiring our aim. the model trained and tested with respectively 860 training images and 215 (20%) test images of tent classes of digits. Finally, the training model gained about 95% accuracy at recognition of Bangla sign language digits. this model will contribute for moving one step forward to make BdSL machine translator. (C) 2018 the Authors. Published by Elsevier B.V.

关键词： BdSL Bangla Sign Language CNN Machine Learning Deep Learning NLP Computer Vision pattern recognition Sign Digits Sign Language

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Context in Human Emotion Perception for Automatic Affect Detection: A Survey of Audiovisual Databases

Context in Human Emotion Perception for Automatic Affect Det...

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international conference on Affective computing and Intelligent Interaction and Workshops, ACII

作者： Bernd Dudzik Michel-Pierre Jansen Franziska Burger Frank Kaptein Joost Broekens Dirk K.J. Heylen Hayley Hung Mark A. Neerincx Khiet P. Truong INSY Delft University of Technology Delft NL HMI University of Twente Enschede NL LIACS Leiden University Leiden NL

An important aspect of human emotion perception is the use of contextual information to understand others' feelings even in situations where their behavior is not very expressive or has an emotionally ambiguous meaning. For technology to successfully detect affect, it must mimic this human ability when analyzing audiovisual input. Databases upon which machine learning algorithms are trained should capture the context of social interactions as well as the behavior expressed in them. However, there is a lack of consensus about what constitutes relevant context in such databases. In this article, we make two contributions towards overcoming this challenge: (a) we identify two principal sources of context for emotion perceptions based on psychological theory, and (b) we provide an overview of how each of these has been considered in published databases covering social interactions. Our results show that a similar set of contextual features are present across the reviewed databases. Between all the different databases researchers seem to have taken into account a set of contextual features reflecting the sources of context seen in psychological theory. However, within individual databases, these features are not yet systematically varied. this is problematic because it prevents them from being used directly as resources for the modeling of context-sensitive affect detection. Based on our findings, we suggest improvements for the future development of affective databases.

关键词： Audio databases Emotion recognition Feature extraction Machine learning algorithms Psychology Affective computing Visual databases

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Affective computing: Using Covariance Descriptors for Facial Expression recognition 8th

Affective Computing: Using Covariance Descriptors for Facial...

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8th international conference on Image and Signal Processing (ICISP)

作者： Naidoo, Ashaylin Tapamo, Jules Raymond Khutlang, Rethabile Univ KwaZulu Natal ZA-4000 Durban South Africa CSIR ZA-0184 Pretoria South Africa

ISBN: (纸本)9783319942117;9783319942100

Facial expression contains a rich variety of affective information. Facial Expression recognition (FER) is an emerging field that has broad applications in the fields of human-computer interaction, psychological behaviour analysis and image understanding. However, FER presently is not fully realized due to the lack of an effective facial feature descriptor. this paper will examine the performance of a novel facial image descriptor referred to as Local Directional Covariance Matrices (LDCM). the descriptor will consist of fusing features, such as location, intensity, filter responses and incorporate local texture patterns. Tests were done on both posed and spontaneous facial expression datasets to evaluate the performance of the proposed model on real-world applications. Results demonstrate the effectiveness of the covariance feature descriptors compared to standard methods.

关键词： Local Directional pattern Facial expression recognition Covariance descriptor Local Binary pattern

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Human-Computer Interaction using Finger Signing recognition with Hand Palm Centroid PSO Search and Skin-Color Classification and Segmentation 8

Human-Computer Interaction using Finger Signing Recognition ...

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8th international conference on Image Processing theory, Tools and Applications (IPTA)

作者： Hamici, Zoubir Al Zaytoonah Univ Jordan ZUJ Fac Engn & Technol Dept Elect Engn Amman Jordan

ISBN: (纸本)9781538664285

this paper presents a novel image processing technique for recognizing finger signs language alphabet. A human-computer interaction system is built based on the recognition of sign language which constitutes an interface between the computer and hearing-impaired persons, or as an assistive technology in industrial robotics. the sign language recognition is articulated on the extraction of the contours of the sign language alphabets, therefore, converting image recognition into one dimensional signal processing, which improves the recognition efficiency and significantly reduces the processing time. the pre-processing of images is performed by a novel skin-color region segmentation defined inside the standard RGB (sRGB) color space, then a morphological filtering is used for non-skin residuals removal. Afterwards, a circular correlation achieves the identification of the sign language after extracting the sign closed contour vector and performing matching between extracted vector and target alphabets vectors. the closed contour vector is generated around the hand palm centroid with position optimized by a particle swarm optimization algorithm search. Finally, a multi-objective function is used for computing the recognition score. the results presented in this paper for skin color segmentation, centroid search and pattern recognition show high effectiveness of the novel artificial vision engine.

关键词： Human-Machine Interaction pattern recognition Particle Swarm Optimization Sign language Skin-color Segmentation

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Buzzer Detection to Maintain Information Neutrality in 2019 Indonesia Presidential Election

Buzzer Detection to Maintain Information Neutrality in 2019 ...

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IIAI international conference on Advanced Applied Informatics (IIAIAAI)

作者： Adrian Julianto Panatra Felisitus Bryan Chandra Wiliam Darmawan Harco Leslie Hendric Spits Warnars Wiranto Herry Utomo Tokuro Matsuo Computer Science Department School Of Computer Science Bina Nusantara University Jakarta Indonesia Bina Nusantara University Jakarta Indonesia Faculty of Computing President University Jawa Barat Indonesia Graduate School of Industrial Technology Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9781728126289

this paper proposed a method which detects a political buzzer in social media, specifically Instagram. With Indonesia undergoing 2019 presidential election, a detection of buzzers that causes much trouble in maintaining information neutrality is seen as a needed. One of the many reasons is because those buzzers spread false news making the information gained by the use of social media to be not neutral and deliberately offends or attack those that they are not in favor of. those buzzers share a similar characteristic, tendency, or even possess the same pattern. Grouping, classification, and detection method are used to counter this problem. this research gives a slight overview of what is happening in social media and a theory of how to deal with those problems. the argument is expected to help to identify buzzer in real life, thus helps in maintaining information neutrality along with the social media in Indonesia.

关键词： Buzzer detection 2019 indonesia presidential election Social media retrieval Buzzer Device Component Social Media Maintaining information Indonesia Neutrality inspection methods

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Automated Situation Analysis as Next Level of Unmanned Aerial Vehicle Artificial Intelligence

Automated Situation Analysis as Next Level of Unmanned Aeria...

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8th international conference on Applied Human Factors and Ergonomics (AHEF) / international conferences on Human Factors and Simulation and Digital Human Modeling and Applied Optimization

作者： Strupka, Gunta Levchenkov, Anatoly Gorobetz, Mikhail Riga Tech Univ Inst Ind Elect & Elect Engn Fac Power & Elect Engn Azenes St 12-1-504 LV-1048 Riga Latvia Riga Tech Univ Inst Ind Elect & Elect Engn Fac Power & Elect Engn Azenes St 12-1-512 LV-1048 Riga Latvia

ISBN: (纸本)9783319605913;9783319605906

In this paper automated situation analysis is discussed together with already accessible advantages of artificial intelligence and control systems of unmanned aerial vehicle. Based on previous researches some new solutions are proposed to fulfill safety tasks in case of traffic, fire and criminal threats. Mostly connected with existing solutions, owing to artificial intelligence and hybrid systems they move to next level and provide better results and guideline for further development.

关键词： Artificial intelligence Probabilistic logic Unmanned aerial vehicles Programming Microcontrollers Automatic control Path planning Safety Security Traffic pattern recognition Act recognition

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Generating Synthetic Samples to Improve Small Sample Learning with Mixed Numerical and Categorical Attributes

Generating Synthetic Samples to Improve Small Sample Learnin...

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IIAI international conference on Advanced Applied Informatics (IIAIAAI)

作者： Yao-San Lin Wan-Ni Cheng Chien-Chih Chen Der-Chiang Li Hung-Yu Chen Singapore Centre for Chinese Language Nanyang Technological University Singapore Department of Industrial and Information Management National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9781728126289

the small data learning issue has existed for over one hundred years (since 1908) when the Student's t-distribution was first developed. Few statistical tools can evaluate a population appropriately if the sample size is too small; small samples can be remedied through virtual sample generation (VSG) methods, which are widely used in industry and machine learning. However, most VSG methods were developed for data having only numerical attributes, very few studies have dealt with nominal attributes and cause domain estimation limitations. therefore, this paper proposes a method that generates virtual samples based on the discrete degrees of nominal attributes, and then estimates the general population domains by fuzzy membership functions. A backpropagation neural network model and a support vector regression model are used to test the efficiency of the proposed method, while the Wilcoxon-sign test is used to test the difference with raw data sets. the result shows that the proposed method can reduce the mean absolute error and enhance classification accuracy by generating virtual samples that have nominal attributes.

关键词： backpropagation fuzzy set theory learning (artificial intelligence) neural nets pattern classification regression analysis support vector machines Support Vector Network back propagation Neural network Nominal pattern recognition Fuzzy Set theory Categorical regression analysis Transmissions

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Examining the Effects of Automatic Comment Classification on Comment Types in Peer Review for Graduate Students' Academic Writing

Examining the Effects of Automatic Comment Classification on...

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IIAI international conference on Advanced Applied Informatics (IIAIAAI)

作者： Calvin C. Y. Liao Yuhuan Li Hercy N. H. Cheng College of Nursing National Taipei University of Nursing and Health Sciences Taipei Taiwan R.O.C. National Engineering Research Center for E-Learning Central China Normal University Wuhan China National Engineering Laboratory for Educational Big Data Central China Normal University Wuhan China

ISBN: (纸本)9781728126289

Although previous studies have confirmed the effect of peer review on academic writing, graduate students are usually unable to provide appropriate comments in peer review. As a result, their comments cannot effectively help authors revise academic papers. For this reason, in order to improve the quality of students' comments, this study aims to provide peer reviewers with feedback of the automatic classification of their comments in an existing peer review system, so that reviewers can improve their comments for writers. In order to examine its effect, this study designs an experiment in an academic writing course for first-year graduate students in two semesters. In the experimental group, peer reviewers are provided with the feedback, while students in the control group did not receive any feedback. the results showed that the graduate students in the experimental group consciously improved their comments after receiving the feedback, especially on the cognitive dimensions (e.g., increasing guidance) and the metacognitive dimensions (e.g., increasing evaluating comments). the findings may provide a direction for students to write high-quality comments and improve writing proficiency.

关键词： cognition computer aided instruction educational courses further education pattern classification Peer Review Continuing Education computer-assisted instruction Education courses graduate students pattern recognition Comment Feedback Cognition Writing Students Control Groups Automatic classification

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Biometric Identification through Eye-Movement patterns

Biometric Identification Through Eye-Movement Patterns

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8th international conference on Applied Human Factors and Ergonomics (AHEF) / international conferences on Human Factors and Simulation and Digital Human Modeling and Applied Optimization

作者： Bayat, Akram Pomplun, Marc Univ Massachusetts Boston MA 02125 USA

ISBN: (纸本)9783319605913;9783319605906

this paper describes how to identify unique individual readers using their eye-movement patterns. A case study including forty participants was conducted in order to measure eye movement during reading. the proposed biometric method is developed based on an informative and stable eye-movement feature set that gives rise to a high performance multi-class identification model. Multiple individual classifiers are trained and tested on our novel feature set consisting of 28 features that represent basic eye-movement, scan path and pupillary characteristics. We combine three high-accuracy classifiers, namely Multilayer Perceptron, Logistic, and Logistic Model Tree using the average of probabilities as the combination rule. We reach an overall accuracy of 95.31% and an average Equal Error Rate (EER) of 2.03%. Our approach dramatically outperforms previous methods, making it possible to build eye-movement biometric systems for user identification and personalized interfaces.

关键词： Biometric identification Eye movement Equal error rate pattern recognition

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8th international conference on ICT Innovations, ICT Innovations 2016

8th International Conference on ICT Innovations, ICT Innovat...

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8th international conference on ICT Innovations, ICT Innovations 2016

ISBN: (纸本)9783319688541

the proceedings contain 24 papers. the special focus in this conference is on ICT Innovations. the topics include: Preface;towards multimodal affective stimulation: Interaction between visual, auditory and haptic modalities;performance evaluation of FIR and IIR filtering of ECG signals;influence of fuzzy tolerance metrics on classification and regression tasks for fuzzy-rough nearest neighbour algorithms;on the Kalman filter approach for localization of mobile robots;evaluation of automatically generated conceptual database model based on business process model: Controlled experiment;analysis of protein interaction network for colorectal cancer;using sentiment analysis of Twitter data for determining popularity of city locations;internet addiction: Evaluating the psychometric properties of the IAT in Macedonia;health care domain mobile reminder for taking prescribed medications;enhancing text-based relatedness measures with semantic web data;power consumption analysis of application layer protocols for the Internet of things;cognitive and emotive robotics: Artificial brain computing cognitive actions and emotive evaluations, since 1981;improving medical cases retrieval using an online fact database;improving scalability of web applications by utilizing asynchronous I/O;relevance re-ranking through proximity based term frequency model;information system for biosensors data exchange in healthcare;an automatic tracking system for natural hazard events with satellite remote sensing;metaphors of creativity;socially intelligent robots, the next generation of consumer robots and the challenges;hand gesture recognition using deep convolutional neural networks;computer-based statistical description of phonetical balance for Romanian utterances;distributed private key generator for ID-based public key infrastructure;pattern recognition of a digital ECG.

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