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Multi-view Learning for 3D LGE-MRI Left Atrial Cavity Segmentation 20

Multi-view Learning for 3D LGE-MRI Left Atrial Cavity Segmen...

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3rd international conference on Artificial Intelligence and pattern recognition (AIPR)

作者： Xiao, Jingjing Si, Dongyue Wu, Yanfang Li, Meng Yin, Jie Ding, Haiyan Army Med Univ Xinqiao Hosp Dept Med Engn Chongqing Peoples R China Tsinghua Univ Ctr Biomed Imaging Res Sch Med Dept Biomed Engn Beijing Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Dept Cardiol Peking Union Med Coll Hosp Beijing Peoples R China

ISBN: (纸本)9781450375511

This paper presents a multi-view learning based method for left atrial cavity segmentation in 3D Late Gadolinium Enhanced Magnetic Resonance Imaging (LGE-MRI). Segmenting left atrium is challenging due to the low intensity contrast, motion artifacts, and extremely thin atrial walls. Since the spatial consistency of the atrium could help to alleviate the segmentation ambiguity caused by those problems, the proposed method consists of three deep convolutional streams which construct 3D segmentation likelihood maps from different views, i.e., axial view, coronal view, and sagittal view. Then, those likelihood maps will be fused and contribute to a final 3D segmentation map, where the method further inspects the 3D connectivity of the labeled pixels and discards the disconnected regions that don't belong to the atrium. The proposed method is tested on a publicly available dataset, where 80 scans are for training and 20 scans are for testing. Compared to the other state-of-the-art algorithms, the proposed method demonstrates a considerable improvement, which shows the advantages of using multi-view information.

关键词： Left atrial cavity segmentation Multi-view learning 3D LGE-MRI

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Image-Based Facial Expression recognition Using Local Neighborhood Difference Binary pattern 3rd

Image-Based Facial Expression Recognition Using Local Neighb...

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3rd international conference on Computer Vision and Image Processing (CVIP)

作者： Saurav, Sumeet Singh, Sanjay Yadav, Madhulika Saini, Ravi Acad Sci & Innovat Res AcSIR Chennai Tamil Nadu India CSIR Cent Elect Engn Res Inst Pilani Rajasthan India Banasthali Vidyapith Dept Elect Vanasthali Rajasthan India

ISBN: (纸本)9789813290884;9789813290877

Automatic facial expression recognition (FER) has gained enormous interest among the computer vision researchers in recent years because of its potential deployment in many industrial, consumer, automobile, and societal applications. There are a number of techniques available in the literature for FER;among them, many appearance-based methods such as local binary pattern (LBP), local directional pattern (LDP), local ternary pattern (LTP), gradient local ternary pattern (GLTP), and improved local ternary pattern (IGLTP) have been shown to be very efficient and accurate. In this paper, we propose a new descriptor called local neighborhood difference binary pattern (LNDBP). This new descriptor is motivated by the recent success of local neighborhood difference pattern (LNDP) which has been proven to be very effective in image retrieval. The basic characteristic of LNDP as compared with the traditional LBP is that it generates binary patterns based on a mutual relationship of all neighboring pixels. Therefore, in order to use the benefit of both LNDP and LBP, we have proposed LNDBP descriptor. Moreover, since the extracted LNDBP features are of higher dimension, therefore a dimensionality reduction technique has been used to reduce the dimension of the LNDBP features. The reduced features are then classified using the kernel extreme learning machine (K-ELM) classifier. In order to, validate the performance of the proposed method, experiments have been conducted on two different FER datasets. The performance has been observed using well-known evaluation measures, such as accuracy, precision, recall, and F1-score. The proposed method has been compared with some of the state-of-the-art works available in the literature and found to be very effective and accurate.

关键词： Facial expression recognition (FER) Local neighborhood difference pattern (LNDP) Principal component analysis (PCA) Kernel extreme learning machine (K-ELM)

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pattern recognition of Dynamic Social Network 3

Pattern Recognition of Dynamic Social Network

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3rd international conference on Intelligent Autonomous Systems (ICoIAS)

作者： Firdaus, Muhamad Fulki Baizal, Z. K. A. Bratawisnu, Made Kevin Gusman, Hanafi Abdullah Telkom Univ Sch Elect Engn Bandung Jawa Barat Indonesia Telkom Univ Sch Comp Bandung Jawa Barat Indonesia Telkom Univ Sch Econ & Business Bandung Jawa Barat Indonesia

ISBN: (数字)9781728160788

ISBN: (纸本)9781728160788

The continuous transformation of civilization caused the connections between society have become more sufficient and persistent. Interaction between human society have spread widely around the globe due to the swift development of technology and internet. The accumulation of people social interaction forms huge-scale unstructured data which changes over time, called the User Generated Content (UGC). The traditional method for analyzing social interactions, namely Social Network Analysis (SNA), only focuses on static social network properties without seeing changes that occur over time. Social network in the real world can be considered to be dynamic processes because individuals follow and quit social interaction by that transforming network structure. Dynamic Network Analysis (DNA) can analyze dynamic social network through graph over time to view pattern recognition of dynamic social interaction during research period. In this observation, we analyze dynamic social network from social media, precisely in Twitter. Case studies used in this research are online transportation, bank, television channel, and online news portal by reason of they are having immense dynamic interactions in social media. Analysis of dynamic network using graph over time to view the evolution of network properties.

关键词： user generated content network properties dynamic social network

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Activity recognition using Multi-Class Classification inside an Educational Building 18

Activity Recognition using Multi-Class Classification inside...

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IEEE international conference on Pervasive Computing and Communications (PerCom)

作者： Das, Anooshmita Kjaergaard, Mikkel Baun Univ Southern Denmark Ctr Energy Informat Odense Denmark

ISBN: (纸本)9781728147161

Activity recognition can be referred to as the process of describing and classifying actions, pinpoint specific movements, and extract unique patterns from the dataset using heterogeneous sensing modalities. Activity recognition approaches have garnered the attention of researchers in the energy management domain to enhance energy utilization in buildings. In our experiment, we define activities as a combination of different actions, which are detected using multiple sensors. To learn insights for the various activities, we used inexpensive Passive Infrared (PIR) sensors in the test-bed. This study aims at gaining high-level knowledge about activities from the low-resolution sensors deployed. For accurate occupancy counts, we have used 3D Stereo Vision Cameras at the entrance, and count lines are defined to capture the transitions of inflow and outflow of multiple occupants. Multi-class labels enable activity recognition on the collected dataset. The multi-class labels used are 1) Moving, 2) Stagnant, 3) Outside, 4) Both (Moving and Stagnant), 5) No activity inside. The labeling for the multi-class is done through an algorithm using supervised learning. The data acquisition gets carried out from 23rd November to 3rd December 2018, spanning over a period for 11 days. The results document that Gradient Boosting Classifier outperforms any other Machine Learning Classification (MLC) algorithm with an accuracy of 97.59% and an F1 score of 97.40% for activity recognition. This paper also explicitly highlights the challenges and limitations faced during the initial phase for the deployment, and it identifies the key research trends and directs towards the potential improvements in the field of occupancy sensing for energy-efficient buildings.

关键词： Building Performance Occupant Behaviour Sensor Fusion pattern recognition Knowledge Discovery Machine Learning Activity recognition

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PCA-based 3D Facial Reenactment from Single Image 3

PCA-based 3D Facial Reenactment from Single Image

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3rd international conference on Multimedia Analysis and pattern recognition, MAPR 2020

作者： Dat, Nguyen Thanh Anh, Nguyen Dang Tuan Sang, Dinh Viet Hanoi University of Science and Technology Department of Computer Science 1 Dai Co Viet Hai Ba Trung Hanoi Viet Nam

ISBN: (纸本)9781728165554

Facial re-enactment from videos and images is one of the essential tasks in character animation applied in the creation of video games and movies. In this paper, we propose an efficient method for 3D facial re-enactment for online facial expression transferring. By approximating the pose, the approximation of expression parameters is reduced to a linear regression problem of which the closed-form solution is readily available, resulting in the improvement in inference speed. We also test the proposed method with multiple variants of initial blendshapes estimation and compare the results with the prior in term of both quality and speed. © 2020 IEEE.

关键词： Image reconstruction

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Research on 3D object recognition based on feature level fusion 3

Research on 3D object recognition based on feature level fus...

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3rd international conference on Computer Network, Electronic and Automation, ICCNEA 2020

作者： Yu, Fan Yang, Rui School of Computer Science and Engineering Xi'An Technological University Xi'an Shaanxi710021 China

ISBN: (数字)9781728170831

ISBN: (纸本)9781728170831

Target recognition has always been a hot research topic in computer image and pattern recognition. This paper proposes a target recognition method based on feature layer fusion. Using the 3D CAD model ModelNet as the object model to be identified, the features are extracted from the point cloud data and the multi-view 2D image of the model through the two-channel convolutional neural network (CNN), and the network fusion is completed in the feature layer. Representative three-dimensional features to complete the classification of objects. The experimental results show that the proposed method effectively improves the accuracy of object recognition. © 2020 IEEE.

关键词： Object recognition

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Comparison EMG pattern recognition Using Bayes and NN Methods 3

Comparison EMG Pattern Recognition Using Bayes and NN Method...

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3rd international conference on Mechanical, Electronics, Computer, and Industrial Technology, MECnIT 2020

作者： Pamungkas, Daniel Sutopo Simatupang, Imanuel Politeknik Negeri Batam Electronics Dept Mechatronics Dept Indonesia

This article presents a comparative study for recognition hand gesture between Na39;ve Bayes and Neural Network (NN) methods for electromyography signals (EMG). EMG signals are obtained from five gestures from a su... 详细信息

ISBN: (纸本)9781728174037

This article presents a comparative study for recognition hand gesture between Naïve Bayes and Neural Network (NN) methods for electromyography signals (EMG). EMG signals are obtained from five gestures from a subject. Myo Armband is used to receive the information from the upper hand of the user. The outputs of the systems are used to control a mobile robot. The results showed that the NN method has a higher percentage of success than the Naïve Bayes algorithm. However, the Naïve Bayes method is faster than the NN algorithm. © 2020 IEEE.

关键词： pattern recognition

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Geometry-based Completed Local Binary pattern for Texture Image Classification 3

Geometry-based Completed Local Binary Pattern for Texture Im...

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3rd IEEE international conference on Information Communication and Signal Processing (ICICSP)

作者： Wang, Wei Kou, Qiqi Zhou, Shu Luo, Ke Zhang, Lifeng Tiandi Changzhou Automat Co Ltd Changzhou 213015 Peoples R China CCTEG Changzhou Res Inst Changzhou 213015 Peoples R China China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China

ISBN: (数字)9781728188232

ISBN: (纸本)9781728188232

In view of the fact that the accuracy of texture image classification is easily affected by changes in illumination and rotation, based on the analysis of geometric curvatures information of the image microscopic geometric surface and the completed local binary pattern (CLBP), this paper proposed a new descriptor, named as Geometry-based Completed Local Binary pattern (GCLBP). Inspired by the continuous rotation invariance and illumination robustness of the geometric curvature information, principal curvatures (PCs) of all pixels are first calculated and then used to represent the gradient magnitude information of the image, which are further exploited to replace the original gradient magnitude information in CLBP. To further improve the accuracy of texture classification, a cross-scale joint coding strategy is exploited to form the final GCLBP. The experimental results on two standard texture databases demonstrate that the GCLBP algorithm proposed in this paper is not only far superior to the original CLBP in terms of classification recognition accuracy and dimensionality of feature vector, but also better than most existing advanced texture classification methods.

关键词： completed local binary pattern (CLBP) principal curvatures (PCs) texture classification geometric curvatures

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3rd international conference on Advanced Communication Technologies and Networking, CommNet 2020

3rd International Conference on Advanced Communication Techn...

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3rd international conference on Advanced Communication Technologies and Networking, CommNet 2020

ISBN: (纸本)9781728187044

The proceedings contain 32 papers. The topics discussed include: RevoCampus: a distributed open source and low-cost smart campus;smart nest box: IoT based nest monitoring in artificial cavities;man-in-the-middle attacks to detect and identify services in encrypted network flows using machine learning;toward a deep smart waste management system based on pattern recognition and transfer learning;identification of the k-most vulnerable entities in a smart grid system;an enhanced authentication protocol based group for vehicular communications over 5G networks;improving the REP in an SGC: multi-objective predictive optimization algorithms based on fuzzy multi-criteria decision-making;classification of patients with breast cancer using neighborhood component analysis and supervised machine learning techniques;and prediction of patients with heart disease using artificial neural network and adaptive boosting techniques.

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Proceedings of the 3rd international conference on Intelligent Sustainable Systems, ICISS 2020

Proceedings of the 3rd International Conference on Intellige...

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3rd international conference on Intelligent Sustainable Systems, ICISS 2020

ISBN: (纸本)9781728170893

The proceedings contain 273 papers. The topics discussed include: intelligent automation based gas valve control mechanism in biogas plant;analysis of guiding quality evaluation model based on regional ecological safety performance evaluation and information mining;review on materials and methods for supercapacitors;performance analysis of current-fed DAB converter for DC microgrid with active power control;computer data processing mode in the era of big data: from pattern recognition to intelligent sensing;road conditions and obstacles indication and autonomous braking system;a study of big data analytics using apache spark with python and Scala;implementing a DC UPS with battery’s state of charge estimation based on coulomb-counting method;cascaded GSM detector-jammer design;diabetes prediction by using big data tool and machine learning approaches;and an efficient design of fault tolerant reversible multiplexer using QCA technology.

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