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A Cognitive Semantic-Based Approach for Human Event Detection in Videos 3rd

A Cognitive Semantic-Based Approach for Human Event Detectio...

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3rd international conference on Smart Trends for Information Technology and Computer Communications, SmartCom 2019

作者： Seemanthini, K. Manjunath, S.S. Srinivasa, G. Kiran, B. Sowmyasree, P. Dayananda Sagar Academy of Technology and Management Bangalore India Department of Computer Science & Engineering ATMECE Mysuru India

ISBN: (纸本)9789811500763

Surveillance systems are mainly used to monitor activity and behaviour for the purpose of managing small human group. In particularly, the depicted approach focused on the small human groups stayed in the same place for a while and characterizing the behaviour of the group. The described approach has wide functions in several areas such as surveillance, group interface and behaviour classification. The video surveillance mainly deals with the recognition and classification of group behaviour with respect to several activities such as Normal speech, kicking, hugging and punching. The processing steps include frame generation, segmentation using Fuzzy C-Mean (FCM) clustering;features are extracted using Local Binary pattern (LBP) and Hierarchical Centroid (HC). Convolution Neural Network (CNN) is used to classify the features. The proposed model is better than existing methods by achieving 80% accuracy. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Local binary pattern

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Toward a Deep Smart Waste Management System based on pattern recognition and Transfer learning 3

Toward a Deep Smart Waste Management System based on Pattern...

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

作者： Jadli, Aissam Hain, Mustapa University Hassan II Sism Team I2SI2E Laboratory Ensam Casablanca Casablanca Morocco

ISBN: (纸本)9781728187044

Smart cities essentially combine the use of Information and communications technology (ICT) to provide services for better living conditions inside. The recent developments in the Internet of Things (IoT) opened vast opportunities for researchers and developers to implement various systems and applications in the field of Smart Cities and Intelligent Transportation Systems (ITS). It is a diverse topic of discussion with several application areas such as smart traffic management, smart street lights or gas and water leak detection. Among these applications, Efficient Waste Collection (WC) is considered a fundamental service for helping maintain a healthier environment for the citizens while reducing operating costs. This paper proposes a new architecture for a smart wastes management system based on artificial intelligence techniques and focuses on the combined use of the Internet of Things (IoT) and surveillance systems as an assistive technology for high Quality of Service (QoS) in waste collection. By integrating deep learning techniques in this sector, important cost reductions can be made while maintaining optimal performance. © 2020 IEEE.

关键词： Smart city

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Bangla Handwritten Basic Character recognition Using Deep Convolutional Neural Network 8

Bangla Handwritten Basic Character Recognition Using Deep Co...

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Joint 8th international conference on Informatics, Electronics and Vision (ICIEV) / 3rd international conference on Imaging, Vision and pattern recognition (icIVPR) / international conference on Activity and Behavior Computing (ABC)

作者： Saha, Chandrika Faisal, Rahat Hossain Rahman, Md Mostafijur Univ Barishal Dept Comp Sci & Engn Barishal Bangladesh

ISBN: (纸本)9781728107882

Bangla is one of the mostly spoken languages all over the world, nonetheless, efforts on Bangla handwritten character recognition is not adequate. The use of Deep Convolutional Neural Network (DCNN) based classifiers has become a triumph over the state of art machine learning techniques. Using DCNN model to classify Bangla isolated basic characters can provide better output because of its ability to detect many hidden features from an image. In this paper, a new DCNN model, namely, BBCNet-15 is proposed for Bangla handwritten basic character recognition. the proposed model consists of 6 convolution layers, 6 max pooling layers, 2 fully connected layers followed by the softmax output unit. To avoid overfitting dropout regularization technique is used. The implementation of the proposed DCNN model is evaluated on a benchmark dataset, CMATErdb 3.1.2 which contains 50 character classes including 39 consonants and 11 vowels. The experimental evaluation of BBCNet-15 on the dataset provides a recognition accuracy of 96.40% which outperforms some prominent techniques in existence.

关键词： Deep learning Convolutional Neural Network pattern recognition Optical Character recognition Bangla Handwritten Character recognition

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3rd international conference on Dynamic Data Driven Application Systems, DDDAS 2020

3rd International Conference on Dynamic Data Driven Applicat...

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3rd international conference on Dynamic Data Driven Application Systems, DDDAS 2020

ISBN: (纸本)9783030617240

The proceedings contain 43 papers. The special focus in this conference is on Dynamic Data Driven Application Systems. The topics include: A DDDAS Protocol for Real-Time Large-Scale UAS Flight Coordination;data-Driven State Awareness for Fly-by-Feel Aerial Vehicles via Adaptive Time Series and Gaussian Process Regression Models;preface;predictive Digital Twins: Where Dynamic Data-Driven Learning Meets Physics-Based Modeling;Integrated Planning, Decision-Making, and Weather Modeling for UAS Navigating Complex Weather;Microgrid Operational Planning Using Deviation Clustering Within a DDDAS Framework;dynamic Data-Driven Self-healing Application for Phasor Measurement Unit Networks;interpretable Deep Attention Model for Multivariate Time Series Prediction in Building Energy Systems;overcoming Stealthy Adversarial Attacks on Power Grid Load Predictions Through Dynamic Data Repair;Uncertainty Analysis of Self-healed Composites with Machine Learning as Part of DDDAS;active Search Methods to Predict Material Failure Under Intermittent Loading in the Serebrinksy-Ortiz Fatigue Model;dynamic Data-Driven Distribution Tracking of Nanoparticle Morphology;machine Learning Algorithms for Improved Thermospheric Density Modeling;Dynamic Transfer Learning from Physics-Based Simulated SAR Imagery for Automatic Target recognition;Dynamic Data-Driven Application Systems for NASA’s Science Mission Directorate;uncertainty Estimation for Semantic Segmentation of Hyperspectral Imagery;Spectral Super Resolution with DCT Decomposition and Deep Residual Learning;active Scene Classification via Dynamically Learning Prototypical Views;informative Ensemble Kalman Learning for Neural Structure;reachability Analysis Based Tracking: Applications to Non-cooperative Space Object Tracking;sparse Regression and Adaptive Feature Generation for the Discovery of Dynamical Systems;improving Prediction Confidence in Learning-Enabled Autonomous Systems;towards Provably Correct Probabilistic Flight Systems;data

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Improving binary skin cancer classification based on best model selection method combined with optimizing full connected layers of Deep CNN 3

Improving binary skin cancer classification based on best mo...

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

作者： Pham, Tri Cong Hoang, Van Dung Tran, Cong Thanh Luu, Minh Sao Khue Mai, Duy Anh Doucet, Antoine Luong, Chi Mai ICTLab University of Science and Technology of Hanoi VAST Vietnam Academy of Science and Technology 18 Hoang Quoc Viet Hanoi Viet Nam Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam Fpt Software 17 Duy Tan Hanoi Viet Nam QuangBinh University Dong Hoi Quang Binh Viet Nam Laboratory L3i University la Rochelle France Institute of Information Technology Vast

ISBN: (纸本)9781728165554

Melanoma is one of the most dangerous skin cancers, leading to high mortality rates. Early detection and resection are two important steps to reduce mortality. Recently, several studies have applied artificial intelligence to facilitate binary skin cancer classification. However, the imbalance of the sensitivity and specificity metrics affects the overall performance of the models. To solve this problem, we propose an optimization for deep Convolutional Neural Network (CNN) combined with a change in the best model selection for this binary melanoma classification. Our research uses ISIC 2019, the latest and largest dataset consist of 17, 302 skin lesion images for training and best model selection. The performance of the best models is analyzed based on the 10% data of ISIC 2019 dataset, then compared with the performance of dermatologists on 100 medical images of the MClass-D dataset. Our optimized deep CNN solves the underfitting problem and avoids overfitting. Our proposed best model selection method with an increase in Youden Index (YI) on both test-10 and MClass-D datasets also outperforms traditional methods. Moreover, our solution effectively outperformed 153 out of 157 dermatologists, which surpasses the current state-of-the-art solution by 17 dermatologists. © 2020 IEEE.

关键词： Convolutional neural networks

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A Deep pattern recognition Approach for Inferring Respiratory Volume Fluctuations from fMRI Data 23rd

A Deep Pattern Recognition Approach for Inferring Respirator...

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23rd international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Bayrak, Roza G. Salas, Jorge A. Huo, Yuankai Chang, Catie Department of Electrical Engineering and Computer Science Vanderbilt University NashvilleTN37215 United States Department of Biomedical Engineering Vanderbilt University NashvilleTN37215 United States Vanderbilt University Institute of Imaging Science NashvilleTN37215 United States

ISBN: (纸本)9783030597276

Functional magnetic resonance imaging (fMRI) is one of the most widely used non-invasive techniques for investigating human brain activity. Yet, in addition to local neural activity, fMRI signals can be substantially influenced by non-local physiological effects stemming from processes such as slow changes in respiratory volume (RV) over time. While external monitoring of respiration is currently relied upon for quantifying RV and reducing its effects during fMRI scans, these measurements are not always available or of sufficient quality. Here, we propose an end-to-end procedure for modeling fMRI effects linked with RV, in the common scenario of missing respiration data. We compare the performance of multiple deep learning models in reconstructing missing RV data based on fMRI spatiotemporal patterns. Finally, we demonstrate how the inference of missing RV data may improve the quality of resting-state fMRI analysis by directly accounting for signal variations associated with slow changes in the depth of breathing over time. © 2020, Springer Nature Switzerland AG.

关键词： pattern recognition

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15th Workshop on Dependable Smart Embedded and Cyber-Physical Systems and Systems-of-Systems, DECSoS 2020, 1st international Workshop on Dependable Development-Operation Continuum Methods for Dependable Cyber-Physical Systems, DepDevOps 2020, 1st international Workshop on Underpinnings for Safe Distributed AI, USDAI 2020, and 3rd international Workshop on Artificial Intelligence Safety Engineering, WAISE 2020, held in conjunction with the 39th international conference on Computer Safety, Reliability and Security, SAFECOMP 2020

15th Workshop on Dependable Smart Embedded and Cyber-Physica...

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15th Workshop on Dependable Smart Embedded and Cyber-Physical Systems and Systems-of-Systems, DECSoS 2020, 1st international Workshop on Dependable Development-Operation Continuum Methods for Dependable Cyber-Physical Systems, DepDevOps 2020, 1st international Workshop on Underpinnings for Safe Distributed AI, USDAI 2020, and 3rd international Workshop on Artificial Intelligence Safety Engineering, WAISE 2020, held in conjunction with the 39th international conference on Computer Safety, Reliability and Security, SAFECOMP 2020

ISBN: (纸本)9783030555825

The proceedings contain 31 papers. The special focus in this conference is on Dependable Smart Embedded and Cyber-Physical Systems. The topics include: A Framework for Building Uncertainty Wrappers for AI/ML-Based Data-Driven Components;rule-Based Safety Evidence for Neural Networks;safety Concerns and Mitigation Approaches Regarding the Use of Deep Learning in Safety-Critical Perception Tasks;positive Trust Balance for Self-driving Car Deployment;Integration of Formal Safety Models on System Level Using the Example of Responsibility Sensitive Safety and CARLA Driving Simulator;a Safety Case pattern for Systems with Machine Learning Components;structuring the Safety Argumentation for Deep Neural Network Based Perception in Automotive Applications;Collecting and Classifying Security and Privacy Design patterns for Connected Vehicles: SECREDAS Approach;an Assurance Case pattern for the Interpretability of Machine Learning in Safety-Critical Systems;A Structured Argument for Assuring Safety of the Intended Functionality (SOTIF);safety and Security Interference Analysis in the Design Stage;formalising the Impact of Security Attacks on IoT Safety;assurance Case patterns for Cyber-Physical Systems with Deep Neural Networks;safety-Critical Software Development in C++;an Instruction Filter for Time-Predictable Code Execution on Standard Processors;ISO/SAE DIS 21434 Automotive Cybersecurity Standard - In a Nutshell;preface;supervisory Control Theory in System Safety Analysis;WiCAR - Simulating Towards the Wireless Car;Automated Right of Way for Emergency Vehicles in C-ITS: An Analysis of Cyber-Security Risks;integrity Checking of Railway Interlocking Firmware;LoRaWAN with HSM as a Security Improvement for Agriculture Applications;multilevel Runtime Security and Safety Monitoring for Cyber Physical Systems Using Model-Based Engineering.

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Bangladeshi License Plate recognition Using Adaboost Classifier 8

Bangladeshi License Plate Recognition Using Adaboost Classif...

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作者： Dhar, Prashengit Abedin, Md Zainal Karim, Razuan Fatema-Tuj-Johora Hossain, Mohammad Shahadat Univ Sci & Technol Chittagong Dept Comp Sci & Engn Chattogram Bangladesh Univ Chittagong Dept Comp Sci & Engn Chittagong 4331 Bangladesh

ISBN: (纸本)9781728107882

License plate recognition (LPR) is a technology for the authentication of a vehicle by locating and recognizing the license plate number in an image through computer vision techniques and machine learning models. To develop intelligent traffic management such as vehicle monitoring, LPR is a key component. However, due to the diversity of layouts and characters of plates, universal solution is not possible. So, this research focuses on development of an algorithm for the recognition of license plate of Bangladesh by using image processing's and machine learning model. This algorithm executes in three steps: detection of the plate with shape verification, tilt correction and recognition of the number. For detection, RGB color space, median filtering, binarization, morphological analysis, region properties for filtering are applied. To discard noisy object, shape verification is done through robust distances to borders vectors. Before character segmentation, horizontal tilt correction is applied. Then, characters are extracted by using bounding box parameters from the extracted plate. Finally, the recognition is implemented by using the blending of Histogram Oriented Gradient (HOG) and Local Binary pattern (LBP) features and adaptive boosting (Adaboost) classifier is used to categorize the characters. The proposed algorithm is simulated on the images which are captured from different roads of Bangladesh. The experimental result shows that the detection and recognition accuracy is noteworthy.

关键词： License Plate recognition (LPR) RGB Color Model Distance to Borders (DtBs) Character Segmentation HOG (Histogram of Oriented Gradient) Local Binary pattern (LBP) Adaboost

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How feature fusion can help to improve multi-shot person re-identification performance? 3

How feature fusion can help to improve multi-shot person re-...

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

作者： Nguyen, Thuy-Binh Nguyen, Trong-Nghia Nguyen, Hong-Quan Le, Thi-Lan Pham, Thi Thanh Thuy School of Electronics and Telecommunications Hanoi University of Science and Technology Viet Nam Computer Vision Department Mica International Research Institute Hanoi University of Science and Technology Viet Nam Faculty of Electrical and Electronics Engineering University of Transport and Communications Hanoi Viet Nam Faculty of Information Technology Viet-Hung Industrial University Hanoi Viet Nam Academy of People Security Hanoi Viet Nam

ISBN: (纸本)9781728165554

Person re-identification (ReID)-a problem of associating the images of the same person from different camera views, can be divided into two categories of multi-shot and single-shot depending on the number of images used for person representation. Numerous methods have been proposed for person ReID. However, these studies focus either on feature extraction for person representation or metric learning for person matching. Taking into account that each feature has its own representation power for each individual, in this paper, we focus on improving person ReID performance through feature fusion. For this, first we formulate multi-shot person ReID as an information retrieval problem where each person in probe is considered as a query person. Inspired the idea of query-adaptive late fusion proposed for image retrieval in [1], we propose two adaptive late fusion schemes for multi-shot person ReID. To show the robustness of the fusion schemes, we integrate them in a multi-shot person ReID framework where both hand-crafted and deep-learned features are extracted. Experimental results obtained on two benchmark datasets of PRID-2011 and iLIDS-VID show that the matching rates at rank-1 increased up to 5.65% and 14.13% on PRID-2011 and iLIDS-VID, respectively. © 2020 IEEE.

关键词： Computer vision

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PD pattern recognition in Transformers Based on Grey-scale Images and Affinity Propagation Algorithm 19

PD Pattern Recognition in Transformers Based on Grey-scale I...

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3rd international conference on Virtual and Augmented Reality Simulations (ICVARS)

作者： Wei, Bengang Huo, Kaixuan Yao, Zhoufei Lou, Jie Li, Xiangyao Shanghai Elect Power Co Elect Power Res Inst 171 Handan Rd Shanghai Peoples R China Shandong Univ Sch Elect Engn 17923 Rd Jinan Shandong Peoples R China

ISBN: (纸本)9781450365925

Partial discharge (PD) pattern recognition plays an important role in determining insulation defects and understanding insulation condition of transformers. In this paper, four PD models are set up in laboratory and pulse current method is used to measure the amplitude of apparent charge, the power frequency phase of PD pulses and the frequency of PD pulses. There are 19 feature parameters which include fractal features, moment features, and textural features are extracted form grey-scale images of phase resolved partial discharge (PRPD) patterns. In order to reduce the computational complexity of the classifier, principal component analysis (PCA) is used to reduce the dimensions of feature parameters and five new feature parameters which explain 93.50% of total variance are obtained. The kernel function and shared nearest neighbors (SNN) are used to improve affinity propagation (AP) algorithm. A classifier based on improved AP with particle swarm optimization (PSO) is established for PD pattern recognition in transformers. Based on these new feature parameters, the PD patterns are recognized by AP classifier, back propagation neural networks (BPNN) and least squares support vector machine (LSSVM). recognition rate of AP classifier is 85%

关键词： Partial discharge pattern recognition affinity propagation (AP) algorithm grey-scale images Kernel function

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