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检索条件"任意字段=Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning"

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Facial Emotion Prediction through Action Units and Deep learning

Facial Emotion Prediction through Action Units and Deep Lear...

引用

Advancements in Computing (ICAC), 2019 international conference on

作者： Madhuka Nadeeshani Akash Jayaweera Pradeepa Samarasinghe Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (数字)9781728184128

ISBN: (纸本)9781728184135

With the recent advancements in deep learning techniques, attention has been given to training and testing facial emotions through highly complex deep learning systems. In this paper we apply machine learning techniques which require less resources to produce comparable results for emotion prediction. As the underlying technique for the emotion prediction in this research is based on clinically recognized Facial Action Coding System (FACS), a further analysis is given on the contribution of each of the Action Units (AUs) for the predicted emotion. This analysis would complement, strengthen and be a main resource for addressing many different health issues related to facial muscle movements.

关键词： Deep learning Gold Analytical models Computational modeling Graphics processing units Predictive models Encoding

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Solar Cell Defect Recognition based on Orthogonal learning Strategy 19

Solar Cell Defect Recognition based on Orthogonal Learning S...

引用

proceedings of the 2019 2nd international conference on Digital Medicine and Image processing

作者： Xiaoyu Song Qianqian Qi Zhili Chen Shenyang Jianzhu University Shenyang City Liaoning Province China

ISBN: (纸本)9781450376983

The quality of silicon wafers is an important factor restricting the efficiency and service life of photovoltaic power generation. In order to inspect the quality of silicon wafers, a defect recognition method based on orthogonal learning strategy is proposed where support vector machine is combined with binary tree for multi-class classification. Firstly, the adaptive threshold is set to remove the raster lines in the original image, and Fourier reconstruction image is used to enhance the defect. After that, we extract the image features. With the help of orthogonal learning strategy, an orthogonal array of feature data is established to implement the initial defect classification, and the classification results are analyzed by factor analysis. The extracted features are sorted according to their influence degree, and the improved support vector machine is used to classify the feature data accumulated one by one. Finally, genetic algorithm and grid search are introduced to optimize the parameters. The recognition accuracy of the designed classifier is up to 96.6%. The experimental results indicate the effectiveness of the proposed method.

关键词： Feature selection Orthogonal learning strategy Parameter optimization Solar cell Defect recognition

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Deep learning-based visual inspection for the delayed brittle fracture of high-strength bolts in long-span steel bridges 2

Deep learning-based visual inspection for the delayed brittl...

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2nd international conference on Image and Video processing, and Artificial Intelligence (IPVAI)

作者： Zhou, Jing Huo, Linsheng Song, Gangbing Li, Hongnan Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116024 Peoples R China Univ Houston Dept Mech Engn Smart Mat & Struct Lab Houston TX 77204 USA

ISBN: (数字)9781510634107

ISBN: (纸本)9781510634107

The delayed brittle fracture of high-strength bolts in long-span steel bridges threatens the safety of the bridges and even lead to serious accidents. Currently, human periodic inspection, the most commonly applied detection method for this kind of high-strength bolts damage, is a dangerous process and consumes plenty of manpower and time. To detect the damage fast and automatically, a visual inspection approach based on deep learning is proposed. YOLOv3, an object detection algorithm based on convolution neural network (CNN), is introduced due to its good performance for the detection of small objects. First, a dataset including 500 images labeled for damage is developed. Then, the YOLOv3 neural network model is trained by using the dataset, and the capability of the trained model is verified by using 2 new damage images. The feasibility of the proposed detection method has been demonstrated by the experimental results.

关键词： You Only Look Once bolted connection high-strength bolt delayed fracture structural damage detection object detection deep learning

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Joining geometric and RGB features for RGB-D semantic segmentation 2

Joining geometric and RGB features for RGB-D semantic segmen...

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2nd international conference on Image and Video processing, and Artificial Intelligence (IPVAI)

作者： Zhang, Shaopeng Zhong, Min Zeng, Gang Gan, Rui Peking Univ Sch Math Sci Beijing Peoples R China Peking Univ Minist Educ Key Lab Machine Percept Beijing Peoples R China

ISBN: (数字)9781510634107

ISBN: (纸本)9781510634107

Depth map is a regular format of geometric data structure. Some approaches attempted to harness point cloud from depth channel to extract 3D features and demonstrated the superiority over traditional 2.5D representation approaches. However, how to add 3D features to the pixel on RGB frames in order to incorporate geometric information is a challenging task. In this paper, we propose a simple and general framework combining geometric information of depth maps and RGB information of color maps for semantic segmentation task. Specifically, we first extract geometric feature from an associated point cloud which is harnessed from depth map, and then the RGB feature from color map. Due to the regular format of depth map, the geometric feature can be easily mapped to the corresponding pixel on RGB feature. After that, we get a combination of RGB and geometric features with our Element-Max-Min-Fuse function. Additionally, an efficient decoder module is designed to refine the segmentation results. We demonstrate the effectiveness of the proposed model on S3DIS dataset, the experimental results show that our method enhances the result of using RGB image or point cloud alone.

关键词： deep learning sementic segmentation feature fuse

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learning Robust 3D Face Reconstruction and Discriminative Identity Representation 2

Learning Robust 3D Face Reconstruction and Discriminative Id...

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2nd IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Luo, Yao Tu, Xiaoguang Xie, Mei Univ Elect Sci & Technol China Sch Commun & Informat Engn Chengdu Peoples R China

ISBN: (纸本)9781728151021

3D face reconstruction from a single 2D image is a very important topic in computer vision. However, the current reconstruction methods are usually non-sensitive to face identities and over-sensitive to facial poses, which may result in similar 3D geometries for faces of different identities, or obtain different shapes for the same identity with different poses. When such methods are applied practically, their 3D estimates are either changeable for different photos of the same subject or over-regularized and generic to distinguish face identities. In this paper, we propose a robust solution to solve this problem by carefully designing a novel "Siamese" Convolutional Neural Network (SCNN). Specifically, regarding the 3D Morphable face Model (3DMM) parameters of the same individual as the same class, we employ the contrastive loss to enlarge the interclass distance and meanwhile reduce the intra-class distance for the output 3DMM parameters. We also propose an identity loss to preserve the identity information for the same individual in the feature space. Training with these two losses, our SCNN could learn representations that are more discriminative for face identity and generalizable for pose variants. Experiments on the challenging database 300W-LP and AFLW2000-3D have shown the effectiveness of our method by comparing with state of the arts.

关键词： 3D face reconstruction 3DMM parameters siamese convolutional neural network

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2nd international conference on Smart Innovations in Communications and Computational Sciences ICSICCS-2018

2nd International Conference on Smart Innovations in Communi...

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2nd international conference on Smart Innovations in Communications and Computational Sciences ICSICCS-2018

ISBN: (纸本)9789811324130

The proceedings contain 47 papers. The special focus in this conference is on Smart Innovations in Communications and Computational Sciences. The topics include: Computer Vision-Based Fruit Disease Detection and Classification;Automated Brain Tumor Detection Using Discriminative Clustering Based MRI Segmentation;cancer Prediction Based on Fuzzy Inference System;energy Consumption Data Analysis and Operation Evaluation of Green Buildings;the Method of Random Generation of Electronic Patrol Path Based on Artificial Intelligence;Channel Power Estimation for DVBRCS to DVBS2 Onboard DSP Payload;a Slotted Microstrip Patch Antenna for 5G Mobile Phone Applications;reliability Study of Sensor Node Monitoring Unattended Environment;RSOM-Based Clustering and Routing in WSNs;real-Time Meta learning Approach for Mobile Healthcare;Evaluation of Received signal Strength Indicator (RSSI) for Relay-Based Communication in WBAN;side-Channel Attacks on Cryptographic Devices and Their Countermeasures—A Review;implementation and Analysis of Different Path Loss Models for Cooperative Communication in a Wireless Sensor Network;Parallel Approach for Sub-graph Isomorphism on Multicore System Using OpenMP;optimized Solution for Employee Transportation Problem Using Linear Programming;spectrum Prediction Using Time Delay Neural Network in Cognitive Radio Network;Reliability Factor Based AODV Protocol: Prevention of Black Hole Attack in MANET;a Survey of Lightweight Cryptographic Algorithms for IoT-Based Applications;fruit Disease Detection Using Rule-Based Classification;Fault Tolerance Through Energy Balanced Cluster Formation (EBCF) in WSN;firearm Detection from Surveillance Cameras Using Image processing and machine learning Techniques;Mining Social Networks: Tollywood Reviews for Analyzing UPC by Using Big Data Framework;cloud-Based E-learning: Using Cloud Computing Platform for an Effective E-learning.

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Self-Quotient Image based CNN: A Basic Image processing assisting Convolutional Neural Network 2019

Self-Quotient Image based CNN: A Basic Image Processing assi...

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3rd international conference on Digital signal processing (ICDSP) / 2nd international conference on Computer Graphics and Virtuality (ICCGV)

作者： Xing, Xingrun Dong, Min Bi, Cheng Yang, Lin Zhengzhou Univ Sch Informat Engn 100 Kexue Rd Zhengzhou Henan Peoples R China Henan Bldg Mat Res & Design Inst Co Ltd 34 Hongqi Rd Zhengzhou Henan Peoples R China

ISBN: (纸本)9781450362047

The Convolutional Neural Networks (CNNs) are able to learn basic and high level features hierarchically with the highlight that it implements an end-to-end learning method. However, lacking in the ability to utilize prior information and domain knowledge has led to the neural networks hard to train. In this paper, a method using prior information is proposed, which is by appending prior feature-maps through a bypass input structure. As an implementation, we evaluate a convolutional neural network integrating with the Self-Quotient Image (SQI) algorithm. Through the bypass, we import the feature-maps from the SQI algorithm and concat them with the output of the first convolution layer. With the help of traditional image processing methods, CNNs can directly improve the accuracy and training stability, while the bypass is exactly a consistent point. Finally, the necessity of this bypass pattern is that it avoids the direct modification of original images. As CNNs are able to focus on far richer features than basic image processing methods, it is advisable for us to expose CNNs to the original data. It is exactly the main design idea that we make the output from synergistic processing algorithm bypass from the side.

关键词： Bypass convolution features-map neural network prior information self-quotient image

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ACM international conference Proceeding Series

ACM International Conference Proceeding Series

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2nd international conference on Control and Computer Vision, ICCCV 2019

ISBN: (纸本)9781450363228

The proceedings contain 26 papers. The topics discussed include: railway signal interlocking logic simulation system;using machine vision to command a 6-axis robot arm to act on a randomly placed zinc die cast product;a robust and effective tracking method in remote sensing video sequences;an approach to real time parking management using computer vision;recognizing faces in shades of gray;multi-focus color image fusion using Laplacian filter and discrete Fourier transformation with qualitative error image metrics;Chinese rubbing image binarization-based on deep learning for image denoising;controlling software evolution process using code smell visualization;modified watershed transform for automated brain segmentation from magnetic resonance images;and research on the fast image motion compensation technology of aerial digital camera.

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learning Assistant to Acquire the Fundamental Language Skills for Non-Native Learners using AI

Learning Assistant to Acquire the Fundamental Language Skill...

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Advancements in Computing (ICAC), 2019 international conference on

作者： Praveenkumar Srikanthan Roshan Nizar Abishaan Ravikumar Keerthigan Lalitharan S.M.B. Harshanath Jesuthasan Alosius Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9781728184128

ISBN: (纸本)9781728184135

The ability to speak and learn a language properly requires good practice, experience and good learning strategies but the existing solutions do not provide proper guidance to learn a language with instant feedback. This research is an approach to devise an improved language learning assistant with practices that will help to improve the fundamental language skills for non-native learners and children who are in the early stage of their education. The four main skills focused on this application will be conversation, pronunciation, listening and grammatical skills. The implementation of this research is done by using technologies like natural language processing, machine learning, and deep learning approaches to come up with components to train the learner. The solution of this research is delivered by using a cross-platform application called GLIB which facilities to improve all the English language skills mentioned above along with guides, tips, practices, and feedback based on an evaluation to improve the English language.

关键词： Deep learning Education Natural language processing

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Chart Classification Using Simplified VGG Model 26

Chart Classification Using Simplified VGG Model

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26th international conference on Systems, signals and Image processing (IWSSIP)

作者： Bajic, Filip Job, Josip Nenadic, Kresimir Univ Comp Ctr SRCE Josipa Marohnica 5 Zagreb 10000 Croatia Fac Elect Engn Comp Sci & Informat Technol Kneza Trpimira 2B Osijek 31000 Croatia

ISBN: (纸本)9781728132273

Data visualization is created due to the need to show vast amount of data in a more transparent way. Data visualization often contains key information that is not listed anywhere in the text and allows the reader to find out important information and longer-term memory. On the other hand, Internet search engines have a problem with filtering data visualization and associating visualization and query that the user has entered. With the use of data visualization, all blind people and people with impaired vision are left off. This paper uses machine learning for classifying charts into 10 categories. The total accuracy achieved across all categories is 81.67%.

关键词： chart image classification chart recognition convolutional neural networks visualization

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