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检索条件"任意字段=International Conference of Soft Computing and Pattern Recognition"

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Benchmarking Annotation Procedures for Multi-channel Time Series HAR Dataset

Benchmarking Annotation Procedures for Multi-channel Time Se...

引用

IEEE Annual conference on Pervasive computing and Communications Workshops (PerCom)

作者： Hülya Avsar Erik Altermann Christopher Reining Fernando Moya Rueda Gernot A. Fink Michael ten Hompel Chair of Materials Handling and Warehousing TU Dortmund University Dortmund Pattern Recognition in Embedded Systems Group TU Dortmund University Dortmund

This contribution evaluates semi-automated annotation for generating high-quality data for multi-channel time series Human Activity recognition. For this purpose, time series data that consists of Optical Motion Capturing and inertial measurements from on-body devices for industrial deployment is created, annotated, and revised by four individuals. The semi-automated annotation consists of predictions from a temporal convolutional neural-network, and manual revisions for generating high-quality and fine-grained annotations for Human Activity recognition. This annotation approach reduces the annotation effort keeping similar quality as compared to manual annotation.

关键词： Pervasive computing Annotations conferences Time series analysis Manuals Optical variables measurement Activity recognition

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Real-time surface EMG pattern recognition for shoulder motions based on support vector machine 9

Real-time surface EMG pattern recognition for shoulder motio...

引用

9th international conference on computing and pattern recognition, ICCPR 2020

作者： Zhou, Yang Chen, Chaoyang Cheng, Mark Franovic, Sreten Muh, Stephanie Lemos, Stephen Wayne State University 818 W Hancock St DetroitMI United States Department of Biomedical Engineering Wayne State University DetroitMI United States Department of Orthopaedic Surgery Henry Ford Health System DetroitMI United States Orthopedic Surgery and Sports Medicine DMC DetroitMI United States

ISBN: (纸本)9781450387835

Surface electromyography (sEMG) signals contain humans' motion intentions and can be used for intuitive control of prostheses or exoskeleton. Although recent research proposes several pattern recognition methods based on sEMG and reported high accuracy, the real-time applications are still limited due to the relatively low accuracy and long time consumption. In this paper, we propose a real-time shoulder motion pattern recognition model based on surface electromyography (sEMG). The Delsys Trigno wireless EMG system with customized LabVIEW program is applied to acquire surface EMG generated by shoulder-related muscles during different shoulder motions. Surface EMG features were extracted and used to create motion recognition. Support Vector Machine (SVM) model was selected and trained for real-time motion recognition. Motion recognition results were given every 135 milliseconds. In order to evaluate the model, an experiment with four shoulder related motions was set up and conducted on five subjects. The experiment result shows that the average accuracy can reach 87.6% in offline training and 85.3% in real-time validation. © 2020 ACM.

关键词： Support vector machines

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A Review on Video Analytics Its Challenges and Applications

A Review on Video Analytics Its Challenges and Applications

引用

international conference on computing, Power and Communication Technologies (GUCON)

作者： Ansari, Shaharyar Alam Zafar, Aasim Aligarh Muslim Univ Aligarh Uttar Pradesh India

ISBN: (纸本)9789811503399;9789811503382

Nowadays the vast amount of video data is generated, which needs to be processed fast for getting correct insights into it. Manpower is not enough to process all of it in a short span of time due to their limited capacity. Video analytics facilitates useful tools to extract more valuable video data. This paper lays stress on different videos analysis activities. Here we explore various techniques which are used in video analysis along with presenting a comparison with image analysis techniques. Further, the application of video analytics along with the challenges being faced while analyzing the real time videos is discussed.

关键词： Video processing Image processing Object detection Object tracking pattern recognition Action recognition

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Real-Time KenalKayu System with YOLOv3 2nd

Real-Time KenalKayu System with YOLOv3

引用

2nd international conference on Innovative Technology, Engineering and Sciences, iCITES 2020

作者： Rosli, Nenny Ruthfalydia Khairuddin, Uswah Fathi, Muhammad Faris Nor Khairuddin, Anis Salwa Mohd Ahmad, Azlin Universiti Teknologi Malaysia Kuala Lumpur54100 Malaysia International Islamic University Malaysia Kuala Lumpur53100 Malaysia Universiti Malaya Kuala Lumpur50603 Malaysia Universiti Teknologi MARA Shah Alam Selangor40450 Malaysia

ISBN: (纸本)9783030709167

An automated tropical wood species recognition system known as KenalKayu has been developed by the Centre for Artificial Intelligence & Robotics (CAIRO) to identify the tropical wood species. The system works very well in offline mode with an accuracy rate of up to 98%. But when it comes to real-time testing, the accuracy rate dropped by about 62%, partly due to low image quality. The system was trained by using ideal quality of wood images that are stored in the database. However, during real-time testing, the quality of wood image captured might be degraded due to motion blur, out of focus and illumination. Therefore, it is challenging to perform accurate recognition via real-time approach. This research proposed an improved KenalKayu prototype by using You Only Look Once version 3 (YOLOv3) algorithm to detect and classify tropical wood species via real-time approach. 60 images from 10 tropical wood species have been trained while another 60 images have been captured and tested during real-time testing. The preliminary test shows promising results where the system is now able to classify tropical wood species in real-time mode with accuracy rate for both training set and testing set are 100%. The average accuracy rate for output probability generated by YOLOv3 is 95.63%. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： pattern recognition

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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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Adversarial Synthesis of Human Pose from Text 1

引用

42nd German conference on pattern recognition, DAGM GCPR 2020 held in parallel with 25th international Symposium on Vision, Modeling, and Visualization, VMV 2020 and 10th Eurographics Workshop on Visual computing for Biology and Medicine, VCBM 2020

作者： Zhang, Yifei Briq, Rania Tanke, Julian Gall, Juergen Computer Vision Group University of Bonn Bonn Germany Bonn-Aachen International Center for Information Technology RWTH-Aachen University Bonn Germany

ISBN: (数字)9783030712785

ISBN: (纸本)9783030712778

This work focuses on synthesizing human poses from human-level text descriptions. We propose a model that is based on a conditional generative adversarial network. It is designed to generate 2D human poses conditioned on human-written text descriptions. The model is trained and evaluated using the COCO dataset, which consists of images capturing complex everyday scenes with various human poses. We show through qualitative and quantitative results that the model is capable of synthesizing plausible poses matching the given text, indicating that it is possible to generate poses that are consistent with the given semantic features, especially for actions with distinctive poses. © 2021, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Detection of Steel Surface Defect Based on Faster R-CNN and FPN 2021

Detection of Steel Surface Defect Based on Faster R-CNN and ...

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7th international conference on computing and Artificial Intelligence, ICCAI 2021

作者： Yin, Tianping Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (纸本)9781450389501

With the development of economy, steel has been widely used by human beings in various fields of social life, for example, military products, vehicles, aerospace, high-rise buildings and daily necessities. Therefore, it is very important to get high quality steel in industrial production. Detection of steel surface defect is a vital process in steel quality inspection. Traditional method uses artificial feature to detect steel surface defect, which costs lots of time to get features, and it is not robust in new environment. Therefore, this paper proposes an efficient and accurate method to solve the problem. In this paper, we adopt the faster R-CNN algorithm as baseline to train our own model. The feature pyramid network (FPN) is added to the original network of faster R-CNN, so that the network can combine high-level feature information and low-level feature information, furthermore, we replace the region-of-interest (RoI) pooling module with RoI align to reduce quantization error, which helps the mean average precision (mAP) increase about 0.8%. We use cycle GAN to do data enhancement, which helps our network easily to converge. Another problem is that it is quite common to get extreme aspect ratio in steel surface samples, which makes the detection more difficult, we introduce a method called multi-layer RoI align to solve this problem, which makes the mAP increase about 3.2%. According to our experiment results, the proposed method has a quite good performance on detection of steel surface defect. © 2021 ACM.

关键词： Aspect ratio

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32nd international conference on Database and Expert Systems Applications, DEXA 2021

32nd International Conference on Database and Expert Systems...

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32nd international conference on Database and Expert Systems Applications, DEXA 2021

ISBN: (纸本)9783030864712

The proceedings contain 68 papers. The special focus in this conference is on Database and Expert Systems Applications. The topics include: Sarcasm Detection for Japanese Text Using BERT and Emoji;Sigmalaw PBSA - A Deep Learning Model for Aspect-Based Sentiment Analysis for the Legal Domain;BERT-Based Sentiment Analysis: A software Engineering Perspective;a Stochastic Block Model Based Approach to Detect Outliers in Networks;Medical-Based Text Classification Using FastText Features and CNN-LSTM Model;diversified pattern Mining on Large Graphs;EHUCM: An Efficient Algorithm for Mining High Utility Co-location patterns from Spatial Datasets with Feature-specific Utilities;BERT-Based Multi-Task Learning for Aspect-Based Opinion Mining;GPU-Accelerated Vertex Orbit Counting for 5-Vertex Subgraphs;subgroup Discovery with Consecutive Erosion on Discontinuous Intervals;efficient Discovery of Partial Periodic-Frequent patterns in Temporal Databases;database Framework for Supporting Retention Policies;internal Data Imputation in Data Warehouse Dimensions;purging Data from Backups by Encryption;improving Quality of Ensemble Technique for Categorical Data Clustering Using Granule computing;Online Optimized Product Quantization for Dynamic Database Using SVD-Updating;querying Collections of Tree-Structured Records in the Presence of Within-Record Referential Constraints;Dealing with Plethoric Answers of SPARQL Queries;feature Selection and software Defect Prediction by Different Ensemble Classifiers;traffic Flow Prediction Through the Fusion of Spatial-Temporal Data and Points of Interest;Fast SQL/Row pattern recognition Query Processing Using Parallel Primitives on GPUs;predicting Student Performance in Experiential Education;log-Based Anomaly Detection with Multi-Head Scaled Dot-Product Attention Mechanism;enhancing Scan Matching Algorithms via Genetic Programming for Supporting Big Moving Objects Tracking and Analysis in Emerging Environments.

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Learning with Delayed Feedback

Learning with Delayed Feedback

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international conference on pattern recognition

作者： Theivendiram Pranavan Terence Sim School of Computing National University of Singapore

We propose a novel supervised machine learning strategy, inspired by human learning, that enables an Agent to learn continually over its lifetime. A natural consequence is that the Agent must be able to handle an input whose label is delayed until a later time, or may not arrive at all. Our Agent learns in two steps: a short Seeding phase, in which the Agent's model is initialized with labelled inputs, and an indefinitely long Growing phase, in which the Agent refines and assesses its model if the label is given for an input, but stores the input in a finite-length queue if the label is missing. Queued items are matched against future input-label pairs that arrive, and the model is then updated. Our strategy also allows for the delayed feedback to take a different form. For example, in an image captioning task, the feedback could be a semantic segmentation rather than a textual caption. We show with many experiments that our strategy enables an Agent to learn flexibly and efficiently.

关键词： Image segmentation Impedance matching Semantics Machine learning pattern recognition Task analysis

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32nd international conference on Database and Expert Systems Applications, DEXA 2021

32nd International Conference on Database and Expert Systems...

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32nd international conference on Database and Expert Systems Applications, DEXA 2021

ISBN: (纸本)9783030864743

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