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检索条件"任意字段=2021 10th International Conference on Computing and Pattern Recognition"

共 1675 条记录，以下是31-40 订阅

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A Facial Expression Based Continuous Emotional State Monitoring System with GPU Acceleration

A Facial Expression Based Continuous Emotional State Monitor...

引用

10th IEEE international conference and Workshops on Automatic Face and Gesture recognition (FG)

作者： Cheng, Jinkuang Deng, Yangdong Meng, Hongying Wang, Zhihua Tsinghua Univ Inst Microelect Beijing Peoples R China Brunel Univ Sch Engn & Design Dept Elect & Comp Engn London England

ISBN: (纸本)9781467355445;9781467355452

Automatic detection and analysis of the emotional states provide important clues for predicting personal behaviors. the techniques are essential for monitoring and protecting such vulnerable people as patients with mental health conditions, persons under heavy stress, and young kids with neurodevelopmental disorders. However, few automatic emotional state recognition systems have been put into real-world applications. Among various reasons leading the lack of success, the overwhelming computing complexity is a major hurdle. In this paper, a continuously emotional state monitoring system that is capable of recognizing naturalistic facial expression in realtime is designed and implemented on a desktop PC with GPU as an accelerator. It has been tested in real scenarios and the experimental results prove promising.

关键词： affective computing dimension model of emotion facial expression pattern recognition GPU

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Classification of Body Regions Based on MRI Log Files 10th

Classification of Body Regions Based on MRI Log Files

引用

10th international conference on Computer recognition Systems (CORES)

作者： Kuhnert, Nadine Lindenmayr, Oliver Maier, Andreas FAU Erlangen Nuremberg Pattern Recognit Lab Erlangen Germany Siemens Healthcare GmbH Erlangen Germany

ISBN: (纸本)9783319591629

Every Siemens Magnetic Resonance Imaging (MRI) system consistently writes events into log files while the system is running. the log files and their contents are constantly refined by software developers. this results in different information contents depending on the software version. One information that is missing in some log files is the examined body region. As the body region is crucial for usage analysis, we used pattern recognition methods to estimate the examined body region for software versions not logging it automatically. We learned the examined body region from a set of used MRI acquisition parameters such as grid and voxel size and could classify body region information with a classification rate up to 94.7%. We compared Bayesian Network augmented Naive Bayes, Decision Trees, and Neural Networks, and found Neural Networks resulting in the best classification rate.

关键词： Classification Data mining pattern recognition Log file analysis System usage

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Automated Rehabilitation Exercise Assessment by Genetic Algorithm-optimized CNN 10

Automated Rehabilitation Exercise Assessment by Genetic Algo...

引用

Joint 10th international conference on Informatics, Electronics and Vision, ICIEV 2021 and 2021 5th international conference on Imaging, Vision and pattern recognition, icIVPR 2021

作者： Raihan, Md Johir Ahad, Md Atiqur Rahman Nahid, Abdullah-Al Electronics and Communication Engineering Discipline Khulna University Khulna9208 Bangladesh Dept. of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh

ISBN: (纸本)9781665449212

Every year, the number of motor dysfunction patients has been rising. these patients require physical therapy and continuous observation and assessment of their exercises by a professional therapist. this process can take a longer time, leading to a staff shortage and increasing financial costs. thus, a reliable rehabilitation framework is necessary to assess these exercises as precisely as possible. In this paper, we have proposed an exercise assessment framework using the 1D Local Binary pattern (LBP) to extract valuable features from skeleton data and a genetic algorithm (GA) optimized Convolutional Neural Network (CNN) to predict the score. the KIMORE dataset has been used in this study. We have achieved 0.0165 Mean Absolute Deviation (MAD) on the training set and 0. 13515 on the validation set. Contribution- We have proposed a rehabilitation exercise assessment framework based on 1D Local Binary pattern (LBP) and Genetic Algorithm (GA) optimized CNN. © 2021 IEEE

关键词： Local binary pattern

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Distributed pattern Transformation-Invariant recognition Scheme for Real-Time Sensory Applications 10

Distributed Pattern Transformation-Invariant Recognition Sch...

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10th international conference on Broadband and Wireless computing, Communication and Applications

作者： Alfehaid, Waleed M. Khan, Asad I. Srinivasan, Bala Alkhalaf, Salem Minist Interior Natl Informat Ctr Alriyadh Saudi Arabia Monash Univ Fac Informat Technol Clayton Vic Australia Qassim Univ Dept Comp Coll Arts & Sci Alrass City Saudi Arabia

ISBN: (纸本)9781467383158

this paper describes the Cellular weighted Graph Neuron (CwGN) scheme, which demonstrates real-time transformation-invariant pattern recognition for resource constraint networks such as wireless sensor networks (WSN). this scheme can recognise patterns from a variety of perspectives in a fully distributed manner, which makes it highly responsive, scalable, and energy efficient. Learning operations are decoupled and reduced to relatively simple processing elements to achieve an economical parallelism. We show that by using only local computation, i.e., between adjacent nodes, we can fully describe patterns by their boundaries and then recognise the transformations of these patterns. Our scheme solves the invariant pattern recognition for the first time in a fully distributed manner within a predictable single cycle learning duration which allows the use of the scheme within resource-constrained networks such as wireless sensor networks for event-detection. theoretical analysis and experimental results demonstrate online recognition of transformed patterns with competitive accuracy and a low energy budget.

关键词： pattern recognition (PR) Artificial Intelligence (AI) Graph Neuron (GN) Wireless Sensor Networks (WSN)

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CNN-NSVM Architecture for Skin Lesion Classification Using Non-Dermoscopic Digital Image 10

CNN-NSVM Architecture for Skin Lesion Classification Using N...

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Joint 10th international conference on Informatics, Electronics and Vision, ICIEV 2021 and 2021 5th international conference on Imaging, Vision and pattern recognition, icIVPR 2021

作者： Kabir, Md Mohsin Mehera, Parisa Saha, Aloke Kumar Mridha, M.F. Hamid, Md Abdul Monowar, Muhammad Mostafa Department of Computer Science and Engineering Bangladesh University of Business & Technology Dhaka Bangladesh Department of Computer Science and Engineering University of Asia Pacific Dhaka Bangladesh Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

ISBN: (纸本)9781665449212

Skin cancer has become a severe problem for medical diagnosis. the adoption of Artificial Intelligence (AI) in pharmaceutical diagnosis is constantly improving. Lately, AI-based computer-aided diagnostics explications for the diagnosis of skin disease have been of prominent concern. Notwithstanding its importance, skin lesion segmentation inhabits an unresolved difficulty of variability in shade, texture, patterns, and obscure borders. Melanoma, precisely called malignant melanoma, is the deadliest kind of skin cancer. It is much more spread to other body organs if it remains undiagnosed and is not treated early. Hence, early screening has the utmost importance in enhancing the cure probability. the proposed algorithms significantly impacted skin cancer classification until today, but they decayed classification rates while using Non-Dermoscopic Digital Images. this paper presents a new approach of classifying skin lesions from non-dermoscopic digital images using Convolutional Neural Network and Neutrosophic Logic Support Vector Machine (CNN-NSVM) combined approach. CNN extracts the features from the images, and the neutrosophic logic employed with SVM classifies the skin lesion types. the proposed methodology is evaluated on the well-known malignant lesion image dataset from the digital image archive of the Department of Dermatology of the University Medical Center Groningen (UMCG). the obtained accuracy of the proposed algorithm is 91% which outperformed MED-NODE by 10% on a similar dataset. Contribution-the paper introduced a skin cancer classification method from non-dermoscopic digital images based on Convolutional Neural Network and Neutrosophic Logic SVM (CNN-NSVM) to classify skin lesions into two types: Melanoma and Naevus. © 2021 IEEE

关键词： Support vector machines

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An Ensemble of Weak Classifiers for pattern recognition in Motion Capture Clouds of Points 10th

An Ensemble of Weak Classifiers for Pattern Recognition in M...

引用

10th international conference on Computer recognition Systems (CORES)

作者： Luis Jimenez-Bascones, Juan Grana, Manuel STT Syst Mot Capture Unit San Sebastian Spain Univ Basque Country UPV EHU Computat Intelligence Grp Leioa Spain

ISBN: (纸本)9783319591629

this paper deals with the problem of labeling a cloud of points as a classification problem, proposing an ensemble of weak classifiers. First, we define a set of geometrical features over small subsets of the cloud of points. then, we apply an Adaboost like strategy to select a collection of features achieving a target accuracy in the detection of correct labeling as a whole. Furthermore, we use these features to generate the labeling of the points in the cloud. We demonstrate the approach on a real dataset obtained from the measurement of gait motion of persons, for which the ground truth labeling has been carried out manually. Results are encouraging, achieving high accuracy in both tasks (correct label detection and label generation) at a reduced computational cost.

关键词： Adaptive boosting

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Alignment Free Sequence Similarity Estimation using Local Binary pattern on DNA Trajectory Images 10

Alignment Free Sequence Similarity Estimation using Local Bi...

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Joint 10th international conference on Informatics, Electronics and Vision, ICIEV 2021 and 2021 5th international conference on Imaging, Vision and pattern recognition, icIVPR 2021

作者： Hossain, Syed Nayem Kabir, Md Hasanul Pal, Arnab Dhaka Bangladesh Gazipur Bangladesh

ISBN: (纸本)9781665449212

Graphical representation of DNA sequences is a popular alignment-free sequence comparison technique for predicting gene families, discovering evolutionary relations, finding similar regions among protein structures, and reconstructing phylogenetic trees. Here, we proposed an alignment-free sequence comparison method, where DNA sequences were represented as two-dimensional trajectories in images. these images contain information of multi-level variation embedded in the trajectories. A micro-texture encoding scheme named Local Binary pattern (LBP) was applied to these images for generating alignment-free (AF) feature descriptors. three distance measures were applied to these AF descriptors and using these distance scores, phylogenetic trees were reconstructed. then, the results were compared with phylogenetic trees produced by binary image-based representation method. It was revealed that the sequence comparison capability of LBP based DNA trajectory image representation is more accurate than binary-based DNA image representation system. It was also found that cosine distance is the most appropriate measure for calculating distance among AF descriptors during phylogenetic tree reconstruction. In addition, the accuracy of our method was also compared with some state of the art alignment-free sequence comparison algorithms. Contribution-In this paper, Local Binary pattern (LBP) encoding scheme was applied on DNA trajectory images to capture multi-level variation, and accordingly to generate an alignment-free descriptor for measuring the similarities among DNA sequences. © 2021 IEEE

关键词： Bioinformatics

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Research on the identification of software behavior in anomaly detection 10

Research on the identification of software behavior in anoma...

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10th international conference on Innovative Mobile and Internet Services in Ubiquitous computing (IMIS)

作者： Yang, Jun Du, Xiangping Zhou, Liang Shan, Songling Cui, Baojiang Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Peoples R China Natl Engn Lab Mobile Network Secur Beijing Peoples R China China Elect Power Res Inst Beijing Peoples R China

ISBN: (纸本)9781509009848

Anomaly detection is an important component of computer security defense. As the security situation becomes increasingly severe, abnormality is detected by the sequential pattern mining has become a hot research topic. For normal behavior to identify in anomaly detection, proposed software behavior pattern recognition technology which based on improved prefixspan algorithms and sequence similarity comparison, mainly for software behavior java program under windows platform identification. the method uses sequential pattern mining to model the software behavior of the sample program, Extraction behavior of the software in the training data based on system call sequence support, and established pattern library to represent the outline of a software behavior. In the detection phase, compare the program library in the normal sequence mode with the current pattern to be detected and calculate sequence similarity, identify the target behavior. In this paper, the method has good performance in terms of software behavior recognition. Finally, the software behavior in network behavior, given the specific experimental procedures, demonstrate the effectiveness of this method.

关键词： Software behavior sequence pattern behavior recognition improved prefixspan

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Foreground and Shadow Segmentation by Exploiting Multiple Cues

Foreground and Shadow Segmentation by Exploiting Multiple Cu...

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3rd international Workshop on e-Activity (IWEA2009)/10th ACIS international conference on Software Engineering Artificial Intelligence, Networking and Parallel/Distributed computing

作者： Gao Junxiang Zhang Hao Liu Yong Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China

ISBN: (纸本)9780769536422

To segment foreground objects and moving shadows in visual surveillance environment, this paper proposes an algorithm by exploiting color information, illumination invariants and spatial information. the presence of a shadow is first hypothesized with simple evidence that shadows darken the surface which they are cast upon. Derivatives of illumination invariants are then used to classify the potential shadow pixels extracted in previous step. To increase the accuracy of shadow detection, two types of spatial analysis are designed to verify actual shadow pixels. Experimental results show that the proposed algorithm can detect moving shadow effectively on indoor and outdoor video sequences. the performance of the method is considerably higher than that of the two well-known shadow detection methods, and it is robust against changing illumination.

关键词： pattern recognition moving shadow detection intelligent video surveillance

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Universal Cellular computing on the Edge of Chaos 10

Universal Cellular Computing on the Edge of Chaos

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10th international conference on Modern Circuits and Systems Technologies (MOCAST)

作者： Slavova, Angela Ignatov, Ventsislav Bulgarian Acad Sci Inst Math & Informat Sofia 1113 Bulgaria Bulgarian Acad Sci Sofia 1000 Bulgaria

ISBN: (纸本)9781665418478

In this work we present a special class of memristor Cellular Nonlinear/Nanoscale Networks (MCNN) operating on the edge of chaos. We study the dynamics of Gierer-Menhardt system for pattern formation in living cells. We determine three regions in the parameter set: locally active and stable, locally active and unstable and locally passive. throughout extensive simulations we obtain non-uniform spatial-pattern generation of MCNN model in the edge of chaos domain. Applications in EEG signal generation are shown in order to predict the epileptic seizures.

关键词： Cellular Nonlinear/Nanoscale Networks memristor edge of chaos pattern generation EEG signal generation

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