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检索条件"任意字段=1st International Conference on Image Processing Machine Learning and Pattern Recognition"

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Graph based Unsupervised learning Methods for Edge and Node Anomaly Detection in Social Network 1

Graph based Unsupervised Learning Methods for Edge and Node ...

引用

1st IEEE international conference on Energy, Systems and Information processing, ICESIP 2019

作者： Venkatesan, M. Prabhavathy, P. National Institute of Technology Department of Computer Science and Engineering Manglore Karnataka India School of Information and Technology and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9781728104195

In the last decade online social networks analysis has become an interesting area of research for researchers, to study and analyze the activities of users using which the user interaction pattern can be identified and capture any anomalies within an user community. Detecting such users can help in identifying malicious individuals such as automated bots, fake accounts, spammers, sexual predators, and fraudsters. An anomaly (outliers, deviant patterns, exceptions, abnormal data points, malicious user) is an important task in social network analysis. The major hurdle in social networks anomaly detection is to identify irregular patterns in data that behaves significantly different from regular patterns. The focus of this paper is to propose graph based unsupervised machine learning methods for edge anomaly and node anomaly detection in social network data. © 2019 IEEE.

关键词： Anomaly detection

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Let Vehicles Talk with images for Smart Vehicular Communication System 2nd

Let Vehicles Talk with Images for Smart Vehicular Communicat...

引用

2nd international conference on Recent Trends in image processing and pattern recognition, RTIP2R 2018

作者： Santosh, K.C. Department of Computer Science The University of South Dakota 414 E Clark St. VermillionSD57069 United States

ISBN: (纸本)9789811391804

In this paper, the need for image analysis and machine learning in the domain to develop smart/intelligent vehicular communication system is succinctly addressed. Like other domains, vehicular communication has to be addressed/cited through the use of big data for several different purposes, such as the vehicle or user authentication, safety and security. Not limited to one of them, the aim of the paper is to show how can we create a panoramic image from a set of images shared by several vehicles at any specific event in the particular vehicular network. The paper is the proof-of-concept prototype to see whether images (via the use of the image- and machine learning-based tools/techniques) can help enhance the driving experience. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Authentication

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Yemeni Paper Currency Detection System 1

Yemeni Paper Currency Detection System

引用

1st international conference of Intelligent Computing and Engineering, ICOICE 2019

作者： Alnowaini, Ghazi Alabsi, Aisha Ali, Heba Taiz University Department of Mechatronic Engineering Taiz Yemen Taiz University Department of Software Engineering Taiz Yemen

ISBN: (纸本)9781728144870

With the great technological advances in colour printing, duplicating and scanning, counterfeit notes haves posed an increasing threat to currency markets all over the world. Over the past few years, the Yemeni currency market has suffered extensively from this problem. Although the Yemeni Paper Counterfeit-Currency Detection System (YPCDS) has made steps to counter this problem, it is limited to the 1000 denomination, leaving a considerable chance for counterfeiting other dominations. This paper offers a technical remedial solution to this problem for all denominations using image processing and machine learning techniques. The main aim of this system is to create an automatic system robot to recognize and detect Yemeni paper currency genuine or counterfeit. The system framework consists of five phases: Acquisition of the currency image, preprocessing, feature extraction, classification, and verification. MATLAB is the main program for creating this system. The efficacy of the system was tested experimentally using four Yemeni currency categories and the system was found to be able to detect whether currency is genuine or counterfeit with high accuracy and fast processing time. © 2019 IEEE.

关键词： image processing

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Sparse slow feature analysis for enhanced control monitoring and fault isolation 1

Sparse slow feature analysis for enhanced control monitoring...

引用

1st international conference on Industrial Artificial Intelligence, IAI 2019

作者： Shang, Chao Huang, Xiaolin Yang, Fan Huang, Dexian Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100084 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200400 China

ISBN: (纸本)9781728135939

This paper presents sparse slow feature analysis (SFA) for efficient process monitoring and fault isolation, which is a new latent variable model for time series data. We first recast sparse SFA in terms of a novel regression-Type problem, and further incorporate l-st-norm penalty into the objective in order to promote sparsity. To solve the induced nonconvex optimization problem, a tailored iterative algorithm is developed. With the sparse representation, process variables with insignificant contributions can be completely omitted, and each latent variable relates only to a fraction of crucial process variables in comparison with the generic SFA. A new process monitoring and fault isolation approach is developed based on the sparse SFA, which results in improved monitoring performance, easy-To-interpret diagnostics, and meaningful process knowledge discovery. Case studies on the Tennessee Eastman process are carried out to address the applicability of the proposed approach. © 2019 IEEE.

关键词： Process monitoring

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Facial expression classification using Cross Diagonal Neighborhood pattern

Facial expression classification using Cross Diagonal Neighb...

引用

international conference on Computer Vision and machine learning (ICCVML)

作者： Obulesu, A. Keerthi, R. Anurag Grp Inst Hyderabad India

Facial Expression recognition has significant applications in the field of Affective Computing. FER has its significant contribution in the fields like human computer interaction, neurology, psychiatry, image processing, computer vision, affective computing, and information security. This work gives unique and robust FER System by extracting unique and robust face features. This work proposes Cross Diagonal Neighborhood patterns (CDNP) for unique feature extraction. The CDNP features are further processed by Gray Level Co-occurrence Matrix (GLCM). The derived CDNP-GLCM features are sent to Convolutional Neural Network (CNN) to train various expressions.

关键词： Affective Computing Cross Diagonal Neighborhood patterns (CDNP) Convolutional Neural Network(CNN)

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Efficient statistical learning Framework with Applications to Human Activity and Facial Expression recognition

Efficient Statistical Learning Framework with Applications t...

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international conference on Advanced Technologies for Signal and image processing (ATSIP)

作者： Fatma Najar Sami Bourouis Marwan Alshar’e Roobaea Alroobaea Nizar Bouguila Ali H. Al Badi Ines Channoufi ENIT Laboratoire RISC Robotique Informatique et Systèmes Complexes Université de Tunis El Manar Tunis Tunisie ENIT Laboratoire: LR-SITI Signal Image et Technologies de 1’Information Université de Tunis E1 Manar Tunis Tunisie Taif University Taif Saudi Arabia Faculty of Computing Sciences Gulf College Muscat Oman The Concordia Institute for Information Systems Engineering (CIISE) Concordia University Montreal QC Canada Centre for Postgraduate Studies and Research Gulf College Muscat Oman

ISBN: (数字)9781728175133

ISBN: (纸本)9781728175140

In this paper, we address the problem of human activities and facial expression recognition by investigating the effectiveness of Bayesian inference methods. Indeed, a novel method termed as Bayesian learning for finite multivariate generalized Gaussian mixture model is developed. The multivariate generalized Gaussian distribution is encouraged by its ability to model a large range of data and its shape flexibility. Our main contribution in this work is to develop a Markov Chain Monte Carlo within Metropolis-Hastings algorithm for proposed generative model. In this research, we tackle also some key issues related to machine learning and pattern recognition such as the statistical model's parameters estimation. We demonstrate the merits of our developed learning framework over two challenging applications that concern human activity recognition and facial expression recognition.

关键词： Bayes methods Face recognition Visualization Mixture models Hidden Markov models Feature extraction Vocabulary

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Multidimensional wavelet neuron for pattern recognition tasks in the internet of things applications 1st

Multidimensional wavelet neuron for pattern recognition task...

引用

1st international conference on Computer Science, Engineering and Education Applications, ICCSEEA2018

作者： Vynokurova, Olena Peleshko, Dmytro Oskerko, Semen Lutsan, Vitalii Peleshko, Marta IT Step University 83a Zamarstynivs’ka str. Lviv79019 Ukraine Lviv State University of Life Safety 35 Kleparivska Lviv79000 Ukraine

ISBN: (纸本)9783319910079

Data mining and processing of Big Data is key problem in developing intelligent Internet of Things (IoT) applications. In this article, the multidimensional wavelet neuron for pattern recognition tasks is proposed. Also, the learning algorithm based on the quadratic error criterion is synthesized. This approach combines the benefits of the neural networks, the neuro-fuzzy system, and the wavelet functions approximation. The proposed multidimensional wavelet neuron can be used to solve a very large class of information processing problems for the Internet of Things applications when signals are fed in online mode from many sensors. The proposed approach is uncomplicated for computational realization and can be implemented in hardware for IoT systems. The proposed learning algorithm is characterized by a high rate of convergence and high approximation properties. © Springer international Publishing AG, part of Springer Nature 2019.

关键词： Internet of things

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Improving Multi-scale Face recognition Using VGGFace2 2nd

Improving Multi-scale Face Recognition Using VGGFace2

引用

2nd international Workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international Workshop on pattern recognition for Cultural Heritage, PatReCH 2019, 1st international Workshop eHealth in the Big Data and Deep learning Era, e-BADLE 2019, international Workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international conference on image Analysis and processing, ICIAP 2019

作者： Massoli, Fabio Valerio Amato, Giuseppe Falchi, Fabrizio Gennaro, Claudio Vairo, Claudio ISTI-CNR via G. Moruzzi 1 Pisa56124 Italy

ISBN: (纸本)9783030307530

Convolutional neural networks have reached extremely high performances on the Face recognition task. These models are commonly trained by using high-resolution images and for this reason, their discrimination ability is usually degraded when they are tested against low-resolution images. Thus, Low-Resolution Face recognition remains an open challenge for deep learning models. Such a scenario is of particular interest for surveillance systems in which it usually happens that a low-resolution probe has to be matched with higher resolution galleries. This task can be especially hard to accomplish since the probe can have resolutions as low as 8, 16 and 24 pixels per side while the typical input of state-of-the-art neural network is 224. In this paper, we described the training campaign we used to fine-tune a ResNet-50 architecture, with Squeeze-and-Excitation blocks, on the tasks of very low and mixed resolutions face recognition. For the training process we used the VGGFace2 dataset and then we tested the performance of the final model on the IJB-B dataset;in particular, we tested the neural network on the 1:1 verification task. In our experiments we considered two different scenarios: (1) probe and gallery with same resolution;(2) probe and gallery with mixed resolutions. Experimental results show that with our approach it is possible to improve upon state-of-the-art models performance on the low and mixed resolution face recognition tasks with a negligible loss at very high resolutions. © 2019, Springer Nature Switzerland AG.

关键词： Face recognition

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A study on Music Retrieval System Using image processing 1st

A Study on Music Retrieval System Using Image Processing

引用

1st international conference on Big Data Analysis and Deep learning (ICBDL)

作者： Takaoka, Emi Zin, Thi Thi Univ Miyazaki Grad Sch Engn Miyazaki Japan

ISBN: (纸本)9789811308697;9789811308680

The music retrieval system has made it possible to easily search music by simply listening to songs on machine with the spread of smartphone applications. However, it requires enormous data by voice information existing system. Therefore, we proposed new music retrieval system using images visualized by sound signal processing. In the experiment, we used 35 songs and made data for retrieval by extracting a part from them and using only melody information. As a result, the percentage of correct answers that appeared in the top 3 places was 89%, which proved that the proposed method is useful.

关键词： Music retrieval Spectrogram Chroma vector Cross-correlation

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1st international Symposium on Signal and image processing, ISSIP 2017

1st International Symposium on Signal and Image Processing, ...

引用

1st international Symposium on Signal and image processing, ISSIP 2017

ISBN: (纸本)9789811088629

The proceedings contain 21 papers. The special focus in this conference is on Signal and image processing. The topics include: Analysis of picosecond pulse for ATR using ultra-wideband RADAR;Detection of endangered gangetic dolphins from underwater videos using improved hybrid frame detection technique in combination with LBP-SVM classifier;Automatic lip extraction using DHT and active contour;early started hybrid denoising technique for medical images;intelligent tutoring by diagram recognition;Color MRI image segmentation using quantum-inspired modified genetic algorithm-based FCM;multi-verse optimization clustering algorithm for binarization of handwritten documents;3D object recognition based on data fusion at feature level via principal component analysis;Digital image watermarking through encryption and DWT for copyright protection;medav filter—Filter for removal of image noise with the combination of median and average filters;speech and non-speech audio files discrimination extracting textural and acoustic features;speaker recognition using occurrence pattern of speech signal;classification of metamaterial-based defected photonic crystal structure from band-pass filter characteristics using soft computing techniques;Sparse encoding algorithm for real-time ECG compression;wavelet based fractal analysis of solar wind speed signal;Class discriminator-based EMG classification approach for detection of neuromuscular diseases using discriminator-dependent decision rule (D3R) approach;a cooperative co-evolutionary approach for multi-objective optimization;automatic license plate recognition;s-shaped binary whale optimization algorithm for feature selection.

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