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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： Inoue, Sozo Mairittha, Tittaya Mairittha, Nattaya Hossain, Tahera Kyushu Inst Technol 1-1 Sensui Cho Tobata Kitakyushu Fukuoka 8048550 Japan

ISBN: (纸本)9781728107882

In this paper, we introduce a system that integrates activity recognition, which collects various activities of the elderly people of a nursing care facility in Japan. this system incorporates various activity levels, sensor data through smartphones, and records by nurses for a period of 4 months. there are 28 activity labels, recorded by a number of staffs/nurses. the system architecture has mobile application to encode data and a networked system in the 6-floored facility, covering a number of residents. the system is designed in such a manner that even a non-expert nurse or user can manage this system. After introducing the entire system and data collection procedure, we recognized the acitivities by exploiting some statistical features and Extremely Randomized Tree. We noticed that there are discrepancies between start and end recording time-stamps. therefore, we explored a time correction technique. the dataset is an extensive one and it can be explored for various activity recognition in smart-homes or elder care facilities. the dataset includes the sensor data from staffs' smartphones, activity labels, and care details input using the system.

关键词： Smart phones Medical services Activity recognition Cloud computing Servers Machine learning Metadata

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Foreword from the editors of the special issue

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pattern recognition and Image Analysis 2008年第4期18卷 515-517页

作者： Zhuravlev, Yu.I. Gurevich, I.B. Romanov, E.M. Niemann, H. Ivanov, V.A. Dorodnicyn Computing Center Russian Academy of Sciences Moscow Russia Mari State Technical University Yoshkar-Ola Russia Friedrich-Alexander University of Erlangen Nuremberg Germany

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Deep Learning Approach for recognition of Handwritten Kannada Numerals 8th

Deep Learning Approach for Recognition of Handwritten Kannad...

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8th international conference on Soft computing and pattern recognition (SoCPaR) / 8th international conference on Computational Aspects of Social Networks (CASoN)

作者： Ganesh, Anirudh Jadhav, Ashwin R. Pragadeesh, K. A. Cibi Vellore Inst Univ Vellore Tamil Nadu India

ISBN: (纸本)9783319606187;9783319606170

We come across a large volume of handwritten texts in our daily lives and handwritten character recognition has long been an important area of research in pattern recognition. the complexity of the task varies among different languages and it so happens largely due to the similarity between characters, distinct shapes and number of characters which are all language-specific properties. there have been numerous works on character recognition of English alphabets and with laudable success, but regional languages have not been dealt with very frequently and with similar accuracies. In this paper, we explored the performance of Convolutional Neural Networks, and Deep Belief Networks in the classification of Handwritten Kannada numerals, and conclusively compared the results obtained. the proposed method has shown satisfactory recognition accuracy in light of difficulties faced with regional languages such as similarity between characters and minute nuances that differentiate them. We can further extend this to all the Kannada characters.

关键词： Deep Belief Networks Convolutional Neural Networks LeNet pattern recognition Restricted boltzmann machine Deep learning

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Acoustic scene classification by enhanced high- frequency weak signal characteristic spectrum 8

Acoustic scene classification by enhanced high- frequency we...

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8th international conference on computing and pattern recognition, iccpr 2019

作者： Hao, Wenjie Zhao, Lasheng Zhang, Qiang Key Laboratory of Advanced Design Intelligent Computing Ministry of Education Dalian University China

ISBN: (纸本)9781450376570

In the acoustic scene classification task, the method of using melspectrogram to express the acoustic scene information is widely applied. However, mel-spectrogram has defects and it ignores important information about some acoustic scenes. the paper improves the mel-spectrogram and its generation algorithm. Including: I. For the sensitivity of the acoustic scene to highfrequency acoustic signals, the paper changes the filter design method of Mel Frequency Cepstrum Coefficient (MFCC). this method preserves more high frequency information by applying the equal-height triangular filter banks and increasing the number of the filters. II. Based on the previous step, an enhancement algorithm is proposed for the problem of the lack of highfrequency weak signals in the characteristic spectrum. the algorithm performs nonlinear mapping on the mel-spectrogram, which makes the transformed high-frequency weak signal feature information more obvious. the algorithm is verified by DCASE 2018 acoustic scene classification dataset and LITIS ROUEN dataset. the experimental results demonstrate the effectiveness of the proposed algorithm. © 2019 Association for computing Machinery.

关键词： Spectrographs

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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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Twitter user geolocation based on heterogeneous network and label propagation 8

Twitter user geolocation based on heterogeneous network and ...

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8th international conference on computing and pattern recognition, iccpr 2019

作者： Tian, Hechan Qiao, Yaqiong Liu, Yimin State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan*** China

ISBN: (纸本)9781450376570

Twitter user geolocation is applied into various applications, such as local messages recommendation and event location recognition. Existing methods do not usually utilize the potential positional correlation between heterogeneous information, resulting in low user geolocation accuracy. therefore, this paper proposes a user geolocation method based on heterogeneous network and label propagation. First, a heterogeneous network is built based on heterogeneous information mined from user tweets. then, by analyzing the factors that impact the efficiency and accuracy of label propagation, redundant nodes and edges in the initialized heterogeneous network are removed. Finally, the locations of unlabeled users are geolocated through label propagation in the simplified heterogeneous network. Experiments on the publicly available Twitter benchmark dataset show that proposed method accurately locate 57% of users within 161 km of their locations, a significant improvement over several existing methods. © 2019 Association for computing Machinery.

关键词： Heterogeneous networks

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Design and Evaluation of Reinforcement Learning Based AI Agent: A Case Study in Gaming 8th

Design and Evaluation of Reinforcement Learning Based AI Age...

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8th international conference on Soft computing and pattern recognition (SoCPaR) / 8th international conference on Computational Aspects of Social Networks (CASoN)

作者： Jayashree, P. Ramakrishnan, K. Anna Univ Dept Comp Technol Madras Inst Technol Madras 600044 Tamil Nadu India

ISBN: (纸本)9783319606187;9783319606170

Soft computing is extensively used in the field of computer games to create AI agents for computers. A case study of reinforcement learning is presented, by designing an AI agent for chopsticks game, with a probabilistic algorithm devised to make use of past game experience as its only tool to guide itself to victory. It has been experimentally verified that the AI agent's performance increases with learning and nears saturation beyond a point of learning. Constant order space and time complexity is achieved with proper design of knowledge base.

关键词： AI agent Chopsticks Reinforcement learning Probabilistic model Soft computing

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Emotion Estimation Using Body Expression Types Based on LMA and Sensitivity Analysis 8

Emotion Estimation Using Body Expression Types Based on LMA ...

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作者： Ono, Yuki Aoyagi, Saizo Yamazaki, Yoichi Yamamoto, Michiya Nagata, Noriko Kwansei Gakuin Univ Sch Sci & Technol Sanda Hyogo 6691337 Japan Toyo Univ Fac Informat Networking Innovat & Design Kita Ku Tokyo 1150053 Japan

ISBN: (纸本)9781728107882

the authors have been working on methods of estimating emotions from spontaneous body movements during a collaborative fabrication task. In this study, the authors propose a novel method of grouping emotional expressions on the basis of body movements to create type-specific emotion estimation models. We assume that there are typical expression types that are idiosyncratic characteristics for each person. We propose a method of grouping people by expression types using body movements only, based on Laban Movement Analysis (LMA) and the concept of sensitivity analysis. We perform an experiment to measure body movements and emotions during a fabrication task. Finally, we evaluate the accuracy of estimation and type-specific estimation models. the accuracy was, on average, 67.92%, which was 18% higher than that of person-independent models and superior to existing studies.

关键词： emotion recognition affective computing

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uPAD: Unsupervised Privacy-Aware Anomaly Detection in High Performance computing Systems 8th

uPAD: Unsupervised Privacy-Aware Anomaly Detection in High P...

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8th international conference on pattern recognition Applications and Methods (ICPRAM)

作者： Ghiasvand, Siavash Tech Univ Dresden Dresden Germany

ISBN: (纸本)9789897583513

Rapid growing complexity of HPC systems in response to demand for higher computing performance, results in higher probability of failures. Early detection of failures significantly reduces the damages caused by failure via impeding their propagation through system. Various anomaly detection mechanism are proposed to detect failures in their early stages. Insufficient amount of failure samples in addition to privacy concerns extremely limits the functionality of available anomaly detection approaches. Advances in machine learning techniques, significantly increased the accuracy of unsupervised anomaly detection methods, addressing the challenge of insufficient failure samples. However, available approaches are either domain specific, inaccurate, or require comprehensive knowledge about the underlying system. Furthermore, processing certain monitoring data such as system logs raises high privacy concerns. In addition, noises in monitoring data severely impact the correctness of data analysis. this work proposes an unsupervised and privacy-aware approach for detecting abnormal behaviors in general HPC systems. Preliminary results indicate high potentials of autoencoders for automatic detection of abnormal behaviors in HPC systems via analyzing anonymized system logs using fast-trainable noise-resistant models.

关键词： pattern Detection Time Series Analysis Anomaly Detection Neural Networks Noise Mitigation

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Advanced Deep Neural Networks for pattern recognition: An Experimental Study 8th

Advanced Deep Neural Networks for Pattern Recognition: An Ex...

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8th international conference on Soft computing and pattern recognition (SoCPaR) / 8th international conference on Computational Aspects of Social Networks (CASoN)

作者： Padmanabhan, Jayashree Premkumar, J. Melvin Jose Anna Univ Madras Inst Technol Madras 600044 Tamil Nadu India Google Inc Software Engn Intern Mountain View CA 94043 USA

ISBN: (纸本)9783319606187;9783319606170

Difficulty in pattern recognition is perceptible and neural networks approach the problem by way of learning from similar known patterns. Interest in Neural Networks started in the early 1980s when they were deemed to effectively model the human thought process. Speech recognition which first used Artificial Neural Networks (ANNs) to model the states of a Hidden Markov Models (HMMs) later started using Gaussian Mixture Models (GMMs). GMM-HMM systems have been the standard until recently when a new concept of Deep Neural Networks (DNNs) pre-trained using Restricted Boltzmann Machines (RBMs) came into existence. the discriminative capability of the resulting DNN is found to improve the performance of the recognition systems. the experimental work with DNN for recognizing patterns in handwriting and speech corpus has been carried out. In this work we implemented Deep Neural Networks for the above tasks and the pre trained DNN has been used for extracting bottleneck features and hereby improving the performance of the baseline systems with respect to recognition errors.

关键词： Deep Neural Networks Boltzmann machine Speech recognition Artificial Neural Networks Machine learning

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