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1st international conference on 'Mechatronics and Artificial Intelligence' (ICMAI-2021)

Journal of Physics: conference Series 2021年第1期1950卷

PREFACE It was a matter of pleasure to organize 1st international conference on 'Mechatronics and Artificial Intelligence' (ICMAI-2021) at Shree Guru Gobind Singh Tricentenary (SGT) University, Gurugram on 26th and 27th February 2021. The conference was organized in Association of Engineering & Technocrats, Faculty of Engineering and Technology, SGT University. The objective of this conference was to provide a common platform to scientists and researchers from all over India for exchanging their knowledge and views to deal with global challenges. The theme of this conference was not only the fundamental technology in the field of engineering science but also can change our society by exploring new horizons and continuous progress. A diverse range of topics from the neural network, digital image processing, machine learning, programmable logic controller, health care, IoT based systems, deep learning, lean manufacturing, abrasive flow machining, welding technology and simulation-based studies are featured in the conference. In addition to that, you will also hear about the incremental forming, automation integration techniques, wireless sensor techniques, non-invasive methods of diagnosis, robot assembly, data mining and various evolutionary algorithms in different applications. I hope, therefore, that you will get a chance to explore recent emerging technologies. We are delighted to deliver special thanks to IOP Publishing as our publication partner to publish the conference proceedings in JPCS. We sincerely thanks to our sponsors Council of Scientific and Industrial Research (CSIR) Human Resource Development Group (HRDG) and 'Sanranchana', SGT University Center of Research Innovation. We believe that the conference had provided the opportunity to the students and the young researchers for enriching their knowledge through interaction with eminent researchers from various NITs, IITs, state universities and reputed organizations. We hope that everybody had a great ex

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2018 1st international conference on Multimedia Analysis and pattern recognition, MAPR 2018 - Proceedings

2018 1st International Conference on Multimedia Analysis and...

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1st international conference on Multimedia Analysis and pattern recognition, MAPR 2018

ISBN: (纸本)9781538641804

The proceedings contain 15 papers. The topics discussed include: a vision based method for automatic evaluation of germination rate of rice seeds;towards real-time smile detection based on faster region convolutional neural network;Nissl stained rat brain cell image analysis post exposure to electromagnetic fields using image processing techniques;multivariate filter for saliency;measuring crowd collectiveness with trajectory smoothing;multi-task regression learning for prediction of response against a panel of anti-cancer drugs in personalized medicine;landmarks detection by applying deep networks;improving human action recognition with two-stream 3D convolutional neural network;heritage image classification by convolution neural networks;enhancing person re-identification based on recurrent feature aggregation network;document layout analysis: a maximum homogeneous region approach;discriminative deep feature learning for facial emotion recognition;blind deconvolution method using omnidirectional Gabor filter-based edge information;and algorithm for hiding high utility sensitive association rule based on intersection lattice.

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Ishara-Bochon: The First Multipurpose Open Access Dataset for Bangla Sign Language Isolated Digits 2nd

Ishara-Bochon: The First Multipurpose Open Access Dataset fo...

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

作者： Islam, Md. Sanzidul Mousumi, Sadia Sultana Sharmin Jessan, Nazmul A. Rabby, Akm Shahariar Azad Abujar, Sheikh Hossain, Syed Akhter Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9789811391804

Ishara-Bochon, the first multipurpose comprehensive open access isolated digits dataset for Bangladeshi Sign Language (BdSL) is introduced in this article. Sign language digits is a major part of communication for deaf and hearing impaired people. It is important in their everyday stuff, such as – in routine accounting and to interact with general people. Ishara-Bachon dataset contains 100 sets of 10 Bangla basic sign digits (0, 1, 2.. 9), collected from different deaf and general volunteers from different institutes. After discarding maximum errors and performing different preprocessing methods, 1000 images of Bangla sign language isolated digits were included in the first dataset. After collectiong raw data, some effective preprocessing methods were performed for making those data useable for computer vision model and any other application development purposes. © 2019, Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Healthy Vessel Wall Detection Using U-Net in Optical Coherence Tomography 1st

Healthy Vessel Wall Detection Using U-Net in Optical Coheren...

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1st international Workshop on machine learning and Medical Engineering for Cardiovasvular Healthcare, MLMECH 2019, and the 8th international Joint Workshops on Computing and Visualization for Intravascular Imaging and Computer Assisted stenting, CVII-stENT 2019, held in conjunction with 22nd international conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2019

作者： Liu, Shengnan Shamonin, Denis P. Zahnd, Guillaume van der steen, A.F.W. van Walsum, Theo van Soest, Gijs Department of Biomedical Engineering Erasmus MC Dr. Molewaterplein 40 Rotterdam3015 GD Netherlands Medical Image Processing Department Leiden University Medical Center Leiden Netherlands Computer Aided Medical Procedures Technische Universität München Boltzmannstraße 3 Garching bei München85748 Germany Biomedical Imaging Group Rotterdam Department of Medical Informatics and Radiology Erasmus MC Dr. Molewaterplein 40 Rotterdam3015 GD Netherlands

ISBN: (纸本)9783030333263

Intravascular optical coherence tomography can be applied for high-resolution imaging in the coronary arteries with ischemic heart disease. The differentiation of the healthy and diseased vessel wall can be used to assess the extent and severity of coronary artery disease, and to guide therapeutic interventions. The aim of this study is to develop a recognition framework that can be potentially used for real-time intraoperative application. structures in an image were labeled into five categories: diseased, healthy, luminal, guide-wire, and others. A U-net was implemented to directly take Cartesian images as input without any additional processing steps. A sigmoid activation and binary cross-entropy loss were applied to perform multi-labeling segmentation. Three transformations were specifically proposed in the polar domain for data augmentation. For evaluation of the proposed framework, 200 images from 20 patients were used and a triple-leave-2-out cross validation was carried out. Performance was evaluated using the average loss in the validation dataset, and the Dice scores were reported as well. Results showed that the proposed framework can perform the segmentation generally with an average performance of 0.88 ± 0.02 in Dice scores. These preliminary results suggest that the proposed framework can be potentially applied for assisting diagnosis in real-time. In the future, we intend to include more data, also take into consideration artifacts such as bad flushing, more deformed lumen, and side-branches. © 2019, Springer Nature Switzerland AG.

关键词： Optical tomography

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Analysis Of images Obtained By Unmanned Aerial Vehicle By Deep learning Methods

Analysis Of Images Obtained By Unmanned Aerial Vehicle By De...

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international Informatics and Software Engineering conference (UBMYK)

作者： Özgür Kutlu Önder Demir Barış Doğan Fen Bilimleri Enstitüsü Marmara Üniversitesi İstanbul Türkiye Teknoloji Fakültesi Bilgisayar Mühendisliği Marmara Üniversitesi İstanbul Türkiye Teknoloji Fakültesi Mekatronik Mühendisliği Marmara Üniversitesi İstanbul Türkiye

In artificial intelligence applications, many sub-methods such as machine learning, artificial neural networks, classification, clustering algorithms are used. One of these methods is deep learning. Deep learning is an advanced machine learning class. Using the Deep learning method, video analysis, image classification, speech recognition and natural language processing are very successful. The data and experiences to be provided by the projects covering Deep learning and Unmanned Aerial Vehicles will increase the number of qualified studies on these issues and contribute to the development of high value-added products in these technologies. In this study, a control software that evaluates image data from unmanned aerial vehicles and makes various inferences (classification, positioning, marking) is created. By using the method of retraining the last layers of the pre-trained artificial neural network models with our data set, it has been tried to reduce the training time and increase the success. In these studies, 2 pre-educated models were used and as a result of training of these models, as a result of 190 thousand steps of training, 25.39 and 27.87 mAP values were reached.

关键词： Unmanned aerial vehicles Deep learning Google Artificial neural networks Training

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The Effectiveness Research of Different Methods of a user Identification in the Noisy Channel Case

The Effectiveness Research of Different Methods of a user Id...

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international conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： A.V. Pashentsev V.V. Vedishchev Lipetsk State Technical University Lipetsk Russia

Nowadays the biometric identification is a part of our life and there are reasons for determining the possibility of applying different methods of a user identification in noisy channel case by using the machine learn... 详细信息

ISBN: (纸本)9781728149127

关键词： Faces machine learning Cluster computing Convolution Convolutional neural networks Feature extraction

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Enhancing Person Re-identification based on Recurrent Feature Aggregation Network 1

Enhancing Person Re-identification based on Recurrent Featur...

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1st international conference on Multimedia Analysis and pattern recognition (MAPR)

作者： Quan Nguyen Hong Thuy-Binh Nguyen Thi-Lan Le Hanoi Univ Sci & Technol Sch Elect & Commun Hanoi Vietnam Hanoi Univ Sci & Technol MICA Int Res Inst CNRS UMI2954 Grenoble INP Hanoi Vietnam Viet Hung Ind Univ Fac Informat Technol Hanoi Vietnam Univ Transport & Commun Fac Elect & Elect Engn Hanoi Vietnam

ISBN: (纸本)9781538641804

This paper proposes a method for video-based person re-identification. Motivated by the capacity of Recurrent Feature Aggregation Network (RFA-Net) that allows to aggregate the image-level features over time-steps and to yield a sequence-level feature. Our work improves the person re-identification performance by (1) proposing to use Gaussian of Gaussian (GOG) - a more powerful feature at image level instead of LBP-Color as in the original work and (2) applying metric learning techniques for person matching. To analyze the role of image feature and metric learning, we have performed extensive experiments on two public benchmark datasets including PRID 2011 and iLIDS-VID. The proposed solution obtains impressive results on the PRID 2011 dataset. The matching rate at rank 1 is increased by 16.8% compared to that of original method. For the iLIDS-VID dataset, the matching rate at rank 1 of proposed method is increased 1.1% whereas these values at the latter ranks are higher than those of the original method by approximately 4%. In additional, the mean and standard deviation of the rank where the correct matching is found of the proposed method on the PRID 2011 and the iLIDS-VID are 2.32, 4.33 and 4.61, 7.67 while those of the original work are 3.50, 5.42 and 8.03, 17.52, respectively. These obtained results emphasize the robustness of the proposed method compared to the original one.

关键词： Feature extraction Measurement image color analysis Cameras Robustness Data mining image sequences image colour analysis image sequences Cameras Feature extraction data mining Persons Robustness image feature Dataset Gauss Rank of data

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Hand Gesture Controlled Drones: An Open Source Library 1

Hand Gesture Controlled Drones: An Open Source Library

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1st international conference on Data Intelligence and Security (ICDIS)

作者： Natarajan, Kathiravan Nguyen, Truong-Huy D. Mete, Mutlu Texas A&M Univ Commerce Dept Comp Sci Commerce TX 75429 USA Fordham Univ Dept Comp & Informat Sci Bronx NY 10458 USA Texas A&M Univ Commerce Grad Sch Commerce TX USA

ISBN: (纸本)9781538657621

Drones are conventionally controlled using joysticks, remote controllers, mobile applications, and embedded computers. A few significant issues with these approaches are that drone control is limited by the range of electromagnetic radiation and susceptible to interference noise. In this study we propose the use of hand gestures as a method to control drones. We investigate the use of computer vision methods to develop an intuitive way of agent-less communication between a drone and its operator. Computer vision-based methods rely on the ability of a drones camera to capture surrounding images and use pattern recognition to translate images to meaningful and/or actionable information. The proposed framework involves a few key parts toward an ultimate action to be taken. They are: image segregation from the video streams of front camera, creating a robust and reliable image recognition based on segregated images, and finally conversion of classified gestures into actionable drone movement, such as takeoff, landing, hovering and so forth. A set of five gestures are studied in this work. Haar feature-based AdaBoost classifier [1] is employed for gesture recognition. We also envisage safety of the operator and drone's action calculating the distance based on computer vision for this task. A series of experiments are conducted to measure gesture recognition accuracies considering the major scene variabilities, illumination, background, and distance. Classification accuracies show that well-lit, clear background, and within 3 ft gestures are recognized correctly over 90%. Limitations of current framework and feasible solutions for better gesture recognition are discussed, too. The software library we developed, and hand gesture datasets are open-sourced at project website.

关键词： Drone image processing machine learning machine vision

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Nissl stained Rat Brain Cell image Analysis Post Exposure to Electromagnetic Fields Using image processing Techniques 1

Nissl Stained Rat Brain Cell Image Analysis Post Exposure to...

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1st international conference on Multimedia Analysis and pattern recognition (MAPR)

作者： Netke, Bhakti Dongre, Swati Bhadouria, Nalini Bhattacharya, Mahua PES University

ISBN: (纸本)9781538641804

studies have shown that mobile phones which emit high-frequency electromagnetic fields have a bleak effect on the human brain. However, the results are found to be inconsistent pertaining to the exact nature of thump it causes with respect to the changes in the brain cell morphology, cell structure, cell shape, cell size, nuclei count and apoptosis. The paper intends to demonstrate novel techniques developed to elucidate the effect of electromagnetic fields (EMF) exposure on neuronal cells of post-natal rats as subjects. The brain cell images of the subjects, prior and post exposure to the electromagnetic fields (EMF) are obtained using the Cresyl Violet (Nissl) staining method. The images are then subjected to analysis using pre-processing techniques such as average filtering combined with median filtering to denoise the Nissl stained cell images, image enhancement techniques like local enhancement using CLAHE algorithm for abating the pixel intensity variations. The image procured after pre-processing is then segmented using a hybrid segmentation technique that integrates adaptive single seed region growing and threshold based segmentation to obtain separate cells and analyzes them for apoptosis. A novel technique using distance transform with maxima propagation has been developed for automated cell counting. The end results intend to limn the accuracy of the developed cell counting and segmentation techniques over the manual approach. And also, to delineate the impact of electromagnetic fields (EMF) radiations beyond a threshold value at the neuronal level.

关键词： Cresyl Violet (Nissl) staining Contrast Limited Adaptive Histogram Equalization (CLAHE) Electromagnetic Fields (EMF) Average Filter Median Filter Distance Transform Adaptive Single Seed Region Growing Segmentation Apoptosis

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image Size, Color Depth, Age variant on Convolution Neural Network 1

Image Size, Color Depth, Age variant on Convolution Neural N...

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1st Indonesian Association for pattern recognition international conference, INAPR 2018

作者： Pranoto, Hady Budiharto, Widodo Hendric Spits Warnars, Harco Leslie Matsuo, Tokuro Heryadi, Yaya Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Graduate School of Industrial Technology Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9781538694220

Facial recognition as of biometric authentication used in the field of security, military, finance and daily use is become a trend or famous, because of its natural and not intrusive nature. Many methods for face recognition such as holistic learning, the use of local features, shallow learning and deep learning, some methods are susceptible to variations in pose change, illumination, expression and age variation. state of the art of face recognition today is a deep learning technique that delivers high accuracy. In this paper author replicate an face recognition using deep learning architecture called OpenFace Convolutional Neural Network. In this research author make variation on the size of image, color dept and age, and see how that factor impact on accuracy of face recognition in that architecture. As the result from the research, the accuracy of a model depends on the image size, color depth, and age variation, but in OpenFace CNN that recognition still provides fairly good accuracy when reducing the size of image and color depth, as long as the image can still be detected on the landmark facial, so the alignment process can be done on the face image. © 2018 IEEE.

关键词： Face recognition

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