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Development of an Algorithm for Determining the Railway Tracks on Video image 1

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1st international Scientific conference on Telecommunications, Computing and Control, TELECCON 2019

作者： Deylid, Ivan Molodyakov, Sergey Tyutin, Boris Peter the Great St. Petersburg Polytechnic University St. Petersburg Russia Opera Software Gothenburg Sweden

ISBN: (数字)9789813366329

ISBN: (纸本)9789813366312

The subject of the work is in the field of creating autopilot railway transport. One of the problems related to railway safety is considered. This paper presents a developed algorithm for determining the railway track from a video image. It is the first step in determining the free path or obstacles in front of the locomotive. The proposed algorithm is a combination of several machine learning algorithms that are applied sequentially (boosting). The first stage of the algorithm is the extraction and classification of features from the image. In this stage, the speeded up robust features or SURF-method is used. At the output of the SURF-stage, we obtain information in the form coordinates of key points and their descriptors. In the second stage, the selected key points are classified. Combinations of two classification methods are used: the K-nearest neighbors or KNN-method and the support vector machines or SVM-method. The final step is the compilation of a railway track mask. For this, the nearest neighbor graph method is used. For practical use of the found mask, the inverse perspective transformation is performed. The efficiency of the developed algorithm is shown experimentally. It can be considered as one of the ways of image segmentation. The main advantages of the algorithm are associated with minimal preparation of the training sample and the ability to analyze its work for further improvement. The results of processing real video images obtained from a video camera mounted on a locomotive are presented. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Nearest neighbor search

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Advanced MRI Segmentation Algorithm for the Detection of Brain Tumor Using U-Net Architecture with Transfer learning EfficientNet-B7 1st

Advanced MRI Segmentation Algorithm for the Detection of Bra...

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1st international conference on Artificial Intelligence and Internet of Things, ICAII 2022

作者： Dubey, Vikash Kumar Murari, Krishna Nath, Triloki Poddar, Kriti Cognizant Delhi India RVS College of Engineering and Technology Jharkhand Jamshedpur India BIT Mesra Jharkhand Ranchi India

ISBN: (纸本)9783031373022

The complexity of segmenting a brain tumor is significant in medical image processing. Treatment options and patient survival rates won’t improve unless brain malignancies are prevented and treated. The most time-consuming and challenging task is manually identifying malignancy from numerous samples. Using magnetic resonance imaging (MRI), the brain is segmented. Tumor brain segmentation must be mechanised. To be categorized, brain tumor need to be automatically segregated. The objective of this study is to develop an automated system for tumor segmentation in magnetic resonance imaging, which will facilitate the precise and quick detection of malignancies. Various machine learning models, such as random forest (RF), support vector machine (SVM), decision tree (DT), and extreme gradient boosting (XGBoost) for image segmentation, are presented in this article along with convolutional neural networks (CNNs) and U-Nets with different transfer learning models as backbones, including ResNet50, Vgg16, InceptionV4, DenseNet169, and EfficientNet-B7. Making use of the BraTS 2020 dataset, brain lesion segmentation is carried out. The tumor segmentation method is evaluated using the F1-Score, Dice loss, and Intersection Over Union (IoU) score based on MRI scans of the brain. In terms of performance measures, the U-Net encoder design with EfficientNet-B7 exceeds all other architectures. The outcomes of this experiment are often quite good. The IoU score was 0.7439 and the dice loss was 0.009386. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Magnetic resonance imaging

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Application of a Novel Deep learning Model to Recognize and Predict Kidney Disease in the Context of image processing 1st

Application of a Novel Deep Learning Model to Recognize and ...

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1st international conference on Advanced Communication and Intelligent Systems, ICACIS 2022

作者： Sreeji, S. Sathiyaprasad, B. Asha, R. Kavitha, G. Babu, K. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Jeppiaar Nagar Rajiv Gandhi Salai Chennai600 119 India

ISBN: (纸本)9783031250873

Chronic Kidney Disease (CKD) is a gradual decline of renal function that eventually results in kidney damage or failure. As the illness progresses, it becomes more challenging to diagnose. Evaluating various phases of CKD using routine doctor consultation data may help with early diagnosis and encourage beneficial intervention. Therefore, researchers offer a strategy for categorizing CKD that makes use of an optimization technique that is motivated by the learning procedure. Artificial intelligence is a concept that helps to make all such Things in this world seem impossible. As a risk-causing agent, some inventions are still surprised by their activity or ability. Even doctors are expecting some advancements to scan the patient’s body and analyse the disease. In this scenario, advanced machine learning algorithms have been introduced that help to find the presence of kidney disease inside a human body. In this research, a novel deep learning model is designed to recognize and predict kidney disease. The model is designed as a hybrid of a Fuzzy Deep Neural Network (FDNN) and is compared with the traditional method. From the output, it shows that the proposed model has obtained an accuracy of 99.23%, which is better than the existing. Additionally, the accuracy of the presence of chronic disease in the human body can be confirmed without the doctor’s acknowledgement as an extended work. While compared to existing information mining classifications, efficiency comparison shows how the proposed approach achieves improved accuracy of classification, precision, F-measure, and sensitivity metrics. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image processing

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Challenges and Difficulties of Multi-Spectral MRI Based Brain Tumor Detection and Segmentation

Challenges and Difficulties of Multi-Spectral MRI Based Brai...

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Health Science and Technology (ICHst), international conference on

作者： László Szilágyi Ágnes Györfi Lehel Dénes-Fazakas Szabolcs Csaholczi Ioan-Marius Pisak-Lukáts Levente Kovács Physiological Controls Research Center Óbuda University Budapest Hungary Comput. Intell. Research Group Sapientia Univ. Tg. Muteş Romania BME IIT Budapest Hungary

ISBN: (数字)9798350315714

ISBN: (纸本)9798350315721

Introduction: The automatic detection and segmentation of brain tumors based on multi-spectral MRI data represents a widely investigated topic, which captured special attention with the Brain Tumor Segmentation challenges organized yearly since 2012 jointly with MICCAI conferences. Up to one thousand different solutions may have been presented by the research community, involving the whole arsenal of artificial intelligence starting with classical machine learning methods and developing together with convolutional neural networks and deep learning. Methodology: This paper proposes to discuss the general challenges and difficulties of automatic brain tumor segmentation methods, comparing the needs and capabilities of a classical machine learning based method using ensemble learning, and a U-net solution involving deep learning, both very popular at their respective time. The main difference of the two approaches is that the first relies on handcrafted features extracted for each pixel separately and applies a classification to estimate the tissue to which each pixel belongs, while the other works with whole slices or volumes at once and extracts its own features needed for decision making. Our team investigated the necessary preprocessing steps of MRI data, and their optimization to suit the needs of both approaches, and trialed various post-processing techniques that can improve the final segmentation outcome after the decision made by the classification algorithm. Results and Discussion: The U-net based solution can achieve finer segmentation quality, reaching Dice scores approximately 2 % higher than the ensemble learning, but its stability is weaker meaning that two different training sessions of the same network may lead to significantly different results. Another main difference consists in the most suitable histogram uniformization technique required, as the ensemble learning works better when the method of Nyul et al (IEEE T. Med. Imag. 19:143-150, 2000) is e

关键词： Deep learning Training image segmentation Magnetic resonance imaging Feature extraction Resonance Ensemble learning

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1st international conference on machine learning and Information processing, ICMLIP 2019

1st International Conference on Machine Learning and Informa...

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1st international conference on machine learning and Information processing, ICMLIP 2019

ISBN: (纸本)9789811518836

The proceedings contain 49 papers. The special focus in this conference is on machine learning and Information processing. The topics include: Virtual conversation with real-time prediction of body moments/gestures on video streaming data;automatic diagnosis of attention deficit/hyperactivity disorder;classification of types of automobile fractures using convolutional neural networks;a subjectivity detection-based approach to sentiment analysis;image processing and machine learning techniques to detect and classify paddy leaf diseases: A review;a comparative study of classifiers for extractive text summarization;capabilities of chatbots and its performance enhancements in machine learning;a deep learning-based approach for predicting the outcome of H-1B visa application;data leakage detection and prevention: Review and research directions;stress detection in speech signal using machine learning and AI;a blockchain based model to eliminate drug counterfeiting;a comparative analysis of predictive modeling techniques: A case study of device failure;Python and OpenCV in automation of live surveillance;Privacy preservation in ROI of medical images using LSB manipulation;novel approach for measuring nutrition values using smartphone;detection of hate speech in hinglish language;enhancing multi-level cache performance using dynamic R-F characteristics;denoising documents using image processing for digital restoration;humming-Based song recognition;a new data structure for representation of relational databases for application in the normalization process;e-Classroom Using IOT to revolutionize the classical way of knowledge sharing;real drowsiness detection using viola–jones algorithm in tensorflow;smart sound system controller for restricted zones;predictive analysis of co-seismic rock fall hazard in hualien county Taiwan.

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Deployment of Scalable Cloud Based image processing Application Using Fuzzy Coupled learning Technique

Deployment of Scalable Cloud Based Image Processing Applicat...

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international conference on Computational Science and Technology (ICCst)

作者： B. Mathivanan K.R. Nandhashree C. Radhakrishnan S. Ananthi P. Suganthi R. Asokan Department of CSE Sri Ramakrishna Engineering College Coimbatore Department of Cyber Security SRM Valliammai Engineering College Department of CSE Kongunadu College of Engineering and Technology Trichy Department of ECE Kongunadu College of Engineering and Technology Trichy

ISBN: (纸本)9781665476560

Because image processing typically uses a lot of memory and computational time, a sophisticated platform is needed. As a result, it's critical to adopt cost-effective solutions to replace outdated ones. We chose to use cloud computing to fulfill our needs for large-scale data processing as a result of these factors. In the meantime, this strategy offers quick access to on-demand services that are highly available and scalable. By outsourcing computations to a third party and utilizing cloud services rather than internal applications, healthcare organizations can reduce operating costs. However, robust data protection against both unauthorized users and unreliable clouds is required to avoid the disclosure of malicious data. Users can now assemble and execute their data using the cloud thanks to the development of numerous frameworks. Distributed systems, cryptosystems, or a combination of the two are frequently used in their construction. Almost all current implementations use homomorphism cryptosystems, Service Oriented Architecture, secret share schemes, and secure multi-party computation as their primary security mechanisms. The main disadvantage of utilizing these techniques for large-scale data analysis on the cloud is the cost of computing image processing tasks. Keeping medical records & sensitive health-related information secure is a problem. We suggest a fresh method for protecting data processing in cloud environments that is based on machine learning methods. We typically employ Fuzzy C-means Clustering and Multi-Dense Convolution Neural Network to more accurately classify image pixels. To further reduce the danger of a possible leak of medical data, we add a third level, the CloudSaS-TPM module, to the conventional two-layered design. The results show some encouraging discoveries that provide new angles for creating cloud storage for the medical industry.

关键词： Training Cloud computing Costs Sensitivity Scientific computing Neural networks Service-oriented architecture

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Fault Detection in Uni-Directional Tape Production Using image processing 25th

Fault Detection in Uni-Directional Tape Production Using Ima...

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25th international conference on pattern recognition Workshops, ICPR 2020

作者： Devagekar, Somesh Delforouzi, Ahmad Plöger, Paul G. Department of Computer Science Hochschule Bonn-Rhein-Sieg Bonn Germany Institute for Algorithms and Scientific Computing Fraunhofer SCAI Bonn Germany

ISBN: (纸本)9783030687984

The quality of uni-directional tape in its production process is affected by environmental conditions like temperature and production speed. In this paper, computer vision algorithms on the scanned images are needed to be used in this context to detect and classify tape damages during the manufacturing procedure. We perform a comparative study among famous feature descriptors for fault candidate generation, then propose own features for fault detection. We investigate various machine learning techniques to find best model for the classification problem. The empirical results demonstrate the high performance of the proposed system and show preference of random forest and canny edges for classifier and feature generator respectively. © 2021, Springer Nature Switzerland AG.

关键词： Feature extraction

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Using Siamese Networks with Transfer learning for Face recognition on Small-Samples Datasets 1

Using Siamese Networks with Transfer Learning for Face Recog...

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1st international conference on machine Vision and image processing, MVIP 2020

作者： Heidari, Mohsen Fouladi-Ghaleh, Kazim College of Farabi University of Tehran Deep Learning Research Lab Iran

ISBN: (纸本)9781728168326

Nowadays, computer-based face recognition is a mature and reliable mechanism that is significantly used in many access control scenarios along with other biometric methods. Face recognition consists of two subtasks including Face Verification and Face Identification. By comparing a pair of images, Face Verification determines whether those images are related to one person or not;and Face Identification has to identify a specific face within a set of available faces in the database. There are many challenges in face recognition such as angle, illumination, pose, facial expression, noise, resolution, occlusion and the few number of one-class samples with several classes. In this paper, we are carrying out face recognition by utilizing transfer learning in a siamese network which consists of two similar CNNs. In the siamese network, a pair of two face images is given to the network as input, then the network extracts the features of this pair of images and finally, it determines whether the pair of images belongs to one person or not by using a similarity criterion. The results show that the proposed model is comparable with advanced models that are trained on datasets containing large numbers of samples. furthermore, it improves the accuracy of face recognition in comparison with methods which are trained using datasets with a few number of samples, and the mentioned accuracy is claimed to be 95.62% on LFW dataset. © 2020 IEEE.

关键词： Face recognition

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Gathering Identification Using image Metrics for Intelligent Situation Awareness System in Real Time Scenarios 1st

Gathering Identification Using Image Metrics for Intelligent...

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1st international conference on Computational Intelligence in pattern recognition, CIPR 2019

作者： Gautam, Nandita Das, Debdoot Khatua, Sunirmal Saha, Banani Computer Science Department University of Calcutta Kolkata700098 India

ISBN: (纸本)9789811390418

The advancement of image processing in the field of Artificial Intelligence has created various research prospects in the area of object detection, pattern recognition, etc. Face detection technology is applied for biometric authentication systems for face recognition and verification. This work aims at developing a tool that can be used in an Intelligent Situation Awareness System for a given Region of Interest. This has been done by identifying human face as objects from digital video frames through real time video streaming by using Recurrent Convolution Neural Network (RCNN) classifiers. The key frames from a video are identified and machine learning algorithm is being applied on it for performing the object identification. After the facial objects are being identified in a given frame, this can be utilized for performing semantic analysis in a given spatiotemporal scenario. The metrics identified in this work include object count, hand gestures, relative distance as well as density of objects for developing a robust system that could function in real time. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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Trident U-Net: An Encoder Fusion for Improved Biomedical image Segmentation 1st

Trident U-Net: An Encoder Fusion for Improved Biomedical Ima...

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1st international conference on Bioengineering and Biomedical Signal and image processing, BIOMESIP 2021

作者： Chatterjee, Rajdeep Roy, Soham Islam, Sk Hafizul School of Computer Engineering KIIT Deemed to be University Bhuvaneswar Odisha751024 India Department of Computer Science and Engineering Indian Institute of Information Technology Kalyani Kalyani West Bengal741235 India

ISBN: (纸本)9783030881627

image segmentation is a fundamental requirement in biomedical image analysis. Recent advances in deep learning have resulted in optimistic results on many biomedical image segmentation benchmark datasets. However, there is a need to propagate and improve biomedical image analysis due to significant variations in biomedical images in recent days. As with the rapid growth of technology in medical equipment, more variations of biomedical data of various tests/scans are being generated rapidly. There is a growing vacuum of quick technical analysis on these biomedical data. Biomedical image segmentation is at the forefront of them. Manual analysis results in outrageous efforts and costs because only biomedical experts can annotate effectively and are often subjected to human error. Various deep learning-based solutions are being created to leverage machine learning capabilities and effectively address this problem. In this paper, We propose a deep learning framework built on the U-Net architecture and use a combination (triplet) of encoder models to address the semantic segmentation of a biomedical image effectively. An extensive experiment of our proposed Trident U-Net architecture has been done using the CVC-612 and ETIS datasets. The overall performance of the proposed Trident U-Net on both datasets outperforms the existing reportedly best performing models. The results obtained from the ETIS dataset are 0.66, 0.68, and 0.59 for the evaluation metrics Recall, Dice Coefficient, and IoU Score, respectively. Similarly, the results achieved on the CVC-612 dataset are 0.91, 0.89, and 0.83 for the same evaluation metrics, respectively. © 2021, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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