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image processing techniques in automatic diabetic retinopathy screening systems

Test Engineering and Management 2020年 83卷 202-215页

作者： Sivakumar, v. Husnoo, Muhammad Akbar Bukit Jalil57000 Malaysia

With the increased number of people suffering from diabetes, there is an urgency to automate Diabetic Retinopathy Detection. According to the International Diabetic Federation, there are over 93 millions of patients suffering from Diabetic Retinopathy world-wide. Currently, there are three types of manual screening procedures to detect Diabetic Retinopathy which include Pupil Dilation, Ophthalmoscopy and Tonometry. Several attempts to automate the screening of Diabetic Retinopathy has been made over the last two decades. An Automatic Diabetic Retinopathy Screening System (ADRSS) serves as a supporting tool for an Ophthalmologist to increase the speed and accuracy of Diabetic Retinopathy Detection. In the domain of research, several such systems have been made available which makes use of ocular images different imaging modalities. Depending upon different modalities, different image processing algorithms have been put in practice. In this juncture, this proposed aims to study and analyze all the different detection techniques available with an emphasis to techniques for respective image modalities. This proposed paper will conduct a detailed review on the different algorithms that are being implemented of the different types of images from different modalities with their diagnostic level of accuracy in Diabetic Retinopathy Screening. © 2020 Mattingley Publishing. All rights reserved.

关键词： Eye protection

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Computerized retinal image analysis-a survey

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第31-32期79卷 22389-22421页

作者： Mittal, Kanupriya Rajam, v. Mary Anita Anna Univ Dept CSE CEG Chennai Tamil Nadu India

The speedy development of digital imaging and computer vision has extended the potential of using these technologies in ophthalmology. image processing systems are increasingly prominent in medical diagnostic systems and especially to modern ophthalmology. The retinal images give information about the health of the visual system. Retinal diseases, such as glaucoma, diabetic retinopathy, age-related macular degeneration, and many other diseases that can lead to blindness, manifest themselves in the retina. An automated system offers standardized large-scale screening at a lower cost, reduces human errors, and provides services to remote areas. Extensive research has been done since the last two decades in developing automated methods. Due to the fast evolution of new techniques, a comprehensive review is needed on such technique and algorithms present to date. This survey paper provides the reader a comprehensive review of the existing research in automated retinal image analysis. In this paper, automated computer aided methods used to diagnose retinal diseases have been reviewed. Several state-of-the art techniques and algorithms used to localize and segment features, such as optic disc and optic cup, macula and fovea, retinal blood vessels, detection of retinal lesions (microaneurysms, haemorrhages, exudates), are discussed and presented.

关键词： Retinal image Machine learning Diabetic retinopathy

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Deep Learning Techniques for image Recognition in IoT-Enabled Surveillance systems

Deep Learning Techniques for Image Recognition in IoT-Enable...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： v Dankan Gowda Y. vishnu Tej vidya Sagar Potharaju Premkumar Reddy Jakkidi Aanchal Sharma N Sudhakar Reddy Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka Department of Computer Science and Systems Engineering Andhra University Visakhapatnam India Department of ECE Vignana Bharati Institute of Technology Ghatkesar Hyderabad India 15950 paramount way Frisco Texas DCSA Panjab University Chandigarh India Department of ECE School of Engineering Mohan Babu University Tirupati India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

This paper proposed an improved method that can be used in the identification of images within IoT based surveillance systems through the use of deep learning techniques especially the CNNs. The growth of concern and complexity therefore entails massive feeding from bursts of data from surveillance cameras, hence the need for reliable algorithms for processing the data in real-time with reasonable accuracy. The described system can be described as inclusive of IoT devices communicating with edge computing and cloud services for the purpose of scalability and performance enhancement. In this study, a very sophisticated experimental setup was employed; the authors used a number of datasets for surveillance purposes to evaluate the model. The results revealed that the segments of the method achieve a high accuracy and are continuously running through data in diverse surveillance scenarios while also being scalable. In the results section, we realized that CNN model was able to handle various environmental conditions such as; extreme lightening and occlusion. On the basis of the above mentioned data, it will be possible to suggest that this approach is rather superior compared to the other conventional approaches and therefore can be suggested as the possible solution of the modern conditions of surveillance. It thus provides platform for the advancement of intelligent surveillance system for use in smart city, security and safety application.

关键词： Deep learning Accuracy image recognition Smart cities Surveillance Computational modeling Real-time systems Internet of Things Security Edge computing

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Traffic Management and Signal Manipulation system using Machine Learning

Traffic Management and Signal Manipulation system using Mach...

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Global Conference for Advancement in Technology (GCAT)

作者： Supriya Telsang Ishan Ranadive Chinmay Rane Manas Bagul Shantanu P. Rajurkar Bhawana D. Rakshit Taneesha Malve Shwetkumar Ramteke Department of Engineering Sciences and Humanities (DESH) Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

This study explores a novel method to traffic control that enhances signal control systems in metropolitan environments by applying machine learning techniques. This research revolves around a methodology that improves vehicle flow and reduces traffic congestion. This paper elaborates on the methodology utilized for collection of data and vehicle detection methods used for optimizing traffic signals. The findings reveal noteworthy enhancements in improving vehicle detection such a as map50 of 80.5% of various types of vehicles pertaining to Indian traffic using Yolo v8. In essence, this research endeavors to transcend traditional paradigms of traffic management, ushering in a new era of intelligent infrastructure capable of harmonizing vehicular movement.

关键词： Machine learning algorithms vehicle detection image processing Machine learning Traffic control Public security Timing Planning Time factors Traffic congestion

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Traffic Sign Detection Using Hybrid Network Of Yolo And Resnet

Traffic Sign Detection Using Hybrid Network Of Yolo And Resn...

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International Conference on Computer Communication and Informatics (ICCCI)

作者： S. vinothkumar S. varadhaganapathy R. Shanthakumari S. Pradeev S. Pragatheeswaran K.Sankara Annamalai Department of Information Technology Kongu Engineering College Perundurai Erode Tamil Nadu

Deep learning (DL) methods have outperformed machine learning (ML) and statistical techniques in predicting road traffic. Neural networks serve as the foundation for deep learning algorithms. In smart transport systems, the recognition and identification of traffic signs is a critical issue. Automated traffic sign detection and recognition Driving is essential for achieving autonomous transportation.. There are numerous traffic signs, and it takes time to train a good model. A TSR system can raise a driver’s awareness of the road situation and condition, potentially lowering traffic accidents. The YOLOv3 (You Only Look Once, version 3) algorithm, a real-time object identification technique with cutting-edge features that detects individual items approach for detecting traffic flow, is what we suggest as a solution to this problem. The YOLO algorithm uses features that have been learned to find objects. For more accuracy we are combining two algorithms to make the prediction more efficient and accurate. ResNet is used to extract features from the training data set and the classification is done by YOLO v3 algorithm. versions 1-3 of the YOLO machine learning algorithm were produced by the third version of the methodology, which is an improved version of the original DL algorithm. Before classification and the output of the optimised result, the image will undergo pre-processing to extract features. The proposed method makes it easier for drivers to detect traffic signs and reduces the number of accidents on the road. Experiment results show that the YOLO-v3 approach outperforms other previously existing algorithms in terms of average accuracy

关键词： Machine learning algorithms Roads Neural networks Feature extraction Prediction algorithms Real-time systems Classification algorithms

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Analysis of an adaptive algorithm for processing space-time signals for image transmission based on 3D wireless channel model

Analysis of an adaptive algorithm for processing space-time ...

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2021 Radiation and Scattering of Electromagnetic Waves, RSEMW 2021

作者： Fedosov, valentin P. Jameel, Jaleel Sadoon Kucheryavenko, Svetlana v. Institute of Radio Systems and Control / Theoretical Foundations of Radio Engineering Southern Federal University Taganrog Russia

ISBN: (纸本)9781665425445

The paper proposes adaptive filtering algorithms and coding for use in wireless networks based on 3D WiMAX, which make it possible to improve the efficiency of signal transmission in mobile communication network with the absence of line-of-sight areas, which is typical for an urban environment with dense buildings. The effects of the multipath signal propagation, the Doppler effect, and the mobility speeds of the subscriber terminal on the performance of the communication system were investigated. The simulation results show that the adaptive algorithm significantly reduces the probability of error and increases the throughput. It can be concluded that the use of adaptive spatio-temporal algorithms has a positive effect on noise immunity when processing signals in the receiving system under conditions of multipath signal propagation. © 2021 IEEE.

关键词： Orthogonal frequency division multiplexing

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The Estimation of Inertial Measurement Units Accuracy Using Digital image processing algorithms

The Estimation of Inertial Measurement Units Accuracy Using ...

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Wave Electronics and its Application in Information and Telecommunication systems (WECONF)

作者： E. v. Shmatko A. Yu. Poroykov Department of Physics named after V.A. Fabrikant National Research University &#x201C Moscow Power Engineering Institute&#x201D Moscow Russia

The paper describes an approach for estimation of inertial measurement unit using image processing algorithm to determine the position of an object in space. The results of measurements of the angular velocities of a freely rotating platform using the inertial and photogrammetry methods are presented. The inertial method data contains the measurement results of a three-axis solid gyroscope. The photogrammetry method determines the position of the platform using digital video cameras based on images of fiducial markers installed on it. A comparison of the measurement results is presented, conclusions about the accuracy of the tested gyroscope are drawn.

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The Analysis of Satellite images for Anomaly Identification in Patterns

The Analysis of Satellite Images for Anomaly Identification ...

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Mayur v. Tiwari Sanjay v. Dudul Ashay I. Rokade Ranjana D. Jawanjal Ashish S. Bhopale Amol S. Mahore Department of Applied Electronics SGBAU Amravati India Department of Electronics & Telecommunication Engineering PRMIT&R Badnera India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

Anomaly detection in remotely sensed imagery constitutes a pivotal endeavor with a multitude of applications, encompassing areas such as environmental surveillance and precision agriculture. A variety of methodologies have been formulated to augment the identification of anomalies, defined as deviations from anticipated patterns within image datasets. These approaches capitalize on sophisticated computational strategies and advanced machine learning algorithms to enhance both accuracy and operational efficiency. Anomaly detection in remotely sensed imagery can be implemented through an array of techniques, including heterogeneous and Edge Computing (EC), CNN, feature spaces with multi-dimensionality, unified anomaly detection frameworks, and unsupervised learning for the identification of burnt areas, among others. This manuscript elucidates various methodologies and state-of-the-art technologies pertinent to anomaly detection. Although each of these methodologies exhibits significant advancements, obstacles and constraints continue to exist pertaining to the requirements for digital assets and the imperative for real-time processing functionalities. Subsequent study endeavors may prioritize the refinement of these models to enable more extensive applications and to improve their flexibility in accommodating emerging data sources.

关键词： Satellites Reviews Surveillance Soft sensors Mathematical models Real-time systems Satellite images Anomaly detection Unsupervised learning Hyperspectral imaging

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Explore the possibilities for objective assessment of some beer quality indicators

Explore the possibilities for objective assessment of some b...

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International Conference on Technics, Technologies and Education 2020, ICTTE 2020

作者： Gabrova, R. Danev, A. Ganchovska, v. Andreeva, H. University of Food Technologies Technical Faculty Department of Computer Systems and Technologies Maritza 26 Plovdiv4000 Bulgaria

The aim of this research is to develop an appropriate experimental setting and to explore the possibilities for objective automatic and express assessment of some appearance indicators of beer quality using computer vision techniques. The goal of the research will be achieved by developing a computer vision system, including a hardware module for obtaining primary information and a software module for processing primary information and extracting the desired characteristics through algorithms based on adapted image processing methods. © Published under licence by IOP Publishing Ltd.

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Bearing Fault Classification using Phase Space Reconstruction Integrated Fine-tuned vGG19 Model

Bearing Fault Classification using Phase Space Reconstructio...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Asim Swarup Soham Rath Madhusmita Rout Dipti Ranjan Dash Mamata P. Wagh Computer Science And Engineering C.V. Raman Global University Bhubaneswar India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Effective fault detection in rotating machinery is essential for ensuring industrial systems’ reliability and operational efficiency. In this work, we proposed a method for fault detection using image matching technique. The continuous images generated from rotating machinery part are considered for experimentation. Traditional signal processing algorithms may fail to extract meaningful information from signals, particularly early in the fault growth process, they might miss the subtle changes that occur during the early phases of fault development. To address this problem an image-matching technique using visual Geometry Group 19 (vGG19) is adopted. The fundamental benefit of this approach is its ability to deal with the complexities of non-stationary chaotic line images, which frequently display temporal changes and noise interference of the expected fault. In the proposed technique, vibration signals are transformed into chaotic line images and vGG-19 architecture is employed to detect the fault from these images. The proposed technique enhances the sensitivity to faults while accommodating the complexities of non-stationary signals and noise by comparing generated images with the pre-stored images of normal and faulty occurrences. The approach showcases its capability to offer advanced warnings of potential malfunctions, enabling maintenance personnel to take proactive measures and prevent costly downtime. The proposed technique exhibits the advantage of improving the reliability and efficiency of industrial systems by enabling timely intervention and preventive maintenance.

关键词： Fault diagnosis vibrations visualization Fault detection Noise Space exploration Complexity theory Machinery image reconstruction Testing

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