scholarly journals IMAGE SEGMENTATION AND OBJECT SELECTION BASED ON MULTI-THRESHOLD PROCESSING

2019 ◽  
Vol 22 (3) ◽  
pp. 24-35 ◽  
Author(s):  
Vladimir Yu. Volkov ◽  
Oleg A. Markelov ◽  
Mikhail I. Bogachev
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Test Methods ◽  
Object Identification ◽  
Detection Methods ◽  
Clustering Methods ◽  
Object Selection ◽  
Advantages And Disadvantages ◽  
Infrared Cameras ◽  
Threshold Processing

Introduction. Detection, isolation, selection and localization of variously shaped objects in images are essential in a variety of applications. Computer vision systems utilizing television and infrared cameras, synthetic aperture surveillance radars as well as laser and acoustic remote sensing systems are prominent examples. Such problems as object identification, tracking and matching as well as combining information from images available from different sources are essential. Objective. Design of image segmentation and object selection methods based on multi-threshold processing. Materials and methods. The segmentation methods are classified according to the objects they deal with, including (i) pixel-level threshold estimation and clustering methods, (ii) boundary detection methods, (iii) regional level, and (iv) other classifiers, including many non-parametric methods, such as machine learning, neural networks, fuzzy sets, etc. The keynote feature of the proposed approach is that the choice of the optimal threshold for the image segmentation among a variety of test methods is carried out using a posteriori information about the selection results. Results. The results of the proposed approach is compared against the results obtained using the well-known binary integration method. The comparison is carried out both using simulated objects with known shapes with additive synthesized noise as well as using observational remote sensing imagery. Conclusion. The article discusses the advantages and disadvantages of the proposed approach for the selection of objects in images, and provides recommendations for their use.

scholarly journals A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images

2020 ◽  
Vol 1 (3) ◽  
pp. 78-91
Author(s):  
Muhammad Muhammad ◽  
Diyar Zeebaree ◽  
Adnan Mohsin Abdulazeez Brifcani ◽  
Jwan Saeed ◽  
Dilovan Asaad Zebari
Keyword(s):  
Breast Cancer ◽  
Image Segmentation ◽  
Signal To Noise Ratio ◽  
Ultrasound Image ◽  
Region Of Interest ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Clustering Methods ◽  
Advantages And Disadvantages ◽  
Ultrasound Image Segmentation

The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challenging issue due to numerous ultrasound artifacts introduced in the imaging process, including high speckle noise, poor contrast, blurry edges, weak signal-to-noise ratio, and intensity inhomogeneity.

scholarly journals A Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images: A Review

2020 ◽  
Vol 1 (3) ◽  
pp. 78-91
Author(s):  
Muhammad Muhammad ◽  
Diyar Zeebaree ◽  
Adnan Mohsin Abdulazeez Brifcani ◽  
Jwan Saeed ◽  
Dilovan Asaad Zebari
Keyword(s):  
Breast Cancer ◽  
Image Segmentation ◽  
Signal To Noise Ratio ◽  
Ultrasound Image ◽  
Region Of Interest ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Clustering Methods ◽  
Advantages And Disadvantages ◽  
Ultrasound Image Segmentation

The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challenging issue due to numerous ultrasound artifacts introduced in the imaging process, including high speckle noise, poor contrast, blurry edges, weak signal-to-noise ratio, and intensity inhomogeneity.

scholarly journals Multi-threshold object selection in images of remote sensing systems

2019 ◽  
Vol 29 (2) ◽  
pp. 76-88 ◽  
Author(s):  
V. Yu. Volkov ◽  
M. I. Bogachev ◽  
O. A. Markelov
Keyword(s):  
Remote Sensing ◽  
Geometric Features ◽  
Scale Invariant ◽  
Object Selection ◽  
Sensing Systems ◽  
Remote Sensing Systems ◽  
Efficiency Of Selection ◽  
Threshold Setting ◽  
Threshold Processing ◽  
Selection Of

The aim of the work is to increase the efficiency of selection of objects of different nature in digital monochrome images formed in remote sensing systems. For this purpose, algorithms for the formation of features of objects with respect to which boundary values are specified are introduced into the structure of multi-threshold processing. New schemes of multi-threshold processing and selection of objects of interest with threshold setting based on selection results are proposed. Algorithms of multi-threshold selection of objects by area and other scale-invariant geometric features, such as the elongation coefficient of the perimeter of the object and the elongation coefficient of the main axis of the describing ellipse, are obtained and tested. The binarization threshold is set for each of the selected objects based on the extremum of the applied geometric criterion. The new invariant geometric features used are different for round and elongated objects and provide independence of characteristics with changes in the image scale. Results of processing of typical models of images, and also results of selection of objects on the real television and infrared images showing efficiency of the proposed selection method are presented.

scholarly journals A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images

2020 ◽  
Vol 1 (3) ◽  
pp. 78-91
Author(s):  
Muhammad Muhammad ◽  
Diyar Zeebaree ◽  
Adnan Mohsin Abdulazeez Brifcani ◽  
Jwan Saeed ◽  
Dilovan Asaad Zebari
Keyword(s):  
Breast Cancer ◽  
Image Segmentation ◽  
Signal To Noise Ratio ◽  
Ultrasound Image ◽  
Region Of Interest ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Clustering Methods ◽  
Advantages And Disadvantages ◽  
Ultrasound Image Segmentation

The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challenging issue due to numerous ultrasound artifacts introduced in the imaging process, including high speckle noise, poor contrast, blurry edges, weak signal-to-noise ratio, and intensity inhomogeneity.

scholarly journals Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

2021 ◽  
Vol 26 (1) ◽  
pp. 200-215
Author(s):  
Muhammad Alam ◽  
Jian-Feng Wang ◽  
Cong Guangpei ◽  
LV Yunrong ◽  
Yuanfang Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Natural Scene ◽  
Remote Sensing Images ◽  
Advantages And Disadvantages ◽  
Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

scholarly journals Edge Computing for Data Anomaly Detection of Multi-Sensors in Underground Mining

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 302
Author(s):  
Chunde Liu ◽  
Xianli Su ◽  
Chuanwen Li
Keyword(s):  
Energy Consumption ◽  
Anomaly Detection ◽  
Underground Mining ◽  
Heterogeneous Data ◽  
Edge Computing ◽  
Sensor Nodes ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Clustering Methods ◽  
Safety Warning

There is a growing interest in safety warning of underground mining due to the huge threat being faced by those working in underground mining. Data acquisition of sensors based on Internet of Things (IoT) is currently the main method, but the data anomaly detection and analysis of multi-sensors is a challenging task: firstly, the data that are collected by different sensors of underground mining are heterogeneous; secondly, real-time is required for the data anomaly detection of safety warning. Currently, there are many anomaly detection methods, such as traditional clustering methods K-means and C-means. Meanwhile, Artificial Intelligence (AI) is widely used in data analysis and prediction. However, K-means and C-means cannot directly process heterogeneous data, and AI algorithms require equipment with high computing and storage capabilities. IoT equipment of underground mining cannot perform complex calculation due to the limitation of energy consumption. Therefore, many existing methods cannot be directly used for IoT applications in underground mining. In this paper, a multi-sensors data anomaly detection method based on edge computing is proposed. Firstly, an edge computing model is designed, and according to the computing capabilities of different types of devices, anomaly detection tasks are migrated to different edge devices, which solve the problem of insufficient computing capabilities of the devices. Secondly, according to the requirements of different anomaly detection tasks, edge anomaly detection algorithms for sensor nodes and sink nodes are designed respectively. Lastly, an experimental platform is built for performance comparison analysis, and the experimental results show that the proposed algorithm has better performance in anomaly detection accuracy, delay, and energy consumption.

scholarly journals Food Allergen Detection Methods: A Coordinated Approach

2004 ◽  
Vol 87 (6) ◽  
pp. 1383-1390 ◽  
Author(s):  
Philip R Goodwin
Keyword(s):  
Hazard Analysis ◽  
Adult Population ◽  
Control Point ◽  
Cost Effective ◽  
Test Methods ◽  
Detection Methods ◽  
Critical Control Point ◽  
Life Threatening ◽  
Monitoring And Surveillance ◽  
Analytical Approaches

Abstract The levels (1–2%) and increasing severity of allergic responses to food in the adult population are well documented, as is the phenomenon of even higher (3–8%) and apparently increasing incidence in children, albeit that susceptibility decreases with age. Problematic foods include peanut, milk, eggs, tree nuts, and sesame, but the list is growing as awareness continues to rise. The amounts of such foods that can cause allergic reactions is difficult to gauge; however, the general consensus is that ingestion of low parts per million is sufficient to cause severe reactions in badly affected individuals. Symptoms can rapidly—within minutes—progress from minor discomfort to severe, even life-threatening anaphylactic shock in those worst affected. Given the combination of high incidence of atopy, potential severity of response, and apparently widespread instances of “hidden” allergens in the food supply, it is not surprising that this issue is increasingly subject to legislative and regulatory scrutiny. In order to assist in the control of allergen levels in foods to acceptable levels, analysts require a combination of test methods, each designed to produce accurate, timely, and cost-effective analytical information. Such information contributes significantly to Hazard Analysis Critical Control Point programs to determine food manufacturers’ risk and improves the accuracy of monitoring and surveillance by food industry, commercial, and enforcement laboratories. Analysis thereby facilitates improvements in compliance with labeling laws with concomitant reductions in risks to atopic consumers. This article describes a combination of analytical approaches to fulfill the various needs of these 3 analytical communities.

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