scholarly journals Review on Computer Aided Sewer Pipeline Defect Detection and Condition Assessment

2019 ◽  
Vol 4 (1) ◽  
pp. 10 ◽  
Author(s):  
Saeed Moradi ◽  
Tarek Zayed ◽  
Farzaneh Golkhoo
Keyword(s):  
Computer Vision ◽  
Visual Inspection ◽  
Review Paper ◽  
Condition Assessment ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Infrastructure Management ◽  
Advantages And Disadvantages ◽  
Current State ◽  
Learning Techniques

Physical and operational inspection of sewer pipelines is critical to sustaining an acceptable level of system serviceability. Emerging inspection tools in addition to developments in sensor and lens technologies have facilitated sewer condition assessment and increased the quality and consistency of provided data. Meanwhile, sewer networks are too vast to be adequately investigated manually so the development of innovative computer vision techniques for automation applications has become an interest point of recent studies. This review paper presents the current state of inspection technology practices in sewer pipelines. An overall inspection tool comparison was conducted and the advantages and disadvantages of each method were discussed. This was followed by a comprehensive review of recent studies on visual inspection automation using computer vision and machine learning techniques. Finally, current achievements and limitations of existing automation methods were debated to outline open challenges and future research for both infrastructure management and computer science researchers.

scholarly journals Deep Learning for Computer Vision: A Brief Review

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Athanasios Voulodimos ◽  
Nikolaos Doulamis ◽  
Anastasios Doulamis ◽  
Eftychios Protopapadakis
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Review Paper ◽  
Machine Learning Techniques ◽  
Future Directions ◽  
Boltzmann Machines ◽  
Learning Techniques ◽  
Previous State ◽  
Human Pose ◽  
Learning Schemes

Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein.

Decay Detection in Citrus Fruits Using Hyperspectral Computer Vision

2012 ◽  
pp. 104-123
Author(s):  
Juan Gómez-Sanchis ◽  
Emilio Soria-Olivas ◽  
Delia Lorente-Garrido ◽  
José M. Martínez-Martínez ◽  
Pablo Escandell-Montero ◽  
...  
Keyword(s):  
Computer Vision ◽  
Agricultural Sector ◽  
Vision System ◽  
Penicillium Digitatum ◽  
Machine Learning Techniques ◽  
Economic Losses ◽  
Citrus Fruits ◽  
Penicillium Italicum ◽  
Citrus Industry ◽  
Learning Techniques

The citrus industry is nowadays an important part of the Spanish agricultural sector. One of the main problems present in the citrus industry is decay caused by Penicillium digitatum and Penicillium italicum fungi. Early detection of decay produced by fungi in citrus is especially important for the citrus industry of distribution. This chapter presents a hyperspectral computer vision system and a set of machine learning techniques in order to detect decay caused by Penicillium digitatum and Penicillium italicum fungi that produce more economic losses to the sector. More specifically, the authors employ a hyperspectral system and artificial neural networks. Nowadays, inspection and removal of damaged citrus is done manually by workers using dangerous ultraviolet light. The proposed system constitutes a feasible and implementable solution for the citrus industry; this has been proven by the fact that several machinery enterprises have shown their interest in the implementation and patent of the system.

Machine Learning

2012 ◽  
pp. 13-22 ◽  
Author(s):  
João Gama ◽  
André C.P.L.F. de Carvalho
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Text Processing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Background Information ◽  
Future Research ◽  
Personal View ◽  
Learning Techniques ◽  
Future Research Directions

Machine learning techniques have been successfully applied to several real world problems in areas as diverse as image analysis, Semantic Web, bioinformatics, text processing, natural language processing,telecommunications, finance, medical diagnosis, and so forth. A particular application where machine learning plays a key role is data mining, where machine learning techniques have been extensively used for the extraction of association, clustering, prediction, diagnosis, and regression models. This text presents our personal view of the main aspects, major tasks, frequently used algorithms, current research, and future directions of machine learning research. For such, it is organized as follows: Background information concerning machine learning is presented in the second section. The third section discusses different definitions for Machine Learning. Common tasks faced by Machine Learning Systems are described in the fourth section. Popular Machine Learning algorithms and the importance of the loss function are commented on in the fifth section. The sixth and seventh sections present the current trends and future research directions, respectively.

Machine Learning

2011 ◽  
pp. 2462-2468 ◽  
Author(s):  
João Gama ◽  
André C.P.L.F. de Carvalho
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Text Processing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Background Information ◽  
Future Research ◽  
Personal View ◽  
Learning Techniques ◽  
Future Research Directions

Machine learning techniques have been successfully applied to several real world problems in areas as diverse as image analysis, Semantic Web, bioinformatics, text processing, natural language processing,telecommunications, finance, medical diagnosis, and so forth. A particular application where machine learning plays a key role is data mining, where machine learning techniques have been extensively used for the extraction of association, clustering, prediction, diagnosis, and regression models. This text presents our personal view of the main aspects, major tasks, frequently used algorithms, current research, and future directions of machine learning research. For such, it is organized as follows: Background information concerning machine learning is presented in the second section. The third section discusses different definitions for Machine Learning. Common tasks faced by Machine Learning Systems are described in the fourth section. Popular Machine Learning algorithms and the importance of the loss function are commented on in the fifth section. The sixth and seventh sections present the current trends and future research directions, respectively.

Machine Learning Techniques to Identify and Characterize Sleep Disorders Using Biosignals

2021 ◽  
pp. 136-160
Author(s):  
Mercedes Barrachina ◽  
Laura Valenzuela López
Keyword(s):  
Machine Learning ◽  
Sleep Disorders ◽  
Adult Population ◽  
The United States ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Research Directions ◽  
Learning Techniques ◽  
Control And Prevention ◽  
Future Research Directions

Sleep disorders are related to many different diseases, and they could have a significant impact in patients' health, causing an economic impact to the society and to the national health systems. In the United States, according to information from the Center for Disease Control and Prevention, those disorders are affecting 50-70 million in the adult population. Sleep disorders are causing annually around 40,000 deaths due to cardiovascular problems, and they cost the health system more than 16 billion. In other countries, such as in Spain, those disorders affect up to 48% of the adult population. The main objective of this chapter is to review and evaluate the different machine learning techniques utilized by researchers and medical professionals to identify, assess, and characterize sleep disorders. Moreover, some future research directions are proposed considering the evaluated area.

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