NDLI: An analogy between various machine-learning techniques for detecting construction materials in digital images

Content Provider	Springer Nature Link
Author	Rashidi, Abbas Sigari, Mohamad Hoseyn Maghiar, Marcel Citrin, David
Copyright Year	2016
Abstract	Digital images and video clips collected at construction jobsites are commonly used for extracting useful information. Exploring new applications for image processing techniques within the area of construction engineering and management is a steady growing field of research. One of the initial steps for various image processing applications is automatically detecting various types of construction materials on construction images. In this paper, the authors conducted a comparison study to evaluate the performance of different machine learning techniques for detection of three common categorists of building materials: Concrete, red brick, and OSB boards. The employed classifiers in this research are: Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Support Vector Machine (SVM). To achieve this goal, the feature vectors extracted from image blocks are classified to perform a comparison between the efficiency of these methods for building material detection. The results indicate that for all three types of materials, SVM outperformed the other two techniques in terms of accurately detecting the material textures in images. The results also reveals that the common material detection algorithms perform very well in cases of detecting materials with distinct color and appearance (e.g., red brick); while their performance for detecting materials with color and texture variance (e.g., concrete) as well as materials containing similar color and appearance properties with other elements of the scene (e.g., ORB boards) might be less accurate.
Starting Page	1178
Ending Page	1188
Page Count	11
File Format	PDF
ISSN	12267988
Journal	KSCE Journal of Civil Engineering
Volume Number	20
Issue Number	4
e-ISSN	19763808
Language	English
Publisher	Korean Society of Civil Engineers
Publisher Date	2015-08-21
Publisher Place	Seoul
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	digital images Multilayer Perceptron (MLP) Radial Basis Function (RBF) Support Vector Machine (SVM) Construction Materials Detection Civil Engineering Industrial Pollution Prevention Geotechnical Engineering & Applied Earth Sciences
Content Type	Text
Resource Type	Article
Subject	Civil and Structural Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Detecting Partial Shadowing and Mismatching Phenomena in Photovoltaic Arrays by Machine Learning Techniques

An analogy between various machine-learning techniques for detecting construction materials in digital images

Comparison of multi layer perceptron (MLP) and radial basis function (RBF) for construction cost estimation: the case of Turkey

Support Vector Machines and Multilayer Perceptron Networks Used to Evaluate the Cyanotoxins Presence from Experimental Cyanobacteria Concentrations in the Trasona Reservoir (Northern Spain)

Opinion mining for Thai restaurant reviews using neural networks and mRMR feature selection

A comparative study of artificial neural network (MLP, RBF) and support vector machine models for river flow prediction

Forecasting performance of LS-SVM for nonlinear hydrological time series

Bed load sediment transport estimation in a clean pipe using multilayer perceptron with different training algorithms

Estimation of dissolved oxygen by using neural networks and neuro fuzzy computing techniques

An analogy between various machine-learning techniques for detecting construction materials in digital images

Similar Documents

Detecting Partial Shadowing and Mismatching Phenomena in Photovoltaic Arrays by Machine Learning Techniques

An analogy between various machine-learning techniques for detecting construction materials in digital images

Comparison of multi layer perceptron (MLP) and radial basis function (RBF) for construction cost estimation: the case of Turkey

Support Vector Machines and Multilayer Perceptron Networks Used to Evaluate the Cyanotoxins Presence from Experimental Cyanobacteria Concentrations in the Trasona Reservoir (Northern Spain)

Opinion mining for Thai restaurant reviews using neural networks and mRMR feature selection

A comparative study of artificial neural network (MLP, RBF) and support vector machine models for river flow prediction

Forecasting performance of LS-SVM for nonlinear hydrological time series

Bed load sediment transport estimation in a clean pipe using multilayer perceptron with different training algorithms

Estimation of dissolved oxygen by using neural networks and neuro fuzzy computing techniques

An analogy between various machine-learning techniques for detecting construction materials in digital images