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dc.contributor.advisorCitrin, David S.
dc.contributor.authorRashidi, Abbas
dc.date.accessioned2013-09-20T13:30:41Z
dc.date.available2013-09-20T13:30:41Z
dc.date.created2013-08
dc.date.issued2013-07-17
dc.date.submittedAugust 2013
dc.identifier.urihttp://hdl.handle.net/1853/49122
dc.description.abstractDigital images acquired at construction sites contain valuable information useful for various applications including As-built documentation of building elements, effective progress monitoring, structural damage assessment, and quality control of construction material. As a result there is an increasing need for effective methods to recognize different building materials in digital images and videos. Pattern recognition is a mature field within the area of image processing; however, its application in the area of civil engineering and building construction is only recent. In order to develop any robust image recognition method, it is necessary to choose the optimal machine learning algorithm. To generate a robust color model for building material detection in an outdoor construction environment, a comparative analysis of three generative and discriminative machine learning algorithms, namely, multilayer perceptron (MLP), radial basis function (RBF), and support vector machines (SVMs), is conducted. The main focus of this study is on three classes of building materials: concrete, plywood, and brick. For training purposes a large-size data set including hundreds of images is collected. The comparison study is conducted by implementing necessary algorithms in MATLAB and testing over hundreds of construction-site images. To evaluate the performance of each technique, the results are compared with a manual classification of building materials. In order to better assess the performance of each technique, experiments are conducted by taking pictures under various realistic jobsite conditions, e.g., different ranges of image resolutions, different distance of camera from object, and different types of cameras.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectImage processing
dc.subjectConstruction material
dc.subjectBrick
dc.subjectConcrete
dc.subjectPlywood
dc.subjectMachine learning
dc.subject.lcshSupport vector machines
dc.subject.lcshPattern perception
dc.subject.lcshPerceptrons
dc.titleEvaluating the performance of machine-learning techniques for recognizing construction materials in digital images
dc.typeThesis
dc.description.degreeM.S.
dc.contributor.departmentElectrical and Computer Engineering
thesis.degree.levelMasters
dc.contributor.committeeMemberLanterman, Aaron
dc.contributor.committeeMemberAshuri, Baabak
dc.date.updated2013-09-20T13:30:41Z


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