A machine vision approach to rock fragmentation analysis

Simple item page
dc.contributor.authorCrida, Robert Charlesen_ZA
dc.date.accessioned2014-11-05T17:19:25Z
dc.date.available2014-11-05T17:19:25Z
dc.date.issued1996en_ZA
dc.descriptionBibliography: p. 217-223.en_ZA
dc.description.abstract[pp. i - iv missing] This thesis is concerned with the development of an instrument for the purpose of performing online measurement of rock size distribution using machine vision. This instrument has application in the gold mining industry where it could be used to measure the fragmentation of gold ore on a conveyor belt feed to an autogenous mill, for the purpose of controlling the mill. The gold ore can range in size from fine material (< 20mm) to very large rocks (0.5m). A machine vision approach is only capable of directly measuring the projected area of particles at the surface of the rock-stream. A volume distribution has to be estimated from this using a stereological method. These methods have been investigated previously and are typically error prone. They have not been investigated here. An investigation of lighting demonstrates that a diffuse lighting arrangement is suitable for this application. This would have two advantages: specular reflection from wet material is suppressed; and intensity values can be used to predict the orientation of the surface of the particles. A computational structure has been developed to identify and delineate rocks in an image for the purpose of measuring their areas. It is based on the human visual system in that it consists of a low-level preattentive vision stage and a higher-level stage of attention focusing. Multiscalar image processing techniques have also been integrated in order to improve the detection of rocks across a wide range of sizes. A performance advantage can be obtained in this way because all the algorithms can be better matched to the size of the objects being detected. Results have been obtained with an average true detection rate of 69 and a further close miss rate of 14 , with very few false alarms. The overall result is that the measured projected area distribution closely matches the true value for each test image.en_ZA
dc.identifier.apacitationCrida, R. C. (1996). <i>A machine vision approach to rock fragmentation analysis</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/9228en_ZA
dc.identifier.chicagocitationCrida, Robert Charles. <i>"A machine vision approach to rock fragmentation analysis."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 1996. http://hdl.handle.net/11427/9228en_ZA
dc.identifier.citationCrida, R. 1996. A machine vision approach to rock fragmentation analysis. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Crida, Robert Charles AB - [pp. i - iv missing] This thesis is concerned with the development of an instrument for the purpose of performing online measurement of rock size distribution using machine vision. This instrument has application in the gold mining industry where it could be used to measure the fragmentation of gold ore on a conveyor belt feed to an autogenous mill, for the purpose of controlling the mill. The gold ore can range in size from fine material (< 20mm) to very large rocks (0.5m). A machine vision approach is only capable of directly measuring the projected area of particles at the surface of the rock-stream. A volume distribution has to be estimated from this using a stereological method. These methods have been investigated previously and are typically error prone. They have not been investigated here. An investigation of lighting demonstrates that a diffuse lighting arrangement is suitable for this application. This would have two advantages: specular reflection from wet material is suppressed; and intensity values can be used to predict the orientation of the surface of the particles. A computational structure has been developed to identify and delineate rocks in an image for the purpose of measuring their areas. It is based on the human visual system in that it consists of a low-level preattentive vision stage and a higher-level stage of attention focusing. Multiscalar image processing techniques have also been integrated in order to improve the detection of rocks across a wide range of sizes. A performance advantage can be obtained in this way because all the algorithms can be better matched to the size of the objects being detected. Results have been obtained with an average true detection rate of 69 and a further close miss rate of 14 , with very few false alarms. The overall result is that the measured projected area distribution closely matches the true value for each test image. DA - 1996 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 1996 T1 - A machine vision approach to rock fragmentation analysis TI - A machine vision approach to rock fragmentation analysis UR - http://hdl.handle.net/11427/9228 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/9228
dc.identifier.vancouvercitationCrida RC. A machine vision approach to rock fragmentation analysis. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 1996 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/9228en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Electrical Engineeringen_ZA
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherElectrical Engineeringen_ZA
dc.titleA machine vision approach to rock fragmentation analysisen_ZA
dc.typeDoctoral Thesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnamePhDen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
thesis_ebe_1996_crida_rc.pdf
Size:
14.81 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
PhD / Doctoral