A review of image analysis techniques for measuring blast fragmentation

Hunter, George; McDermott, Christopher; Miles, N.J.; Singh, Ajoy Kumar; Scoble, Malcolm J.

doi:10.1016/0167-9031(90)80003-y

“…For artificial illumination factors such as intensity, direction of lighting and diffused lighting should be considered. Generally angled lighting increases the contrast but also increases disintegration of the image during analysis (Hunter et al, 1990). For this study all the images were taken in the sunlight, and the lighting angle was close to perpendicular to the rock face.…”

Section: Causes For Sampling Errormentioning

confidence: 99%

“…A trade-off has to be found between the need to take close-up images in which fines can be resolved and the sampling error introduced by analyzing a reduced area of blasted material. It should also be noted that to achieve an adequate sample size with close-up images requires the analysis of an increased number of images thus increasing the processing time (Hunter et a!., 1990). …”

Section: Causes For Sampling Errormentioning

confidence: 99%

“…If this is not feasible, two scale markers can be used with the image being analyzed in two portions at different scales. Normally one scale marker is used, although if a batch of photographs are to be taken at a constant distance from the camera, for example, on a conveyor belt, then only the first image requires a marker (Hunter et al, 1990).…”

Section: Causes For Sampling Errormentioning

confidence: 99%

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Analysis of rock fragmentation using digital image processing

1994

International Journal of Rock Mechanics and Mining Sciences &am

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This manuscript has been reproduced from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer.The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction.In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. 2STATEMENT BY AUTHOR This thesis has been submitted in partial fulfillment of requirements for an advanced degree at the University of Arizona and is deposited in the University Library to be made available to borrowers under rules of the Library.Brief quotations from this thesis are allowable without special permission, provided that accurate acknowledgment of source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by the head of the major department or the Dean of the Graduate College when in his or her judgment the proposed use of the material is in the interests of scholarship. In all other instances, however, permission must be obtained from the author. Table Page Data obtained from sieve analysis in the laboratory 3 4 SIGNED: APPROVAL BY THESIS DIRECTOR ABSTRACTThe success of rock fragmentation due to blasting depends on many variables, such as rock properties, in-situ fracturing, and blast design. Traditionally, the size distribution of fragmented rock particles has been determined through screen sieving.Modern techniques using video images and computer image processing techniques have the potential for analyzing rock fragmentation accurately and efficiently.A procedure has been developed for analyzing rock fragmentation which uses a high-resolution video camera for capturing images in the field, and specialized computer algorithms for processing these images. First of all, computer algorithms have been developed to delineate the individual rock fragments in the images. Secondly, a set of experiments have been conducted in the laboratory, in which the two dimensional information from the images is correlated with sieve results. Based on these experiments, a set of probabilities have been determined for correctly determining the size and volume of rock fragments from two dimensional images. Using these probabilities along with the particle delineation algorithm, the size distribution for the rock fragments is calculated. The computer algorithms can also combine information from many images to take into account sampling and images taken at different scales.

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“…Specific blasting conditions can dictate which of these is the most representative method of sampling. Consideration should also be given to the fact that random or systematic sampling is a requirement in order to minimize bias, and that some methods may be preferable in this respect (Hunter et al, 1990).…”

Section: Causes For Sampling Errormentioning

confidence: 99%

Analysis of rock fragmentation using digital image processing

1994

International Journal of Rock Mechanics and Mining Sciences &am

0

View full text Add to dashboard Cite

This manuscript has been reproduced from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer.The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction.In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. 2STATEMENT BY AUTHOR This thesis has been submitted in partial fulfillment of requirements for an advanced degree at the University of Arizona and is deposited in the University Library to be made available to borrowers under rules of the Library.Brief quotations from this thesis are allowable without special permission, provided that accurate acknowledgment of source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by the head of the major department or the Dean of the Graduate College when in his or her judgment the proposed use of the material is in the interests of scholarship. In all other instances, however, permission must be obtained from the author. Table Page Data obtained from sieve analysis in the laboratory 3 4 SIGNED: APPROVAL BY THESIS DIRECTOR ABSTRACTThe success of rock fragmentation due to blasting depends on many variables, such as rock properties, in-situ fracturing, and blast design. Traditionally, the size distribution of fragmented rock particles has been determined through screen sieving.Modern techniques using video images and computer image processing techniques have the potential for analyzing rock fragmentation accurately and efficiently.A procedure has been developed for analyzing rock fragmentation which uses a high-resolution video camera for capturing images in the field, and specialized computer algorithms for processing these images. First of all, computer algorithms have been developed to delineate the individual rock fragments in the images. Secondly, a set of experiments have been conducted in the laboratory, in which the two dimensional information from the images is correlated with sieve results. Based on these experiments, a set of probabilities have been determined for correctly determining the size and volume of rock fragments from two dimensional images. Using these probabilities along with the particle delineation algorithm, the size distribution for the rock fragments is calculated. The computer algorithms can also combine information from many images to take into account sampling and images taken at different scales.

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“…These methods are not only time consuming and expensive, but also do not provide information that can be used for online control or process improvement. Image analysis is becoming increasingly attractive as the cost of computation continuous to decrease, and with it, the implementation of machine vision systems for rock size analysis is becoming more feasible [1]. Some of the current commercial applications available for this purpose, include Split and Wipfrag [2]- [4].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Rock Particle Size Distribution Using Wavelet Decomposition and Morphological Operations

Amankwah¹,

Aldrich²

2013

IJCTE

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Abstract-Estimation of the amount of fines in crushed rock from images using standard segmentation algorithms is difficult, owing to the heterogeneity and partial obscuring of the objects to be identified. In this paper, we present an image classification system to estimate the proportion of fines in aggregates of rock particles based on two-level wavelet decomposition and morphological operations. Morphological opening and closing filtering performed on rock images with structuring elements of increasing size also enhanced the class separability of the images of rock particulates. Experimental results showed that the performance of the image classification approach can be superior to standard methods.Index Terms-Image-classification system, morphological operations, rock particulate size, wavelet. I. INTRODUCTIONInformation on the size distribution of particles is important for the efficient operation of process plants in the mining industry. In the coal industry, fine material size can lead to channel flow of gases through solid burdens in reactors and significant operational inefficiencies. In the mining industry, particle size distributions are typically measured by using sieves, cyclones and sedimentation techniques. These methods are not only time consuming and expensive, but also do not provide information that can be used for online control or process improvement. Image analysis is becoming increasingly attractive as the cost of computation continuous to decrease, and with it, the implementation of machine vision systems for rock size analysis is becoming more feasible [1]. Some of the current commercial applications available for this purpose, include Split and Wipfrag [2]- [4].To analyze rock particles, four major processes need to be accomplished in a machine vision system. These are image capture and enhancement, segmentation, feature extraction and the application of domain information to recognize objects. The most difficult step in the measurement of rock particle sizes on conveyer belts is segmentation. Thus, in this paper we propose an image classification methodology for rock particulate size estimation that do not depend on image segmentation. We use wavelet transforms, owing to their multi-resolution representation of images and its efficiency with regard to textural feature extraction. Morphological operations using increasing structuring element sizes is also used to extract features that differentiate the rock images with the varying amounts of fines (rock particles with sizes below 6 mm in this case). The amounts of fines in test rock images are determined by averaging the votes of the k closest training data points in the feature space. II. WAVELET TRANSFORMThe wavelet transform is a popular tool for texture analysis, as it provides useful information about the spatial and frequency features of the images. In contrast, Fourier transforms for example, show only the frequency characteristics of the pattern [5]. Since the correlation of identical patterns at different scales can be low, mul...

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A review of image analysis techniques for measuring blast fragmentation

Cited by 44 publications

References 5 publications

Analysis of rock fragmentation using digital image processing

Analysis of rock fragmentation using digital image processing

Analysis of rock fragmentation using digital image processing

Estimation of Rock Particle Size Distribution Using Wavelet Decomposition and Morphological Operations

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