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Dive into the research topics where Matthew J. Thurley is active.

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Featured researches published by Matthew J. Thurley.


Computer Vision and Image Understanding | 2005

Identifying, visualizing, and comparing regions in irregularly spaced 3D surface data

Matthew J. Thurley; Kim Chew Ng

Image segmentations have been performed to identify the surface fragmentation of rock piles using 3D surface data, and quantified. The advantages for fragmentation measurement using image analysis are significant and include: quantifying image segmentation performance in isolation of the downstream processes of fragment classification and size distribution calculation, utilization of 3D data to overcome various limitations of photographic-based image analysis, and the capacity to use 3D fragment data to eliminate the misclassification of partially visible fragments as smaller entirely visible fragments. The segmentation results have been quantified by comparison with the 3D surface data of each individual rock fragment. Mathematical morphology and image segmentation algorithms have been extended from greyscale image-based definitions and applied to irregularly spaced 3D coordinate surface data. 3D coordinate surface data can now be morphologically processed directly in 3D, segmented, visualized, and directly compared to the actual surface fragmentation in order to quantify the results.


IEEE Journal of Selected Topics in Signal Processing | 2012

Morphology-Based Crack Detection for Steel Slabs

Anders Landström; Matthew J. Thurley

Continuous casting is a highly efficient process used to produce most of the world steel production tonnage, but can cause cracks in the semi-finished steel product output. These cracks may cause problems further down the production chain, and detecting them early in the process would avoid unnecessary and costly processing of the defective goods. In order for a crack detection system to be accepted in industry, however, false detection of cracks in non-defective goods must be avoided. This is further complicated by the presence of scales; a brittle, often cracked, top layer originating from the casting process. We present an approach for an automated on-line crack detection system, based on 3D profile data of steel slab surfaces, utilizing morphological image processing and statistical classification by logistic regression. The initial segmentation successfully extracts 80% of the crack length present in the data, while discarding most potential pseudo-defects (non-defect surface features similar to defects). The subsequent statistical classification individually has a crack detection accuracy of over 80% (with respect to total segmented crack length), while discarding all remaining manually identified pseudo-defects. Taking more ambiguous regions into account gives a worst-case false classification of 131 mm within the 30 600 mm long sequence of 150 mm wide regions used as validation data. The combined system successfully identifies over 70% of the manually identified (unambiguous) crack length, while missing only a few crack regions containing short crack segments. The results provide proof-of-concept for a fully automated crack detection system based on the presented method.


IEEE Journal of Selected Topics in Signal Processing | 2012

Fast Morphological Image Processing Open-Source Extensions for GPU Processing With CUDA

Matthew J. Thurley; Victor Danell

GPU architectures offer a significant opportunity for faster morphological image processing, and the NVIDIA CUDA architecture offers a relatively inexpensive and powerful framework for performing these operations. However, the generic morphological erosion and dilation operation in the CUDA NPP library is relatively naive, and performance scales expensively with increasing structuring element size. The objective of this work is to produce a freely available GPU capability for morphological operations so that fast GPU processing can be readily available to those in the morphological image processing community. Open-source extensions to CUDA (hereafter referred to as LTU-CUDA) have been produced for erosion and dilation using a number of structuring elements for both 8 bit and 32 bit images. Support for 32 bit image data is a specific objective of the work in order to facilitate fast processing of image data from 3D range sensors with high depth precision. Furthermore, the implementation specifically allows scalability of image size and structuring element size for processing of large image sets. Images up to 4096 by 4096 pixels with 32 bit precision were tested. This scalability has been achieved by forgoing the use of shared memory in CUDA multiprocessors. The vHGW algorithm for erosion and dilation independent of structuring element size has been implemented for horizontal, vertical, and 45 degree line structuring elements with significant performance improvements over NPP. However, memory handling limitations hinder performance in the vertical line case providing results not independent of structuring element size and posing an interesting challenge for further optimisation. This performance limitation is mitigated for larger structuring elements using an optimised transpose function, which is not default in NPP, and applying the horizontal structuring element. LTU-CUDA is an ongoing project and the code is freely available at https://github.com/VictorD/LTU-CUDA.


Computer Vision and Image Understanding | 2008

Identification and sizing of the entirely visible rocks from a 3D surface data segmentation of laboratory rock piles

Matthew J. Thurley; Kim Chew Ng

Once segmentation of 3D surface data of a rock pile has been performed, the next task is to determine the visibility of the surface rocks. A region boundary-following algorithm that accommodates irregularly spaced 3D coordinate data is presented for determining this visibility. We examine 3D surface segmentations of laboratory rock piles and determine which regions in the segmentation correspond to entirely visible rocks, and which correspond to overlapped or partially visible rocks. This is a significant distinction as it allows accurate size determination of entirely visible rocks, separate handling of partially visible rocks, and prevents erroneous bias resulting from mishandling partially visible rocks as smaller entirely visible rocks. Literature review indicates that other rock pile sizing techniques fail to make this distinction. The rock visibility results are quantified by comparison to manual surface classifications of the laboratory piles and the size results are quantified by comparison to the sieve size.


Pattern Recognition Letters | 2013

Adaptive morphology using tensor-based elliptical structuring elements

Anders Landström; Matthew J. Thurley

Mathematical Morphology is a common strategy for non-linear filtering of image data. In its traditional form the filters used, known as structuring elements, have constant shape once set. Such rigid structuring elements are excellent for detecting patterns of a specific shape, but risk destroying valuable information in the data as they do not adapt in any way to its structure. We present a novel method for adaptive morphological filtering where the local structure tensor, a well-known method for estimation of structure within image data, is used to construct adaptive elliptical structuring elements which vary from pixel to pixel depending on the local image structure. More specifically, their shape varies from lines in regions of strong single-directional characteristics to disks at locations where the data has no prevalent direction.


Pattern Recognition Letters | 2014

Adaptive mathematical morphology - A survey of the field

Vladimir Urić; Anders Landström; Matthew J. Thurley; Cris L. Luengo Hendriks

We present an up-to-date survey on the topic of adaptive mathematical morphology. A broad review of research performed within the field is provided, as well as an in-depth summary of the theoretical advances within the field. Adaptivity can come in many different ways, based on different attributes, measures, and parameters. Similarities and differences between a few selected methods for adaptive structuring elements are considered, providing perspective on the consequences of different types of adaptivity. We also provide a brief analysis of perspectives and trends within the field, discussing possible directions for future studies.


Mining Technology | 2015

Measuring blast fragmentation at Esperanza mine using high-resolution 3D laser scanning

I. Onederra; Matthew J. Thurley; A. Catalan

Abstract Image analysis as a technique for fragmentation measurement of rock piles has been the subject of research since the 1980s, and to date, run of mine (ROM) fragmentation optimisation studies have primarily relied on particle size measurement using photographic-based 2D imaging systems. Disadvantages of 2D imaging systems include particle delineation errors because of variable lighting and material colour and texture variation; no direct measure of scale and perspective distortion; and inability to distinguish overlapped particles, non-overlapped particles, and areas of fines. With the development of 3D imaging technologies, there is an opportunity to develop techniques that could improve data collection and overcome the limitations of existing 2D image-based systems. This paper describes the first attempt to use 3D high-resolution laser scanning techniques to quantify ‘whole of muckpile’ fragmentation from full-scale production blasting. During two monitoring campaigns in 2013, high-resolution laser scanning data were collected from production blasts at Esperanza mine (Antofagasta Minerals Group). Fully automated analysis of the 3D data was possible in all cases where the data were of sufficiently high resolution. Manual pre-processing was required when the data were of low resolution to specify the region of fines. Overall results indicated that ROM fragmentation requirements were meeting specified targets, despite the marked differences in powder factors. This was particularly the case for those blasts conducted in similar geological domains. This work has demonstrated that high-resolution laser scanning can be used as an alternative technique to measure ‘whole of muckpile’ fragmentation in production blasting.


digital image computing techniques and applications | 2013

Automated Image Segmentation and Analysis of Rock Piles in an Open-Pit Mine

Matthew J. Thurley

Measurement and image analysis of 3D surface profile data of blasted rock piles in an open-pit mine are presented. A proof-of-concept/demonstration project into determining the size distribution of the visible rocks on the pile was performed. The results demonstrate the capacity to collect high resolution 3D surface profile data using a high-end two-axis scanning laser range-finder. Furthermore, automated image analysis was applied to this data to identify and size the rocks on the pile. Areas of very fine particles, too small to individually detect, are able to be detected and classified as areas-of-fines. Detection of these areas-of-fines is extremely important as the amount of fine material is a key factor in evaluating blasting outcomes. The algorithms to perform this segmentation and classification analysis are outlined and results are shown in the form of images and sizing graphs.


IFAC Proceedings Volumes | 2009

Automated Online Measurement of Particle Size Distribution using 3D Range Data

Matthew J. Thurley

Abstract Fully automated online measurement of the size distribution of limestone fragments on conveyor belt is presented based on 3D range data collected every minute during 13 hours of production. The research establishes the necessary measurement technology to facilitate automatic control of particle breaking or aggregating processes to improve both energy efficiency and product quality. Techniques are presented covering; sizing of fragments, determination of non-overlapped and overlapped fragments, and mapping of sizing results to distributions comparable to sieving. Detailed variations in the product sieve size are shown with an abrupt change when the size range of the limestone fragments was changed.


instrumentation and measurement technology conference | 2007

Pellet Size Estimation Using Spherical Fitting

Tobias Andersson; Matthew J. Thurley; Olov Marklund

Evaluation of spherical fitting as a technique for sizing iron ore pellets is performed. Size measurement of pellet in industry is usually performed by manual sampling and sieving techniques. Automatic on-line analysis of pellet size would allow non-invasive, frequent and consistent measurement. Previous work has used an assumption that pellets are spherical to estimate pellet sizes. In this research we use a 3D laser camera system in a laboratory environment to capture 3D surface data of pellets and steel balls. Validation of the 3D data against a spherical model has been performed and demonstrates that pellets are not spherical and have physical structures that a spherical model cannot capture.

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Anders Landström

Luleå University of Technology

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Tobias Andersson

Luleå University of Technology

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Håkan Jonsson

Luleå University of Technology

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Frida Nellros

Luleå University of Technology

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Olov Marklund

Luleå University of Technology

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Olle Hagman

Luleå University of Technology

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Tobias Pahlberg

Luleå University of Technology

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Cris L. Luengo Hendriks

Swedish University of Agricultural Sciences

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Erik Johansson

Luleå University of Technology

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