This paper proposes a way of better approximating continuous, two-dimensional morphologyin the discrete domain, by allowing for irregularly sampled input and output signals. We generalizeprevious work to allow for a greater variety of structuring elements, both flat and non-flat. Experimentallywe show improved results over regular, discrete morphology with respect to the approximation ofcontinuous morphology. It is also worth noting that the number of output samples can often be reducedwithout sacrificing the quality of the approximation, since the morphological operators usually generateoutput signals with many plateaus, which, intuitively do not need a large number of samples to be correctlyrepresented. Finally, the paper presents some results showing adaptive morphology on irregularlysampled signals.

Today, a large number of people are manually grading and detecting defects in wooden lamellae in the parquet flooring industry. This paper investigates the possibility of using the ensemble methods random forests and boosting to automatically detect cracks using ultrasound-excited thermography and a variety of predictor variables. When friction occurs in thin cracks, they become warm and thus visible to a thermographic camera. Several image processing techniques have been used to suppress the noise and enhance probable cracks in the images. The most successful predictor variables captured the upper part of the heat distribution, such as the maximum temperature, kurtosis and percentile values 92–100 of the edge pixels. The texture in the images was captured by Completed Local Binary Pattern histograms and cracks were also segmented by background suppression and thresholding.

The classification accuracy was significantly improved from previous research through added image processing, introduction of more predictors, and by using automated machine learning. The best ensemble methods reach an average classification accuracy of 0.8, which is very close to the authors’ own manual attempt at separating the images (0.83).

The particle size distribution of fragmented rock in mines significantly affects operational performance of loading equipment, materials handling and crushing systems. A number of methods to measure rock fragmentation exist at present, however these systems have a number of shortcomings in an underground environment. This paper outlines the first implementation of high resolution 3D laser scanning for fragmentation measurement in an underground mine. The system is now used routinely for fragmentation measurement at the Ernest Henry sublevel-cave mine following extensive testing and calibration. The system is being used to study the effects of blasting parameters on rock fragmentation to optimise blast design. Results from 125 three dimensional scans measured the average P₅₀ and P₈₀ to be 230mm and 400mm respectively. The equipment, methodology and analysis techniques are described in detail to enable application of the measurement system at other mines.

Mathematical morphology (MM) on grayscale images is commonly performed in the discretedomain on regularly sampled data. However, if the intention is to characterize or quantify continuousdomainobjects, then the discrete-domain morphology is affected by discretization errors that may bealleviated by considering the underlying continuous signal, given a correctly sampled bandlimited image.Additionally, there are a number of applications where MM would be useful and the data is irregularlysampled. A common way to deal with this is to resample the data onto a regular grid. Often this createsproblems where data is interpolated in areas with too few samples. In this paper, an alternative way ofthinking about the morphological operators is presented. This leads to a new type of discrete operatorsthat work on irregularly sampled data. These operators are shown to be morphological operators thatare consistent with the regular, morphological operators under the same conditions, and yield accurateresults under certain conditions where traditional morphology performs poorly

This paper introduces a new operator that can be used to ap-proximate continuous-domain mathematical morphology on irregularly sampled surfaces. We define a new way of approximating the continuous domain dilation by duplicating and shifting samples according to a flat continuous structuring element. We show that the proposed algorithm can better approximate continuous dilation, and that dilations may be sampled irregularly to achieve a smaller sampling without greatly com-promising the accuracy of the result.

We present a new approach to approximate continuous-domain mathematical morphology operators. The approach is applicable to irregularly sampled signals. We define a dilation under this new approach, where samples are duplicated and shifted according to the flat, continuous structuring element. We define the erosion by adjunction, and the opening and closing by composition. These new operators will significantly increase precision in image measurements. Experiments show that these operators indeed approximate continuous-domain operators better than the standard operators on sampled one-dimensional signals, and that they may be applied to signals using structuring elements smaller than the distance between samples. We also show that we can apply the operators to scan lines of a two-dimensional image to filter horizontal and vertical linear structures.

Fragmentation in sublevel caving (SLC) is vitally important. Both gravity flow and any downstreamprocesses are affected. Fairly coarse fragmentation may lead to larger draw bodies (isolatedextraction zones) and hence potentially higher primary ore recovery and depressed/delayedwaste rock inflow from above. Fewer flow disturbances are expected by mitigating oversize. Inaddition, it requires less boulder handling and reduces wear and possible hang-up problems inorepasses. To assess the present-day functionality of large-scale SLC, a multiyear, comprehensivemeasurement program was initiated. It covers the main elements for SLC, namely blast function,fragmentation and gravity flow. The present paper focuses on fragmentation measurements. Animage acquisition system was used to document the drawpoint and the load-haul-dump (LHD)bucket for each mucking cycle. At the beginning, four buckets (about 70 t total) were sieved tovalidate the results of both 2D and 3D image analysis techniques. The fundamental and specificproblems are discussed herein. At the moment, fragmentation of the SLC rings is evaluated usinga quick rating system and a 2D fragment delineation software. The results enable a descriptionof fragmentation and its variation during mucking but also – combined with the gravity flowmeasurements – conclusions on the fragmentation for different parts of the SLC ring. Possibleinfluences on flow disturbances and ore recovery/dilution are investigated. The recent findingsallow a better understanding of breakage and flow and support future process improvements.

In general terms, sintering describes the bonding of particles into a more coherent structure, where joins form between packed particles, usually as a result of heating. Characterization of sintering is an important topic in the fields of metallurgy, steel, iron ore pellets, ceramics, and snow for understanding material properties and material strength. Characterization using image analysis has been applied in a number of these fields but is either semi-automatic, requiring human interaction in the analysis, or based on statistical sampling and stereology to characterize the sample. This paper presents a novel fully automatic image analysis algorithm to analyze and determine the degree of sintering based on analysis of the particle joins and structure. Quantitative image analysis of the sintering degree is demonstrated for samples of iron ore pellets but could be readily applied to other packed particle materials. Microscope images of polished cross-sections of iron ore pellets have been imaged in their entirety and automated analysis of hundreds of images has been performed. Joins between particles have been identified based on morphological image processing and features have been calculated based on the geometric properties and curvature of these joins. The features have been analyzed and determined to hold discriminative power by displaying properties consistent with sintering theory and results from traditional pellet diameter measurements on the heated samples, and a statistical evaluation using the Welch t-test.

To assess the present-day functionality of large-scale sublevel caving (SLC) at LKAB Kiruna a comprehensive measurement program was undertaken involving blast function, fragmentation and gravity flow. As part of this assessment, a fragmentation measurement trial was performed based on 3D imaging ofthe draw-point and corresponding bucket load of the underground load-haul-dump (LHD) excavator. 3D image data from stereo photogrammetry was collected and an automated image analysis strategy developed. A number of data sets were collected for each of the draw-point and LHD bucket, along withsieving results for four of the LHD bucket loads (totally about 70 tonnes). Two of the sieving results were used to inform the automated image analysis strategy, and two were held back as a comparison. Large variations in the visible particles are apparent when comparing corresponding draw-points and LHD bucketshighlighting the impact of sampling location and the need to measure large quantities of data in order to avoid bias from small samples. The results show that 3D imaging and analysis can produce fully automated measurement and analysis of the visible particle size distribution. Although this is not the same as the sievesize distribution it provides useful estimation of both the larger size classes and a bulk estimate of fine material below approximately 60mm. The 3D stereo photogrammetry measurement system used produced very high 3D point density but this was achieved using a custom up-sampling technique which significantly smoothed the data, removing small particles, smoothing edges, and this negatively affected the particle delineation algorithms.

Image analysis as a technique for fragmentation measurement of rock piles has been the subject of research since the 1980’s 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 due to variable lighting and material colour and texture variation; no direct measure of scale & 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 was 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 was of sufficiently high resolution. Manual pre-processing was required when the data was of low resolution to specify the region of fines. Overall results indicated that run of mine 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.