Digital Image Processing Methods

The only practical method of large-scale fragmentation measurement currently available is digital image processing (DIP) in which photographs or video images are analysed using computer-based image processing techniques. Image analysis is the process by which the size distribution of particles in the material of concern is identified in the image and corrected by stereological methods (Hunter et al 1990).

Digital image analysis methods have a number of advantages over the other methods outlined above in terms of:

• speed of sampling;

• non-disruptive method of sampling;

• ability to analyse many samples at a smaller expense; and

• ease of practical application.

There are three stages in the image analysis process - sampling, image acquisition and image analysis. Sampling is the process of obtaining “representative” images of the fragmented material being analysed. Image acquisition involves taking images of sufficient quality and resolution for successful analysis. As has been noted previously, image analysis itself is the process by which the size distribution of fragments is identified from the images and corrected by stereological methods.

There are potentially several sources of significant error in all vision based granulometry systems - sampling errors, poor edge net fidelity, scaling errors, processing errors, and missing fines.

Sampling errors result from systematic bias in the process of taking an image of the

fragmentation. They have the potential to be the most serious of all errors. They occur if the camera is pointed at a place in the muck pile where coarse blocks or zones of fines dominate. This topic has been explored by Maerz (1996).

Sampling errors are a function of the type, scale and number of images collected. In this context, the type of image collected refers to the location and state of the material being sampled, and to the quality of the images produced. It is essential that a set of representative images of the material be captured.

A number of precautions should be taken when collecting images in the field:

• when selecting an area of broken rock, the particle boundaries should be clearly visible for good particle delineation. Sometimes the coarse fragments are partly obscured by fines and are not interpreted as separate fragments;

Chapter 4: Fragmentation Assessment

• shadows along the fragment edges should be minimized; and

• several samples should be taken around each whole area and several zoomed images should be taken to account for fines.

A sufficiently large number of images should be taken to ensure adequate statistical sampling of the muck pile. The number of images required cannot be easily defined. Kemeny (1994) suggests that the number is usually between 8 and 20, while Palangio and Franklin (1996) recommend that at least 8 to 12 images be acquired. Smaller fragment size fractions require less material to be sampled for accuracy than do larger size fractions.

Poor delineation of individual fragments also produces erroneous results. Poor delineation

arises from a combination of two sources:

• poor images (eg contrast too low or too high, image too grainy, lighting inadequate or uneven), or the size of the fragments in the image is too small; and

• highly textured rock in which shadows and/or colouring on the surface of the fragments are as prominent as the shadows between rock fragments.

Where the smallest fragments in a distribution are not delineated on the image, either because they are too small relative to the image to be resolved or they have fallen in and behind larger fragments, there is clearly a bias towards over-representing the coarse end of the size distribution. Where the distribution has a relatively narrow size range (well sorted or poorly graded), problems of this type do not normally arise. However, where the distribution has a relatively wider size range (poorly sorted or well graded), typically with size differences of more than one order of magnitude, missing fines can affect the measurement results.

4.3.3 Examples of DIP Systems

A number of digital image processing (DIP) systems and associated analytical techniques are available for use in mining applications. These methods may employ either manual or automatic image input. Manual input of images involves the manual digitisation of the particle outlines from a photograph and as a result is very slow and time consuming. An advantage of this method is that it allows for the human interpretation of indistinct particle edges that may otherwise cause errors. A disadvantage is that sampling errors are likely to increase because fewer images can be processed in a given time.

Automatic image input allows for more rapid processing of images because the computer identifies particle outlines. This method requires good contrast between the particles and the background in the image. This presents a particular difficulty in some underground mining applications.

One of the disadvantages of photographic methods is that they can process only the surface fragment distribution and must assume that the surface size distribution is representative of the fragment distribution of the entire pile. This disadvantage certainly applies to images of muck piles taken at caving drawpoints. It may be minimised when the ore is spread evenly to shallow depths on conveyor belts, for example. For this reason, most of the currently available systems are better suited to making fragmentation measurements on conveyor belts than in caving drawpoints. Three of the currently available DIP systems will be introduced below for purposes of illustration.

FRAGSCAN

FRAGSCAN is an automatic image processing system developed by Schleifer and Tessier (1996) for assessing fragmentation distributions using images of the visible parts of muck piles. This system differs from other image processing systems in that it does not use edge detection to identify the particles, but uses an algorithm to separate them into a series of size classes, simulating sieving through the corresponding mesh sizes. Surface areas are converted to volumes or weights by assuming a spherical model and using experiments on small-scale rock piles to compensate for overlap.

Schleifer and Tessier (1996) initially applied FRAGSCAN to three different problems. The first application was to compute the size distribution of material carried in haul trucks to optimise blasting parameters for crushing operations in a quarry. In this application it was found that the system is sensitive to fragmentation variation. The second application used FRAGSCAN on a conveyor belt to check the proportion of large blocks on the belt. The third application involved the use of still photographs for quality control of rock blocks prior to export.

WipFrag

WipFrag is an automated image-based granulometry system that uses digital image analysis of photographs and videotape images to determine the size distribution. WipFrag analyses images from sources such as roving camcorders, fixed photographs or digital files. The system comes with an on-board video amplifier and has manual or automatic gain and offset adjustments to account for lighting effects. In the original WipFrag system, images were most frequently acquired through the use of roving camcorders (Maerz et al 1996).

Identification of blocks is done in a two-stage process. The initial stage uses thresholding and gradient operators to detect faint shadows between adjacent blocks. The second stage uses reconstruction techniques to delineate the blocks that are only partly outlined during the first stage (Maerz et al 1996).

Chapter 4: Fragmentation Assessment

Industry experience with the original WipFrag and other systems has shown that sampling errors may be significant with manual camera operation. As a result, Maerz and Palangio (1999) concluded that a preferred approach was to measure fragmentation on-line either on a conveyor belt or as the material falls off the belt. This led to the development of WipFrag System II for automated on-line digital image processing. This system has a variety of options for communication with process control equipment. It may not be ideally suited for underground application, although on-line time-lapse video photography has been used by INCO to characterise the fragmentation at drawpoints (Maerz and Palangio 1999, Preston and Likdea 1996).

Split

Split is an image processing program designed to compute the size distribution of rock fragments from grey scale images at various stages of rock breaking in mining and mineral processing. The source of these images can be a muck pile, haul truck, leach pile, drawpoint, waste dump, stock pile or conveyor belt. The Split imaging program was developed at the University of Arizona, USA, (Kemeny et al 1993) and subsequently advanced and applied in conjunction with the Julius Kruttschnitt Mineral Research Centre (eg La Rosa et al 2001). It operated initially on Macintosh computers but a PC version is now available. The program is based on Image, a public-domain image processing program developed by the National Institutes of Health (NIH), USA (Kemeny et al 1993). Since its original introduction, Split has undergone further development and improvement, particularly in the area of fines recognition and in its software features (Kemeny et al 1999, La Rosa et al 2001).

Kemeny et al (1999) describe the basic steps in the Split system in the following terms: 1. Acquire digital images, either automatically or manually.

2. Pre-process the images to correct for lighting problems and to screen for unacceptable images.

3. Delineate the individual fragments in each image using digital image processing algorithms (Wu and Kemeny 1992).

4. Apply statistical algorithms to the 2-D particle areas in each image to determine 3-D particle volumes.

5. Statistically correct 3-D volumes for overlap and shape and determine histogram of particle volumes.

7. Process multiple images together to get an average distribution (including images taken at different scales).

8. Data output to the screen, hard disk and network.

Split is available in two formats. One is a user-controlled version which processes a group of images and the other is fully automated and operates continuously on images obtained from an on-line digital video camera. Both formats use the same algorithms for delineating particles and computing size distributions. The user-controlled version, called Split Desktop, is normally used for processing images from blast muck piles and drawpoints. The fully automated system is called Split Online, and can be used to process images taken from moving conveyor belts or tipping dumpers.

4.3.4 Validation Studies

A range of validation and calibration studies have been carried out on the Split and other digital imaging systems in recent years (eg Kemeny et al 1999, Kojovic et al 1998, Liu and Tran 1996, Maerz and Zhou 1999). Schleifer et al (1999) discussed the difficulties, including sampling, involved in using what are usually small-scale measurements to assess the fragmentation distributions of large and largely unseen masses of broken rock. They point out that this problem is generically similar to that encountered in sampling three-dimensional rock masses for geomechanics purposes discussed in some detail in Chapter 2.

For validation studies of Split Desktop which is of particular interest here, images are taken of rock particles in field situations and processed by Split and the rock particles are also screened using traditional methods. Validation studies of Split Online involve stopping the conveyor belt, removing and screening the belt material, and comparing the screening results with the image processing results. In general, belt validation studies are easier to carry out than muck pile validations since the screening of much less material is involved. Kemeny et al (1999) report validation studies using the improved version of Split in which accuracies of less than 10% were achieved for sizes down to less than 1 mm.

In 1995, the Noranda Technology Centre, Canada, conducted a series of validation tests. The versions of three systems, Fragscan, WipFrag, and Split, then available were used to measure the size distribution of a backfill muck pile from the Holloway Joint Venture, and the results were compared with those obtained from sieving. A pile of fragmented material was divided into four parts. One part was sieved and the other three were spread out, imaged, and analysed with the three programs. The results of these tests indicated that Split and WipFrag produced results that were similar and closer to the sieving results than FragScan (Liu and Tran 1996). Other results of interest were:

Chapter 4: Fragmentation Assessment

• all three systems under-estimated fine material, with FragScan under-estimating fine sizes more than the other two systems;

• for coarser materials, the difference between the FragScan results and results from the other two systems decreased;

• the Split system used lower resolution images and showed improvements when higher resolution images were used; and

• FragScan and Split can be applied to fully automated operations but WipFrag demonstrated more flexibility in the manual mode.

4.3.5 Application of DIP Systems to Caving

In block and panel caving, a proper assessment of drawpoint production and performance history requires the following data:

• fragmentation size distribution (from random surveys);

• secondary breakage activity (frequency and unit costs);

• tonnes drawn between secondary breakage activity;

• hangups (type and frequency);

• tonnes drawn between hangups; and

• drawpoint damage, repair and availability.

Most of these factors will be discussed in Chapters 6 and 7. Knowledge of the size distribution of the broken ore reporting to drawpoints is required for the range of purposes outlined in Section 4.1. Digital imaging systems such as those discussed above can be used for estimating the fragmentation size distribution. However, it should be noted that most image processing systems are more suited to open pit environments and to environments having sufficient and even lighting. Provided good quality images of underground drawpoints can be obtained, current digital image analysis systems can produce acceptable results.

As an illustration, an example will be given of a digital imaging exercise carried out at a sublevel caving operation. The objective was to assess the fragmentation resulting from different blast designs. This example illustrates the application of the Split system to a case in which the fragment size was relatively small in comparison to the fragmentation typically arising from the natural caving of the stronger orebodies now being mined by block and panel caving methods.

In this example, photographs were taken using a Nikon Coolpix digital camera. Initially, a 315 mm long torch was used for scale. It was later replaced by a 910 mm cardboard tube placed horizontally at the bottom of the muck pile. Scaling of the images is essential for subsequent analysis. Photographs were taken 5 m back from the foot of the muck pile. An example of one of the photographs is shown in Figure 4.2a.

Figure 4.2: (a) Drawpoint image, and (b) segmented image at a sublevel caving drawpoint

As has been noted, the Split system has an automated data analysis module. Nevertheless, a user can edit the results manually. Figure 4.2b is an example of the output binary file showing the delineated particles. Grey areas indicate parts of the photograph that have been edited out of the sizing process and black areas denote parts of the photograph estimated to be fines. From this image, the drawpoint fragmentation distribution shown in Figure 4.3 was calculated using Split. As was noted in Section 4.3.2, several images of the muck pile are required in order to obtain a reliable estimate of the fragmentation distribution.

Figure 4.3: Fragmentation analysis output Particle Size (mm)

Chapter 4: Fragmentation Assessment

The JKMRC applied the Split system at the Northparkes E26 Lift 1 block cave to estimate the drawpoint fragmentation during the early development of the cave. The exercise was terminated following the arrest of caving and the introduction of hydraulic fracturing to induce caving (van As and Jeffrey 2000). In general, provided the lighting is adequate, digital image analysis techniques should give better results for block and panel caving than for sublevel caving because of the larger sizes of the fragments produced by the natural caving of rock masses such as those encountered at Northparkes. As an example, Figure 4.4 is a photograph of a drawpoint in the El Teniente Esmeralda block cave. The automatic segmentation of this image produced by the Split system is shown in Figure 4.5. On the basis of the experience outlined here, it is concluded that the use of image analysis techniques for drawpoint fragmentation assessment in caving mines is feasible provided there is sufficient lighting and minimum air borne dust.

Figure 4.5: Automatic segmentation of the image shown in Figure 4.4