216 Prakt. Met. Sonderband 46 (2014) 
In order to respond to the characteristics of the different types of iron ore carriers their 
evaluation algorithms are realized with the measurement and automation programming 
language LabVIEW developed by National Instruments. First, before the actual processing 
routine, the quality control is performed. The quality control comprises of an edge 
detection program. Every image is executed by a differentiation filter. This filter produces 
continuous contours by highlighting each pixel where an intensity variation occurs between 
itself and its neighbors. The mean value of the highlighted pixels for every image is 
calculated and diagrammed by a boxplot. This display option gives a first impression of the 
overall image quality. Every image below a defined drop out value is removed. With this 
algorithm it is possible to eject blurred images and mostly black images (e.g. out of 
grain/sample boundary). Next step removes color fringes between dark and bright areas 
by correction of the color layers. Image noise is minimized by a suitable filter routine like 
Gaussian filter. The evaluation routine starts with phase identification by thresholding. 
Each phase gets unique intervals for RGB (red, green and blue) and HSL (hue, saturation 
and luminescence). Depending on the texture of the ore and effects like interior reflections 
it is necessary to correct the phase identification with morphology tools like erode/dilate or 
removing small particles. 
Depending « 
4. EVALUATION one calculat 
measured. * 
Hematite, magnetite and limonite are identified on considering lump ores. For pellets which is dis 
hematite and magnetite are combined to Fe oxide. In addition glass and pores are removed are 
identified (Fig. 1). The identified area to image area is accumulated image by image. correlate wit 
Referring to the basic equation from stereology Vy=Aa based on the principle of Cavalieri reduction tes 
the percentage of volume is calculated for each phase. The pellet ¢ 
measured al 
a cumulative 
the reductiol 
assumed to 
5. SUMMA 
Semi-autom: 
in terms of 
information ; 
phases a co 
for lump ore 
correlation w 
Fig. 1: Binary image of the identified phases (green- pore, red- Fe oxide, black- glass) of a 
pellet sample, image width = 0.354mm. ACKNOW! 
The quality evaluation of lump ore is based on a simulation of the reduction process by a This is a 
concentric phase front movement. It is a distance contouring by encoding a pixel value of a Technologie 
particle as a function of the location of that pixel in relation to the distance to the border of Austrian Re 
the particle. As a result every particle is subdivided into concentric shells from border to Technologies 
core. The algorithm is called Danielsson distance map by Erik Danielsson [1] (Fig. 2). acknowledge