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 based on a statistical evaluation using the Welch t-test and display properties consistent with sintering theory and results from traditional pellet diameter measurements on the heated samples.

Knowledge about pellet microstructure such as porosity and oxidation degree is essential in improving the pellet macro behavior such as structural integrity and reduction properties. Manual optical microscopy is commonly used to find such information but is both highly time consuming and operator dependent. This paper presents research to automate image capture and analysis of entire cross-sections of baked iron ore pellets to characterize proportions of magnetite, hematite, and other components.The presented results cover: semi-automated image acquisition of entire pellets, separation of pellet and epoxy and calculation of total percentages of magnetite, hematite and pores. Using the Leica Qwin microscope software and a segmentation method based on Otsu thresholding these three objectives have been achieved with the phases labelled as magnetite, hematite and pores & additives. Furthermore, spatial distributions of magnetite, hematite and pores & additives are produced for each pellet, graphing proportions in relation to the distance to the pellet surface. The results are not directly comparable to a chemical analysis but comparisons with manual segmentation of images validates the method. Different types of pellets have been tested and the system has produced robust results for varying cases.

Abstract. A priority-based method for pixel reconstruction and incremental hole filling in incomplete images and 3D surface data is presented. The method is primarily intended for reconstruction of occluded areas in 3D surfaces and makes use of a novel prioritizing scheme, based on a pixelwise defined confidence measure, that determines the order in which pixels are iteratively reconstructed. The actual reconstruction of individual pixels is performed by interpolation using normalized convolution. The presented approach has been applied to the problem of reconstructing 3D surface data of a rock pile as well as randomly sampled image data. It is concluded that the method is not optimal in the latter case, but the results show an improvement to ordinary normalized convolution when applied to the rock data and are in this case comparable to those obtained from normalized convolution using adaptive neighborhood sizes.