Simple texture-based liberation modelling of ores

Mineralogical analysis is often used to assess the liberation properties of particles. A direct method of estimating liberation is to actually break particles and then directly obtain liberation information from applying mineralogical analysis to each size-class of the product. Another technique is to artificially apply random breakage to the feed particle sections to estimate the resultant distribution of product particle sections. This technique provides a useful alternative estimation method. Because this technique is applied to particle sections, the actual liberation properties for particles can only be estimated by applying stereological correction. A recent stereological technique has been developed that allows the discrepancy between the linear intercept composition distribution and the particle section composition distribution to be used as guide for estimating the particle composition distribution. The paper will show results validating this new technique using numerical simulation. (C) 2004 Elsevier Ltd. All rights reserved.

Automatic texture segmentation for texture-based image retrieval

Texture-segmentation is the crucial initial step for texture-based image retrieval. Texture is the main difficulty faced to a segmentation method. Many image segmentation algorithms either can’t handle texture properly or can’t obtain texture features directly during segmentation which can be used for retrieval purpose. This paper describes an automatic texture segmentation algorithm based on a set of features derived from wavelet domain, which are effective in texture description for retrieval purpose. Simulation results show that the proposed algorithm can efficiently capture the textured regions in arbitrary images, with the features of each region extracted as well. The features of each textured region can be directly used to index image database with applications as texture-based image retrieval.