Papers by Keyword: Statistical Descriptors

Abstract: The microstructure of hardened cement pastes comprises of a heterogeneous agglomeration of distinct quasihomogeneous domains with variable physical, chemical, and morphological features, denoted here as material phases. Accurate material characterization rests on a precise description and quantification of underlying principal phases, focusing, in particular, on their volumetric proportions and spatial configuration within the microstructure, as these affect, to a large extent, the macroscopic properties of the composite material. A realistic cement paste microstructure used in this study was obtained from micro-computed X-ray tomography, following the application of suitable segmentation filters, highlighting and isolating the sought phase – anhydrous cement grains – for statistical analysis. The present paper then compares and assesses several implementations of a lineal path function, all applied to quantifying the phase connectedness and short-range order characteristics of the grains. The main emphasis rests on assessing the accuracy and evaluation speed of the implemented algorithms.

Abstract: Wear level of tool inserts in automated processes is tried using techniques of artificial vision. An application has been developed in Matlab that allows the acquisition of images with different resolutions and later on to process them. It is explained how the vision system used has been designed and implemented. The method for acquiring tool insert images and their treatment in the pre-processing, segmentation and post-processing is commented. First results are also presented using diverse texture descriptors. These first results must be corroborated carrying out new experiments with a bigger number of images.