Analyses of stone surfaces by optical methods

Ornamental stone products are generally used for decorative cladding. A major quality parameter is their aesthetical appearance, which directly impacts their commercial value. The surface quality of stone products depends on the presence of defects both due to the unpredictability of natural materials and to the actual manufacturing process. This work starts reviewing the literature about optical methods for stone surface inspection. A classification is then proposed focusing on their industrial applicability in order to provide a guideline for future investigations. Three innovative systems are proposed and described in details: a vision system, an optical profilometer and a reflectometer for the inspection of polished, bush-hammered, sand-blasted, flame-finished, waterjet processed, and laser engraved surfaces

Robust and efficient automated detection of tooling defects in polished stone

The automated detection of process-induced defects such as tooling marks is a common and important problem in machine vision. Such defects are often distinguishable from natural flaws and other features by their geometric form, for example their circularity or linearity. This paper discusses the automated inspection of polished stone, where process-induced defects present as circular arcs. This is a particularly demanding circle detection problem due to the large radii and disrupted form of the arcs, the complex nature of the stone surface, the presence of other natural flaws and the fact that each circle is represented by a relatively small proportion of its total boundary. Once detected and characterized, data relating to the defects may be used to adaptively control the polishing process. We discuss the hardware requirements of imaging such a surface and present a novel implementation of a randomised circle detection algorithm that is able to reliably detect these defects. The algorithm minimizes the number of iterations required, based on a failure probability specified by the user, thus providing optimum efficiency for a specified confidence whilst requiring no prior knowledge of the image. The probabilities of spurious results are also analyzed, and an optimization routine introduced to address the inaccuracies often associated with randomized techniques. Experimental results demonstrate the validity of this approach. © 2005 Published by Elsevier B.V