<title>High-speed, high-resolution 3D range camera</title>

Abstract

There is a large class of machine vision inspection problems which requires detecting and characterizing voids in mostly diffuse materials. Typical examples include surface chips or gouges in ceramic parts, voids in powered metal components prior to sintering, and surface distress in construction and paving materials. There are two interrelated difficulties: achieving lighting which provides enough image contrast for reliable detection; developing image processing algorithms which can distinguish reliably between voids and image artifacts. The problems are particularly acute when inspecting composite materials containing dark inclusions which can be confused for voids, such as the aggregates in cementitious materials. We have examined this problem theoretically to understand the contrast-forming mechanisms. In this paper we present theoretical methods for modeling image contrast in images of small voids. We show how to use these methods to design appropriate illuminating systems and image processing algorithms. We validate the approach by comparing the theoretical results with experimental measurements. The prototype system we will discuss uses machine vision methods to measure air voids in cured and sectioned samples of portland cement concrete. These measurements allow estimation of air entrainment--a material property which, when properly controlled, can enhance the concrete's ability to resist microcracking and structural deterioration during repeated cycles or freezing and thawing.