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The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by Strathclyde researchers, including by researchers from the Physical Activity for Health Group based within the School of Psychological Sciences & Health. Research here seeks to better understand how and why physical activity improves health, gain a better understanding of the amount, intensity, and type of physical activity needed for health benefits, and evaluate the effect of interventions to promote physical activity.

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Interpreting three-dimensional shape distributions

Rea, H. and Sung, R. and Corney, J.R. and Clark, D. and Taylor, N.k. (2005) Interpreting three-dimensional shape distributions. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 219 (6). pp. 553-566. ISSN 0954-4062

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Abstract

Effective content-based shape retrieval systems would allow engineers to search databases of three-dimensional computer-aided design (CAD) models for objects with specific geometries or features. Much of the academic work in this area has focused on the development of indexing schemes based on different types of three-dimensional to two-dimensional 'shape functions'. Ideally, the shape function used to generate a distribution should be easy to compute and permit the discrimination of both large and small features. The work reported in this paper describes the properties of three new shape distributions based on computationally simple shape functions. The first shape function calculates the arithmetic difference between distributions derived (using the original D2 distance shape function) from both a three-dimensional model and its convex hull. The second shape function is obtained by sampling the angle between random pairs of facets on the object. The third shape function uses the surface orientation to filter the results of a distance distribution. The results reported in this paper suggest that these novel shape functions improve significantly the ability of shape distributions to discriminate between complex engineering parts.