Zernike Moments and Descriptors for 3D Object Processing Submitters Reference

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The description of 3D objects in a concise mathematical framework has only rather recently become suited for efficient comparisons, thanks to developments made mainly by computer scientists working on content-based web search methods. As biological imaging continues to build up 2D and 3D views of biological entities, it it crucial to develop computational approaches to efficiently search for and compare these objects. In this project we wish to build on and extend the state-of-the-art methodology we have developed for molecular shape descriptors (proteins, ligands) to suit the needs of the biological plant imaging community. This proposal focusses on the use of Zernike polynomials which have been used with success in 2D and very recently also for some 3D problems. They have a number of important advantages over other approaches such as being region rather than surface based and possessing powerful rotationally invariant features. We will evaluate their use in biological imaging, mainly the optical projection tomography and microscopy images but also for unmodelled crystallographic electron density interpretation. This will enable us to exploit our recent proof-of-principle prototype and to transform this into a robust and efficient tool for biological imaging. Such tools are important for the study of whole plant growth and development. In addition, we will investigate an extension of this approach to include elasticity, i.e. to allow for deformation, and to take this into account during shape comparison.

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