Zernike moments and descriptors for 3D object processing

点击翻译按钮获取中文摘要

A deeper understanding of mechanisms that underlie biological processes at the microscopic level, such as molecular recognition, requires models for describing three dimensional objects and how they interact. Similarly at the macroscopic level, shape and shape changes are a crucial first step in understanding growth and development. This has been recognised and biological imaging is enjoying an increased research effort worldwide and is providing stunning images and many new insights into important biological processes. A picture can say more than a thousand words. To store and index 3D images efficiently, a powerful shape description method is required. Plants represent highly challenging objects as they have convoluted forms that deviate significantly from so-called star-shape objects (which are computationally easier to handle). The underlying mathematical framework has been derived to allow many kinds of 3D shapes and distributions to be described by Zernike moments. With further development this approach can be made more efficient whilst including additional properties. The proposed research will build on the current proof-of-principle developments and translate them into robust code for efficiently comparing molecular shapes. Further enhancements to be investigated will include the extension of the current methodology to four dimensions, in which time is presented in the fourth coordinate. This will allow for shape changes (motion, deformation, growth) to be modeled within the same framework. In addition, reliable approaches for the segmentation of objects will be investigated. These developments will enable shapes to be assembled in space and missing components scanned for in shape databases. This enhancement will allow for flexible shape fitting, with applications in imaging, X-ray scattering and especially EM.