Author Archives: Xianghua Xie

About Xianghua Xie

csvision.swan.ac.uk

Radial Basis Function based Level Set Interpolation and Evolution for Deformable Modelling

We present a study in level set representation and evolution using radial basis functions (RBFs) for active contour and active surface models. It builds on recent works by others who introduced RBFs into level sets for structural topology optimisation. Here, we introduce the concept into deformable models and present a new level set formulation able to handle more complex topological changes, in particular perturbation away from the evolving front. In the conventional level set technique, the initial active contour/surface is implicitly represented by a signed distance function and periodically re-initialised to maintain numerical stability. We interpolate the initial distance function using RBFs on a much coarser grid, which provides great potential in modelling in high dimensional space. Its deformation is considered as an updating of the RBF interpolants, an ordinary differential equation (ODE) problem, instead of a partial differential equation (PDE) problem, and hence it becomes much easier to solve. Re-initialisation is found no longer necessary, in contrast to conventional finite difference method (FDM) based level set approaches. The proposed level set updating scheme is efficient and does not suffer from self-flattening while evolving, hence it avoids large numerical errors. Further, more complex topological changes are readily achievable and the initial contour or surface can be placed arbitrarily in the image. These properties are extensively demonstrated on both synthetic and real 2D and 3D data. We also present a novel active contour model, implemented with this level set scheme, based on multiscale learning and fusion of image primitives from vector-valued data, e.g. colour images, without channel separation or decomposition.

Xianghua Xie and Majid Mirmehdi, Radial Basis Function based Level Set Interpolation and Evolution for Deformable Modelling, Image and Vision Computing, volume 29, issues 2-3, 167 Р177, February 2011.

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Active Contouring Based on Gradient Vector Interaction and Constrained Level Set Diffusion

This paper presents an extension of our recently introduced MAC model to deal with the initialization dependency problem that commonly appears in edge-based approaches. Its dynamic force field, unique bidirectionality, and constrained diffusion-based level set evolution provide great freedom in contour initialization and show significant improvements in initialization independency compared to other edge-based techniques. It can handle more sophisticated topological changes than splitting and merging. It provides new potentials for edge-based active contour methods, particularly when detecting and localizing objects with unknown location, geometry, and topology.

Xianghua Xie, Active Contouring Based on Gradient Vector Interaction and Constrained Level Set Diffusion, IEEE Transactions on Image Processing (T-IP), volume 19, number 1, pages 154 Р164, IEEE CS Press, January 2010.

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RIVIC Graduate School: Visual Computing

The 2010 Wales RIVIC Visual Computing Graduate School is a four-day meeting, bringing distinguished international scientists in computer graphics, computer vision, image processing, visualization, and other areas of visual computing together to share their expertise, knowledge, wisdom, and vision with young researchers (e.g. PhD students and PostDocs). The school provides a stimulating opportunity for young researchers and Ph.D. students. The participants will benefit from direct interaction and discussions with leaders in Visual Computing. Participants will also have the possibility to present the results of their research, and to interact with their scientific peers, in a friendly and constructive environment. The Graduate School offers scientists and researchers a rare opportunity to explore the future research directions of visual computing, especially the convergence of its different areas. The event features insightful talks from keynote speakers, research presentations, discussion panels, and PhD forums. Its informal social events provide young researchers a valuable opportunity to exchange research experience and explore potentials for collaboration.

MAC: Magnetostatic Active Contour Model

We propose an active contour model using an external force field that is based on magnetostatics and hypothesized magnetic interactions between the active contour and object boundaries. The major contribution of the method is that the interaction of its forces can greatly improve the active contour in capturing complex geometries and dealing with difficult initializations, weak edges and broken boundaries. The proposed method is shown to achieve significant improvements when compared against six well-known and state-of-the-art shape recovery methods, including the geodesic snake, the generalized version of GVF snake, the combined geodesic and GVF snake, and the charged particle model.

Xianghua Xie and Majid Mirmehdi, MAC: Magnetostatic Active Contour Model, IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), volume 30, number 4, pages 632 – 646, IEEE CS Press, April 2008.

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