Author Archives: Xianghua Xie

About Xianghua Xie

csvision.swan.ac.uk

Invited Paper: Image Gradient Based Level Set Methods in 2D and 3D

This paper presents an image gradient based approach to perform 2D and 3D deformable model segmentation using level set. The 2D method uses an external force field that is based on magnetostatics and hypothesized magnetic in- teractions 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. This method is then generalized to 3D by reformulating its external force based on geometrical interactions between the relative geometries of the deformable model and the object boundary characterized by image gradient.

Xianghua Xie et al.,Image Gradient Based Level Set Methods in 2D and 3D, In Deformation Models, Edited by G. Hidalgo et al., Springer, September, 2012.

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Automatic Bootstrapping and Tracking of Object Contours

A new fully automatic object tracking and segmen- tation framework is proposed. The framework consists of a motion-based bootstrapping algorithm concurrent to a shape-based active contour. The shape-based active contour uses finite shape memory that is automatically and continuously built from both the bootstrap process and the active-contour object tracker. A scheme is proposed to ensure that the finite shape memory is continuously updated but forgets unnecessary information. Two new ways of automatically extracting shape information from image data given a region of interest are also proposed. Results demonstrate that the bootstrapping stage provides important motion and shape infor- mation to the object tracker. This information is found to be es- sential for good (fully automatic) initialization of the active contour. Further results also demonstrate convergence properties of the content of the finite shape memory and similar object tracking performance in comparison with an object tracker with unlim- ited shape memory. Tests with an active contour using a fixed- shape prior also demonstrate superior performance for the pro- posed bootstrapped finite-shape-memory framework and similar performance when compared with a recently proposed active con- tour that uses an alternative online learning model.

J. Chiverton, X. Xie, and M. Mirmehdi, Automatic Bootstrapping and Tracking of Object Contours, IEEE Transactions on Image Processing (T-IP), volume 21, issue 3, pages 1231 – 1245, March 2012.

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Xianghua Xie joins IET Computer Vision editorial board

Dr. Xianghua Xie was appointed as an Associated Editor of the IET Computer Vision journal. The vision of the journal is to publish the highest quality research work that is relevant and topical to the field, but not forgetting those works that aim to introduce new horizons and set the agenda for future avenues of research in Computer Vision.

New BRU RAs

Warm welcome to Dr. Feng Zhao who joined the group as a post-doc Research Officer under the supervision of Dr. Xianghua Xie. Feng obtained his first degree in a top university in China and PhD from HongKong.

At the same time, Dr. Ben Daubney is now moving to work on the same project that is funded by NISCHR Biomedical Research Unit on Medical Image Analysis and Visualization.

Entropy Driven Hierarchical Search for 3D Human Pose Estimation

In this work a hierarchical approach is presented to efficiently estimate 3D pose from single images. To achieve this the body is represented as a graphical model and optimized stochastically. The use of a graphical representation allows message passing to ensure individual parts are not optimized using only local image information, but from information gathered across the entire model. In contrast to existing methods the poste- rior distribution is represented parametrically. A different model is used to approximate the conditional distribution between each connected part. This permits measurements of the Entropy, which allows an adaptive sampling scheme to be employed that ensures that parts with the largest uncertainty are allocated a greater proportion of the available resources. At each iteration the estimated pose is updated dependent on the Kullback Leibler (KL) divergence measured between the posterior and the set of samples used to approximate it. This is shown to improve performance and prevent over fitting when small numbers of particles are being used. A quantitative comparison is made using the HumanEva dataset that demonstrates the efficacy of the presented method.

B. Daubney and X. XieEntropy Driven Hierarchical Search for 3D Human Pose Estimation, In Proceedings of the 22nd British Machine Vision Conference, September 2011. The video of oral presentation (acceptance rate 8%). can be found from here.

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Tracking 3D Human Pose with Large Root Node Uncertainty

Representing articulated objects as a graphical model has gained much popularity in recent years, often the root node of the graph describes the global position and ori- entation of the object. In this work a method is presented to robustly track 3D human pose by permitting greater un- certainty to be modeled over the root node than existing techniques allow. Significantly, this is achieved without increasing the uncertainty of remaining parts of the model. The benefit is that a greater volume of the posterior can be supported making the approach less vulnerable to tracking failure. Given a hypothesis of the root node state a novel method is presented to estimate the posterior over the remaining parts of the body conditioned on this value. All probability distributions are approximated using a single Gaussian allowing inference to be carried out in closed form. A set of deterministically selected sample points are used that allow the posterior to be updated for each part requiring just seven image likelihood evaluations making it extremely efficient. Multiple root node states are supported and propagated using standard sampling techniques. We believe this to be the first work devoted to efficient track- ing of human pose whilst modeling large uncertainty in the root node and demonstrate the presented method to be more robust to tracking failures than existing approaches.

Ben Daubney and Xianghua XieTracking 3D Human Pose with Large Root Node Uncertainty, In Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2011.

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Geometrically Induced Force Interaction for Three-Dimensional Deformable Models

In this paper, we propose a novel 3-D deformable model that is based upon a geometrically induced external force field which can be conveniently generalized to arbitrary dimensions. This external force field is based upon hypothesized interactions between the relative geometries of the deformable model and the object boundary characterized by image gradient. The evolution of the deformable model is solved using the level set method so that topological changes are handled automatically. The relative geometrical configurations between the deformable model and the object boundaries contribute to a dynamic vector force field that changes accordingly as the deformable model evolves. The geometrically induced dynamic interaction force has been shown to greatly improve the deformable model performance in acquiring complex geometries and highly concave boundaries, and it gives the deformable model a high invariancy in initialization configurations. The voxel interactions across the whole image domain provide a global view of the object boundary representation, giving the external force a long attraction range. The bidirectionality of the external force field allows the new deformable model to deal with arbitrary cross-boundary initializations, and facilitates the handling of weak edges and broken boundaries. In addition, we show that by enhancing the geometrical interaction field with a nonlocal edge-preserving algorithm, the new deformable model can effectively overcome image noise. We provide a comparative study on the segmentation of various geometries with different topologies from both synthetic and real images, and show that the proposed method achieves significant improvements against existing image gradient techniques.

S. Yeo, X. Xie, I. Sazonov and P. Nithiarasu,Geometrically Induced Force Interaction for Three-Dimensional Deformable Models, IEEE Transactions on Image Processing (T-IP), volume 20, number 5, pages 1373 – 1387, IEEE CS Press, May 2011.

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