Diffusion pruning for rapidly and robustly selecting global correspondences using local isometry

DPFinding correspondences between two surfaces is a fundamental operation in various applications in computer graphics and related fields. Candidate correspondences can be found by matching local signatures, but as they only consider local geometry, many are globally inconsistent. We provide a novel algorithm to prune a set of candidate correspondences to those most likely to be globally consistent. Our approach can handle articulated surfaces, and ones related by a deformation which is globally nonisometric, provided that the deformation is locally approximately isometric. Our approach uses an efficient diffusion framework, and only requires geodesic distance calculations in small neighbourhoods, unlike many existing techniques which require computation of global geodesic distances. We demonstrate that, for typical examples, our approach provides significant improvements in accuracy, yet also reduces time and memory costs by a factor of several hundred compared to existing pruning techniques. Our method is furthermore insensitive to holes, unlike many other methods.

Gary K. L. Tam, Ralph R. Martin, Paul L. Rosin, Yu-Kun Lai
ACM Transactions on Graphics (TOG) 33(1):4, January 2014

Segmentation of biomedical images using shape prior

Screen Shot 2014-05-09 at 10.26.02In this article, a new level set model is proposed for the segmentation of biomedical images. The image energy of the proposed model is derived from a robust image gradient feature which gives the active contour a global representation of the geometric configuration, making it more robust in dealing with image noise, weak edges, and initial configurations. Statistical shape information is incorporated using nonparametric shape density distribution, which allows the shape model to handle relatively large shape variations. The segmentation of various shapes from both synthetic and real images depict the robustness and efficiency of the proposed method.

pdficon_largeS. Y. Yeo, X. Xie, I. Sazonov, and P. Nithiarasu, Segmentation of biomedical images using active contour model with robust image feature and shape prior, International Journal for Numerical Methods in Biomedical Engineering, Volume 30, Issue 2, pages 232–248, February 2014.

More details can be found at the Swansea Vision website.

Integrated Segmentation and Interpolation of Sparse Data

Screen Shot 2014-05-09 at 09.51.59We address the two inherently related problems of segmentation and interpolation of 3D and 4D sparse data and propose a new method to integrate these stages in a level set framework.
The interpolation process uses segmentation information rather than pixel intensities for increased robustness and accuracy. The method supports any spatial configurations of sets of 2D slices having arbitrary positions and orientations. We achieve this by introducing a new level set scheme based on the interpolation of the level set function by radial basis functions. The proposed method is validated quantitatively and/or subjec- tively on artificial data and MRI and CT scans and is compared against the traditional sequential approach, which interpolates the images first, using a state-of-the-art image interpolation method, and then segments the interpolated volume in 3D or 4D. In our experiments, the proposed framework yielded similar segmentation results to the sequential approach but provided a more robust and accurate interpolation. In particular, the interpolation was more satisfactory in cases of large gaps, due to the method taking into account the global shape of the object, and it recovered better topologies at the extremities of the shapes where the objects disappear from the image slices. As a result, the complete integrated framework provided more satisfactory shape reconstructions than the sequential approach.

pdficon_largeA. Paiement, M. Mirmehdi, X. Xie, and M. Hamilton, Integrated Segmentation and Interpolation of Sparse Data, IEEE Transactions on Image Processing (T-IP), volume 23, issue 1, pages 110-125, January 2014.

More details can be found at the Swansea Vision website.

Ian Doidge successfully defends PhD

Ian Doidge viva photo

From left: Gary, Ian, Mark and Kurt

Today, Ian Doidge successfully defended his PhD thesis: Utilising Path-Vertex Data to Improve Monte Carlo Global Illumination.

Well done Ian. Mark W. Jones was the supervisor, Markus Roggenbach the viva chair, Gary Tam the internal examiner and Kurt Debattista (Warwick) the external.

Ian’s contributions were published as Probabilistic illumination-aware filtering for Monte Carlo rendering and Mixing Monte Carlo and Progressive Rendering for Improved Global Illumination.

Transformation of an Uncertain Video Search Pipeline to a Sketch-based Visual Analytics Loop

Video search interface

Video search interface

Traditional sketch-based image or video search systems rely on machine learning concepts as their core technology. However, in many applications, machine learning alone is impractical since videos may not be semantically annotated sufficiently, there may be a lack of suitable training data, and the search requirements of the user may frequently change for different tasks. In this work, we develop a visual analytics systems that overcomes the shortcomings of the traditional approach. We make use of a sketch-based interface to enable users to specify search requirement in a flexible manner without depending on semantic annotation. We employ active machine learning to train different analytical models for different types of search requirements. We use visualization to facilitate knowledge discovery at the different stages of visual analytics. This includes visualizing the parameter space of the trained model, visualizing the search space to support interactive browsing, visualizing candidature search results to support rapid interaction for active learning while minimizing watching videos, and visualizing aggregated information of the search results. We demonstrate the system for searching spatio-temporal attributes from sports video to identify key instances of the team and player performance.

pdficon_largePowerpoint iconPhil A. Legg, David H. S. Chung, Matt L. Parry, Rhodri Bown, Mark W. Jones, Iwan W. Griffiths, Min Chen.
IEEE Transactions on Visualization and Computer Graphics, 19(12), 2109-2118.

Shape and Appearance Priors for Level Set-based LV Segmentation

We propose a novel spatiotemporal constraint based on shape and appearance and combine it with a level-set deformable model for left ventricle (LV) segmentation in four-dimensional gated cardiac SPECT, particularly in the presence of perfusion defects. The model incorporates appearance and shape information into a ‘soft-to-hard’ probabilistic constraint, and utilises spatiotemporal regularisation via a maximum a posteriori framework. This constraint force allows more flexibility than the rigid forces of shape constraint-only schemes, as well as other state of the art joint shape and appearance constraints. The combined model can hypothesise defective LV borders based on prior knowledge. The authors present comparative results to illustrate the improvement gain. A brief defect detection example is finally presented as an application of the proposed method.

IET Journal of Computer Vision, vol. 7, no.3, pp. 170-183, 2013.

Follow this link to see more publications on Computer Vision and Medical Image Analysis.

Biometric Lab

Equipment in our biometric lab - 3D/4D capture, high speed cameras, kinect

Biometric lab

We are just setting up our new biometric lab with 3D/4D cameras from 3dMD, Point Grey Gazelle high speed cameras, Kinect and Canon EOS cameras. The equipment will be primarily used for 3D and 4D reconstruction experiments by Jason Xie and Gary Tam.