Tag Archives: Computer Vision

On the implementation and analysis of expectation maximization algorithms with stopping criterion

The Expectation Maximization (EM) algorithm is an alternative reconstruction method to the Filtered Back Projection method, providing many advantages including decreased sensitivity to noise. However the algorithm requires a large number of iterations to reach adequate convergence. Due to this, research has been carried out into accelerating the convergence rate of the EM algorithm. In this paper we present an analysis of an EM implementation which uses both OSEM and MGEM, comparing results on a per time basis with both acceleration techniques alone as well as a combination of the two methods. We provide an alternative stopping criterion based on the RMS error of the projections of the current reconstruction and compare the result with an existing variance based approach.

Andrew Ryan, Benjamin Mora and Min Chen.
IEEE International Conference on Image Processing 2012.

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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Successful MIUA Conference at Swansea

The 16th Conference on Medical Image Understanding and Analysis (MIUA) was held at Swansea University this year. Swansea University is set in rolling parkland overlooking the majestic sweep of Swansea Bay. The campus is a stone’s throw from the old fishing village of Mumbles and a short distance to the Maritime Quarter. The University enjoys a prime position overlooking Swansea Bay, the start of the famously dramatic Gower coastline. MIUA is the principal UK forum for communicating research progress within the community interested in image analysis applied to medicine and related biological science.

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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.