Colloquium/Seminar

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Event(s) on June 2012


  • Monday, 4th June, 2012

    Title: A Class of Operator Splitting Methods and Their Applications to Image Processing
    Speaker: Ms. Xinxin Li, Department of Mathematics, Department of Mathematics, Hong Kong
    Time/Place: 11:00  -  12:00
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: Many applications arising in image processing can be modeled into convex programming problems with separable objective functions and linear coupling constraints. A class of operator splitting methods stemmed from the augmented Lagrangian method turn out to be very efficient for this type of models. The alternating direction method of multipliers is a benchmark method of this type. In this talk, we will discuss how to develop some operator splitting methods based on the proximal point algorithm, a classical method in optimization literature. We then focus on numerical study of these splitting methods to solve some imaging problems such as image deconvolution, image inpainting, mixed noise removal, and video surveillance. Some research topics for future study will also be presented.


  • Wednesday, 13th June, 2012

    Title: OBTL Workshop
    Speaker: Dr. Gerald P Sellinger, Centre for the Enhancement of Teaching and Learning, The University of Hong Kong, Hong Kong
    Time/Place: 10:00  -  13:00
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: The workshop will include the following: 1. How does OBTL differ from traditional teaching and assessment? 2. How to apply the OBTL method to course planning, teaching and assessment? Regarding the first point, I will begin with a brief introduction to OBTL. After that, I will present a clear contrast between OBTL and the traditional way of preparing a course of study. Then I will list and discuss the three essential requirements for a course to be outcome-based. In relation to the second point, I will give the audience some practical tips for using OBTL to design courses. The tips will include: vocabulary relating to course design using OBTL, tips for writing sections of a course outline using OBTL and the steps to follow when writing a course outline using OBTL. Then, in order to reinforce the knowledge, my colleague will do an exercise with the audience in writing some sections of a course outline using the OBTL method. By the end of the workshop, participants should be able to use OBTL to design courses of study. The session should last somewhere between two and three hours.


  • Friday, 29th June, 2012

    Title: CMIV Lecture 1: On Some Variational Models for Image Segmentation
    Speaker: Professor K. Chen, Department of Mathematical Sciences, University of Liverpool, UK
    Time/Place: 14:30  -  15:30
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: Image segmentation is an important task in image processing. Variational models have proven to be reliable in various challenging applications. In this lecture, I shall first give a brief introduction to global segmentation using the Chan-Vese model as an example and related computational issues / algorithms are also mentioned. Then I'll present some recent work on local segmentation model and finally touch upon a simple model for texture segmentation.


  • Friday, 29th June, 2012

    Title: CMIV Lecture 2: Mean Curvature Regularisation with Application in Deformable Registration Models
    Speaker: Professor K. Chen, Department of Mathematical Sciences, University of Liverpool, UK
    Time/Place: 16:00  -  17:00
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: Image registration is another important task in image processing, where regularization is a major issue in designing new models. The total variation (TV) semi-norm based regulation is much well-known for image denoising and registration modelling, with recent work generalised with the help of Bregman distance. However mean curvature regularisation serves as strong competitor to the TV. In this lecture, I shall first review the mean curvature model by Lysaker-Osher-Tai (2004) and the related Zhu-Chan (2008,2012) models for image denoising. Then I briefly discuss 2 ways of speeding up the computational convergence. Finally I show how to use the mean curvatures to minimize the deformation fields in a registration model and highlight the advantages of the resulting new model.