The March’s PGRs Research Presentations was held on Wed. 13th March, 2pm, Meeting Room, MC3108 (3rd floor).
This session we had the following presentations:
| Title: “A primal-dual fixed point algorithm with nonnegative constraint for CT image reconstruction“. |
Title: “Video Similarity in Compressed Domain” | |
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By: Yuchao Tang |
By: Saddam Bekhet |
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| Abstract:Computed tomography (CT) image reconstruction problems often can be solved by finding the minimizer of a suitable objective function which usually consists of a data fidelity term and a regularization term subject to a convex constraint set $C$. In the unconstrained case, an efficient algorithm called the primal-dual fixed point algorithm (PDFP$^{2}$O) has recently been developed to this problem, when the data fidelity term is differentiable with Lipschitz continuous gradient and the regularization term composed by a simple convex function (possibly non-smooth) with a linear transformation. In this paper, we propose a modification of the PDFP$^{2}$O, which allows us to deal with the constrained minimization problem. We further propose accelerated algorithms which based on the Nesterov’s accelerated method. Numerical experiments on image reconstruction benchmark problem show that the proposed algorithms can produce better reconstructed image in signal-to-noise ratio than the original PDFP$^{2}$O and state-of-the-art methods with less iteration numbers. The accelerated algorithms exhibit the fastest performance compared with all the other algorithms |
AbstractThe volume of video data is rapidly increasing, more than 4 billion hours of video are being watched each month on YouTube and more than 72 hours of video are uploaded to YouTube every minute, and counters are still running fast. A key aspect of benefiting from all that volume of data is the ability to annotate and index videos, to be able to search and retrieve them. The annotation process is time consuming and automating it, with semantically acceptable level, is a challenging task.The majority of available video data exists in compressed format MPEG-1, MPEG-2 and MPEG-4. Extraction of low level features, directly from compressed domain without decompression, is the first step towards efficient video content retrieval. Such approach avoids expensive computations and memory requirement involved in decoding compressed videos, which is the tradition in most approaches. Working on compressed videos is beneficial because they are rich of additional, pre-computed, features such as DCT coefficients, motion vectors and Macro blocks types.
The DC image is a thumbnail version that retains most of the visual features of its original full image. Taking advantage of the tiny size, timeless reconstruction and richness of visual content, the DC image could be employed effectively alone or in conjunction with other compressed domain features (e.g. AC coefficients, macro-block types and motion vectors) to represent video clips (with signature) and to detect similarity between videos for various purposes such as automated annotation, copy detection or any other higher layer built upon similarity between videos. |
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The Q/A was followed by a demonstration of the PGRs blog and discussion with PGRs (and attending staff) about the blog, BB community,…etc.