Browsing by Author "Aslan S."

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    Citation - Scopus: 1
    A Comparative Study of Compressed Sensing Video Encoding Gop Patterns for Stereo Distributed Video Coding
    (2012) Aslan S.; Tunalı, Turhan
    In this study, compressed sensing concepts are applied to multi-view video coding. Existing work from single view video is utilized to develop efficient GOP patterns and reference framing for stereo coding. It has been observed that the most typical choice of pattern improved the characteristics 0.4 dB with respect to the model that do not benefit from interview sparsity for all frames. Alternatives for future work are proposed. © 2012 IEEE.
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    Joint Compressive Video Coding and Analysis With Hidden Markov Model Based Weighted Reconstruction
    (2013) Aslan S.; Tunalı, Turhan
    This paper examines the performance of Hidden Markov Tree model based weights in reconstruction quality for an existing task-Aware compressive video coding system which aims object detection specifically. The existing system utilizes weights in reconstruction which are computed by tracking of the foreground object. The proposed system acquires similar average PSNR with the existing one which reported some improvement compared to the conventional unweighted reconstruction at low sampling rates. Furthermore, it is a little bit better than the existing system at higher sampling rates. It can be inferred from this study that Bayesian approaches that take account structural dependencies between transformation coefficients has the potential of improving reconstruction quality for such a compressive video coding system with object detection task. © 2013 IEEE.
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    A Novel Study on Developing a Model-Driven Visual Dictionary
    (Institute of Electrical and Electronics Engineers Inc., 2016) Aslan S.; Akgul C.B.; Sankur B.; Tunalı, Turhan
    We designed SymPaD framework, a model-driven visual dictionary construction and description method, with new shape models and quantized shape library. We demonstrate that, with this new design, the most current model-driven dictionary construction method is outperformed with even smaller dictionary in object recognition and image retrieval tasks. © 2016 IEEE.
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    Citation - Scopus: 3
    SymPaD: Symbolic patch descriptor
    (SciTePress, 2015) Aslan S.; Akgül C.B.; Sankur B.; Tunalı, Turhan
    We propose a new local image descriptor named SymPaD for image understanding. SymPaD is a probability vector associated with a given image pixel and represents the attachment of the pixel to a previously designed shape repertoire. As such the approach is model-driven. The SymPad descriptor is illumination and rotation invariant, and extremely flexible on extending the repertoire with any parametrically generated geometrical shapes and any desired additional transformation types. Copyright © 2015 SCITEPRESS - Science and Technology Publications All rights reserved.