Researcher:
Tümen, Recep Sinan

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PhD Student

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Recep Sinan

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Tümen

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Tümen, Recep Sinan

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2010 - 201922020 - 20221

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Researcher Of Publications: search.filters.isAuthorOfPublication.fc58dd84-7b42-4cad-a983-d0a859634b18

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    DPFrag: trainable stroke fragmentation based on dynamic programming
    (IEEE Computer Soc, 2013) N/A; Department of Computer Engineering; Tümen, Recep Sinan; Sezgin, Tevfik Metin; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 18632
    Many computer graphics applications must fragment freehand curves into sets of prespecified geometric primitives. For example, sketch recognition typically converts hand-drawn strokes into line and arc segments and then combines these primitives into meaningful symbols for recognizing drawings. However, current fragmentation methods' shortcomings make them impractical. For example, they require manual tuning, require excessive computational resources, or produce suboptimal solutions that rely on local decisions. DPFrag is an efficient, globally optimal fragmentation method that learns segmentation parameters from data and produces fragmentations by combining primitive recognizers in a dynamic-programming framework. The fragmentation is fast and doesn't require laborious and tedious parameter tuning. In experiments, it beat state-of-the-art methods on standard databases with only a handful of labeled examples.
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    Segmentation and recognition of offline sketch scenes using dynamic programming
    (Ieee Computer Soc, 2022) N/A; N/A; Department of Computer Engineering; Tümen, Recep Sinan; Sezgin, Tevfik Metin; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; N/A; 18632
    Sketch recognition aims to segment and identify objects in a collection of hand-drawn strokes. In general, segmentation is a computationally demanding process since it requires searching through a large number of possible recognition hypotheses. It has been shown that, if the drawing order of the strokes is known, as in the case of online drawing, a class of efficient recognition algorithms becomes applicable. In this article, we introduce a method that achieves efficient segmentation and recognition in offline drawings by combining dynamic programming with a novel stroke ordering method. Through rigorous evaluation, we demonstrate that the combined system is efficient as promised, and either beats or matches the state of the art in well-established databases and benchmarks.
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    Fixation count prediction for textural scenes
    (IEEE, 2010) Department of Computer Engineering; N/A; Sezgin, Tevfik Metin; Tümen, Recep Sinan; Faculty Member; PhD Student; Department of Computer Engineering; College of Engineering; Graduate School of Sciences and Engineering; 18632; N/A
    The human eye collects visual information by means of saccades and fixations. Recent work shows that fixation locations are not arbitrary. On the contrary, they tend to cluster on the salient regions of the scene. Automatic estimation of the number of fixations on an image has uses in many applications and contexts including computer vision (e.g., robot vision, compression, salience estimation) and human-computer interaction (e.g interface usability assessment). In this study, we present an algorithm for estimating the number of fixations on parts of an image based on local descriptors using supervised regression models on the DOVES eye movements dataset. Our results suggest that in the absence of contextual information, local descriptors can be used to generate a reasonably accurate fixation intensity map of an image.