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Indexed at: search.filters.indexed.PubMedSubject: search.filters.subject.Software engineering

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Now showing 1 - 4 of 4
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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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    Real-time finite-element simulation of linear viscoelastic tissue behavior based on experimental data
    (Ieee Computer Soc, 2006) N/A; N/A; N/A; Department of Mechanical Engineering; Sedef, Mert; Samur, Evren; Başdoğan, Çağatay; Master Student; Master Student; Faculty Member; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 125489
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    Sketch-based articulated 3D shape retrieval
    (IEEE Computer Soc, 2017) Sahilliogğu, Yusuf; Department of Computer Engineering; Sezgin, Tevfik Metin; Faculty Member; Department of Computer Engineering; College of Engineering; 18632
    Sketch-based queries are a suitable and superior alternative to traditional text-and example-based queries for 3D shape retrieval. The authors developed an articulated 3D shape retrieval method that uses easy-to-obtain 2D sketches. It does not require 3D example models to initiate queries but achieves accuracy comparable to a state-of-the-art example-based 3D shape retrieval method.
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    Statistical analysis of cortical morphometrics using pooled distances based on labeled cortical distance maps
    (Springer, 2011) Hosakere, M.; Nishino, T.; Alexopoulos, J.; Todd, R. D.; Botteron, K. N.; Miller, M. I.; Ratnanather, J. Tilak; Department of Mathematics; Ceyhan, Elvan; Faculty Member; Department of Mathematics; College of Sciences; N/A
    Neuropsychiatric disorders have been demonstrated to manifest shape differences in cortical structures. Labeled Cortical Distance Mapping (LCDM) is a powerful tool in quantifying such morphometric differences and characterizes the morphometry of the laminar cortical mantle of cortical structures. Specifically, LCDM data are distances of labeled gray matter (GM) voxels with respect to the gray/white matter cortical surface. Volumes and descriptive measures (such as means and variances for each subject) based on LCDM distances provide descriptive summary information on some of the shape characteristics. However, additional morphometrics are contained in the data and their analysis may provide additional clues to underlying differences in cortical characteristics. To use more of this information, we pool (merge) LCDM distances from subjects in the same group. These pooled distances can help detect morphometric differences between groups, but do not provide information about the locations of such differences in the tissue in question. In this article, we check for the influence of the assumption violations on the analysis of pooled LCDM distances. We demonstrate that the classical parametric tests are robust to the non-normality and within sample dependence of LCDM distances and nonparametric tests are robust to within sample dependence of LCDM distances. We specify the types of alternatives for which the tests are more sensitive. We also show that the pooled LCDM distances provide powerful results for group differences in distribution of LCDM distances. As an illustrative example, we use GM in the ventral medial prefrontal cortex (VMPFC) in subjects with major depressive disorder (MDD), subjects at high risk (HR) of MDD, and healthy subjects. Significant morphometric differences were found in VMPFC due to MDD or being at HR. In particular, the analysis indicated that distances in left and right VMPFCs tend to decrease due to MDD or being at HR, possibly as a result of thinning. The methodology can also be applied to other cortical structures.