Browsing by Publisher "Springer Berlin Heidelberg"

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    The long-term effect on testicular function of a mesh bioprosthesis used for inguinal hernia repair
    (Springer Berlin Heidelberg, 2010) Aydede H.
    Polypropylene mesh currently finds widespread use throughout the world in hernia repair, as it is well tolerated by the body and allows tissue ingrowth into the prosthesis [1]. Experimentally, all meshes cause an initial inflammatory tissue response in the recipient after implantation [2]. Interestingly, in most clinical studies in humans, these meshes are described as inert materials, but systematic morphological examinations of the long-term effects of meshes commonly used for hernia repair in humans are rare [3]. © 2010 Springer-Verlag Berlin Heidelberg.
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    Muscle Injuries: Strains, Contusions, and Ruptures
    (Springer Berlin Heidelberg, 2015) Dönmez G.; Dilicikik U.; Aydog S.T.; Evrenos M.K.; Tetik O.; Demirel M.; Doral M.N.
    Muscle injuries among athletic population are common and often occur during sportive activities or training. Most sports-related muscle injuries involve strains, contusions, and uncommonly ruptures. These injuries are responsible for a large proportion of time lost to competition, and particularly for all professional athletes, rapid return to training and competition is a priority. This chapter will discuss the etiology, risk factors, classification, treatment, and possible complications of common types of muscle injuries under the lights of current literature and recent trends. © Springer-Verlag Berlin Heidelberg 2012, 2015, All Rights Reserved.
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    A machine learning framework for full-reference 3D shape quality assessment
    (Springer Berlin Heidelberg, 2018) Çipiloğlu Yıldız, Zeynep; Öztireli, A. Cengiz; Capin, Tolga; Çipiloğlu Yıldız, Zeynep; Fakülteler > Mühendislik Ve Doğa Bilimleri Fakültesi > Bilgisayar Mühendisliği Bölümü
    To decide whether the perceived quality of a mesh is influenced by a certain modification such as compressionors implification, a metric for estimating the visual quality of 3D meshes is required. Today, machine learning and deep learning techniques are getting increasingly popular since they present efficient solutions to many complex problems. However, these techniques are not much utilized in the field of 3D shape perception. We propose a novel machine learning-based approach for evaluating the visual quality of 3D static meshes. The novelty of our study lies in incorporating crowdsourcing in a machine learning framework for visual quality evaluation. We deliberate that this is an elegant way since modeling human visual system processes is a tedious task and requires tuning many parameters. We employ crowdsourcing methodology for collecting data of quality evaluations and metric learning for drawing the best parameters that well correlate with the human perception. Experimental validation of the proposed metric reveals a promising correlation between the metric output and human perception. Results of our crowdsourcing experiments are publicly available for the community.