Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed
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Publication Metadata only Analyzing Playability in Multi-platform Games: A Case Study of the Fruit Ninja Game(SPRINGER INTERNATIONAL PUBLISHING AG, 2016) Aker, Cakir; Rizvanoglu, Kerem; Inal, Yavuz; Yilmaz, Alan Sarp; Marcus, A; Bahcesehir University; Galatasaray University; Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)Video games offer new perspectives for discussions and studies on user experience, which results in a change of the relevant terms in the context of gaming, replacing 'usability' with 'playability' and 'user experience' (UX) with 'player experience' (PX). PX can be inspected in various gaming platforms, which present diverse interaction methods through different peripherals, consequently uncovering the complex nature of video games. Therefore, it is critical to understand the nature of PX through user research. However, limited number of studies investigated PX and playability in detail in order to create an analysis framework for entertainment systems by referring to former UX and usability methodologies. Majority of those studies presented a set of playability heuristics on theoretical basis, which still required to be tested through empirical research in various gaming platforms. In this context, this study focuses on the qualitative analysis of multi-platform PX through a proposed playability heuristics framework derived from relevant literature. This study aims to test the proposed framework in a multi-platform game setting and thus seek ways to contribute to the establishment of a new comprehensive analysis framework to understand multi-platform PX. For this purpose, a qualitative multi-method study based on game platform diversity is designed to measure player experience with 8 users in two different gaming platforms which is based on mobile and full body gesture based interaction. Besides revealing the effect of On-Screen elements on PX such as game interface, mechanics and gameplay, the study also presents promising findings for the effect of Off-Screen aspects such as the environmental and social factors.Publication Metadata only Towards Stereoscopic Video Deblurring Using Deep Convolutional Networks(SPRINGER INTERNATIONAL PUBLISHING AG, 2021) Imani, Hassan; Islam, Md Baharul; Bebis, G; Athitsos, V; Yan, T; Lau, M; Li, F; Shi, C; Yuan, X; Mousas, C; Bruder, G; Bahcesehir UniversityThese days stereoscopic cameras are commonly used in daily life, such as the new smartphones and emerging technologies. The quality of the stereo video can be affected by various factors (e.g., blur artifact due to camera/object motion). For solving this issue, several methods are proposed for monocular deblurring, and there are some limited proposed works for stereo content deblurring. This paper presents a novel stereoscopic video deblurring model considering the consecutive left and right video frames. To compensate for the motion in stereoscopic video, we feed consecutive frames from the previous and next frames to the 3D CNN networks, which can help for further deblurring. Also, our proposed model uses the stereoscopic other view information to help for deblurring. Specifically, to deblur the stereo frames, our model takes the left and right stereoscopic frames and some neighboring left and right frames as the inputs. Then, after compensation for the transformation between consecutive frames, a 3D Convolutional Neural Network (CNN) is applied to the left and right batches of frames to extract their features. This model consists of the modified 3D U-Net networks. To aggregate the left and right features, the Parallax Attention Module (PAM) is modified to fuse the left and right features and create the output deblurred frames. The experimental results on the recently proposed Stereo Blur dataset show that the proposed method can effectively deblur the blurry stereoscopic videos.