Makale Koleksiyonu

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http://hdl.handle.net/20.500.14701/60605

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Browsing by Author "Çipiloğlu Yıldız, Zeynep"

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    A framework for applying the principles of depth perception to information visualization
    (ACM, 2010) Çipiloğlu Yıldız, Zeynep; Bülbül, Abdullah; Capin, Tolga; Çipiloğlu Yıldız, Zeynep; Fakülteler > Mühendislik Ve Doğa Bilimleri Fakültesi > Bilgisayar Mühendisliği Bölümü
    During the visualization of 3D content, using the depth cues selectively to support the design goals and enabling a user to perceive the spatial relationships between the objects are important concerns. In this novel solution, we automate this process by proposing a framework that determines important depth cues for the input scene and the rendering methods to provide these cues. While determining the importance of the cues, we consider the user’s tasks and the scene’s spatial layout. The importance of each depth cue is calculated using a fuzzy logic–based decision system. Then, suitable rendering methods that provide the important cues are selected by performing a cost-profit analysis on the rendering costs of the methods and their contribution to depth perception. Possible cue conflicts are considered and handled in the system. We also provide formal experimental studies designed for several visualization tasks. A statistical analysis of the experiments verifies the success of our framework.
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    A perceptual quality metric for dynamic triangle meshes
    (Springer International Publishing, 2017) Çipiloğlu Yıldız, Zeynep; Capin, Tolga; Çipiloğlu Yıldız, Zeynep; Fakülteler > Mühendislik Ve Doğa Bilimleri Fakültesi > Bilgisayar Mühendisliği Bölümü
    A measure for assessing the quality of a 3D mesh is necessary in order to determine whether an operation on the mesh, such as watermarking or compression, affects the perceived quality. The studies on this field are limited when compared to the studies for 2D. In this work, we aim a full-reference perceptual quality metric for animated meshes to predict the visibility of local distortions on the mesh surface. The proposed visual quality metric is independent of connectivity and material attributes. Thus, it is not associated to a specific application and can be used for evaluating the effect of an arbitrary mesh processing method. We use a bottom-up approach incorporating both the spatial and temporal sensitivity of the human visual system. In this approach, the mesh sequences go through a pipeline which models the contrast sensitivity and channel decomposition mechanisms of the HVS. As the output of the method, a 3D probability map representing the visibility of distortions is generated. We have validated our method by a formal user experiment and obtained a promising correlation between the user responses and the proposed metric. Finally, we provide a dataset consisting of subjective user evaluation of the quality of public animation datasets.
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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.