Browsing by Subject "Rendering methods"

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    A framework for applying the principles of depth perception to information visualization
    (Association for Computing Machinery, 2013) Zeynep, C. Y.; Bulbul, A.; Capin, T.
    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. © 2013 ACM.
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    A framework for enhancing depth perception in computer graphics
    (ACM, 2010-07) Çipiloğlu, Zeynep; Bülbül, Abdullah; Çapin, Tolga
    This paper introduces a solution for enhancing depth perception in a given 3D computer-generated scene. For this purpose, we propose a framework that decides on the suitable depth cues for a given scene and the rendering methods which provide these cues. First, the system calculates the importance of each depth cue using a fuzzy logic based algorithm which considers the target tasks in the application and the spatial layout of the scene. Then, a knapsack model is constructed to keep the balance between the rendering costs of the graphical methods that provide these cues and their contibution to depth perception. This cost-profit analysis step selects the proper rendering methods. In this work, we also present several objective and subjective experiments which show that our automated depth enhancement system is statistically (p < 0.05) better than the other method selection techniques that are tested. © 2010 ACM.