Browsing by Author "Yildiz, ZC"

Now showing 1 - 6 of 6
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    A Fully Object-space Approach for Full-reference Visual Quality Assessment of Static and Animated 3D Meshes
    Yildiz, ZC; Capin, T
    3D mesh models are exposed to several geometric operations such as simplification and compression. Several metrics for evaluating the perceived quality of 3D meshes have already been developed. However, most of these metrics do not handle animation and they measure the global quality. Therefore, a full-reference perceptual error metric is proposed to estimate the detectability of local artifacts on animated meshes. This is a bottom-up approach in which spatial and temporal sensitivity models of the human visual system are integrated. The proposed method directly operates in 3D model space and generates a 3D probability map that estimates the visibility of distortions on each vertex throughout the animation sequence. We have also tested the success of our metric on public datasets and compared the results to other metrics. These results reveal a promising correlation between our metric and human perception.
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    A machine learning framework for full-reference 3D shape quality assessment
    Yildiz, ZC; Oztireli, AC; Capin, T
    To decide whether the perceived quality of a mesh is influenced by a certain modification such as compression or simplification, 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.
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    A perceptual quality metric for dynamic triangle meshes
    Yildiz, ZC; Capin, T
    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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    Learning a crowd-powered perceptual distance metric for facial blendshapes
    Yildiz, ZC
    It is known that purely geometric distance metrics cannot reflect the human perception of facial expressions. A novel perceptually based distance metric designed for 3D facial blendshape models is proposed in this paper. To develop this metric, comparative evaluations of facial expressions were collected from a crowdsourcing experiment. Then, the weights of a distance metric, based on descriptive features of the models, were optimized to match the results with crowdsourced data, through a metric learning process. The method incorporates perceptual properties such as curvature and visual saliency. A formal analysis of the results proves the high correlation between the metric output and human perception. The effectiveness and success of the proposed metric were also compared to other distance alternatives. The proposed metric will enable intelligent processing of 3D facial blendshapes data in several ways. It will be possible to generate perceptually valid clustering and visualization of 3D facial blendshapes. It will help reduce storage and computational requirements by removing redundant expressions that are perceptually identical from the overall dataset. It can also be used to assist novice animators while creating plausible and expressive facial animations.
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    A portfolio construction framework usingLSTM-based stock markets forecasting
    Yildiz, ZC; Yildiz, SB
    A novel framework that injects future return predictions into portfolio constructionstrategies is proposed in this study. First, a long-short-term-memory (LSTM) model is trained to learn the monthly closing prices of the stocks. Then these predictions are used in the calculation of portfolio weights. Five different portfolio construction strategies are introduced including modifications to smart-beta strategies. The suggested methods are compared to a number of baseline methods, using the stocks of BIST30 Turkey index. Our strategies yield a very high mean annualized return (25%) which is almost 50% higher than the baseline approaches. The mean Sharpe ratio of our strategies is 0.57, whereas the compared methods' are 0.29 and -0.32. Comprehensive analysis of the results demonstrates that utilizing predicted returns in portfolio construction enables a significant improvement on the performance of the portfolios.
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    A Framework for Applying the Principles of Depth Perception to Information Visualization
    Yildiz, ZC; 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.