36-Issue 7

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/2631805

Browse

Browsing 36-Issue 7 by Subject "I.3.3 [Computer Graphics]"

Now showing 1 - 5 of 5
  • No Thumbnail Available
    Item
    Albero: A Visual Analytics Approach for Probabilistic Weather Forecasting
    (The Eurographics Association and John Wiley & Sons Ltd., 2016) Diehl, Alexandra; Pelorosso, Leandro; Delrieux, Claudio; Matkovic, Kresimir; Ruiz, Juan; Gröller, M. Eduard; Bruckner, Stefan; Jernej Barbic and Wen-Chieh Lin and Olga Sorkine-Hornung
    Probabilistic weather forecasts are amongst the most popular ways to quantify numerical forecast uncertainties. The analog regression method can quantify uncertainties and express them as probabilities. The method comprises the analysis of errors from a large database of past forecasts generated with a specific numerical model and observational data. Current visualization tools based on this method are essentially automated and provide limited analysis capabilities. In this paper, we propose a novel approach that breaks down the automatic process using the experience and knowledge of the users and creates a new interactive visual workflow. Our approach allows forecasters to study probabilistic forecasts, their inner analogs and observations, their associated spatial errors, and additional statistical information by means of coordinated and linked views. We designed the presented solution following a participatory methodology together with domain experts. Several meteorologists with different backgrounds validated the approach. Two case studies illustrate the capabilities of our solution. It successfully facilitates the analysis of uncertainty and systematic model biases for improved decision-making and process-quality measurements.
  • No Thumbnail Available
    Item
    Efficient Gradient-Domain Compositing Using an Approximate Curl-free Wavelet Projection
    (The Eurographics Association and John Wiley & Sons Ltd., 2016) Ren, Xiaohua; Luan, Lyu; He, Xiaowei; Zhang, Yanci; Wu, Enhua; Jernej Barbic and Wen-Chieh Lin and Olga Sorkine-Hornung
    Gradient-domain compositing has been widely used to create a seamless composite with gradient close to a composite gradient field generated from one or more registered images. The key to this problem is to solve a Poisson equation, whose unknown variables can reach the size of the composite if no region of interest is drawn explicitly, thus making both the time and memory cost expensive in processing multi-megapixel images. In this paper, we propose an approximate projection method based on biorthogonal Multiresolution Analyses (MRA) to solve the Poisson equation. Unlike previous Poisson equation solvers which try to converge to the accurate solution with iterative algorithms, we use biorthogonal compactly supported curl-free wavelets as the fundamental bases to approximately project the composite gradient field onto a curl-free vector space. Then, the composite can be efficiently recovered by applying a fast inverse wavelet transform. Considering an n-pixel composite, our method only requires 2n of memory for all vector fields and is more efficient than state-of-the-art methods while achieving almost identical results. Specifically, experiments show that our method gains a 5x speedup over the streaming multigrid in certain cases.
  • No Thumbnail Available
    Item
    High-resolution 360 Video Foveated Stitching for Real-time VR
    (The Eurographics Association and John Wiley & Sons Ltd., 2016) Lee, Wei-Tse; Chen, Hsin-I; Chen, Ming-Shiuan; Shen, I-Chao; Chen, Bing-Yu; Jernej Barbic and Wen-Chieh Lin and Olga Sorkine-Hornung
    In virtual reality (VR) applications, the contents are usually generated by creating a 360 video panorama of a real-world scene. Although many capture devices are being released, getting high-resolution panoramas and displaying a virtual world in realtime remains challenging due to its computationally demanding nature. In this paper, we propose a real-time 360 video foveated stitching framework, that renders the entire scene in different level of detail, aiming to create a high-resolution panoramic video in real-time that can be streamed directly to the client. Our foveated stitching algorithm takes videos from multiple cameras as input, combined with measurements of human visual attention (i.e. the acuity map and the saliency map), can greatly reduce the number of pixels to be processed. We further parallelize the algorithm using GPU to achieve a responsive interface and validate our results via a user study. Our system accelerates graphics computation by a factor of 6 on a Google Cardboard display.
  • No Thumbnail Available
    Item
    Photometric Stabilization for Fast-forward Videos
    (The Eurographics Association and John Wiley & Sons Ltd., 2016) Zhang, Xuaner; Lee, Joon-Young; Sunkavalli, Kalyan; Wang, Zhaowen; Jernej Barbic and Wen-Chieh Lin and Olga Sorkine-Hornung
    Videos captured by consumer cameras often exhibit temporal variations in color and tone that are caused by camera autoadjustments like white-balance and exposure. When such videos are sub-sampled to play fast-forward, as in the increasingly popular forms of timelapse and hyperlapse videos, these temporal variations are exacerbated and appear as visually disturbing high frequency flickering. Previous techniques to photometrically stabilize videos typically rely on computing dense correspondences between video frames, and use these correspondences to remove all color changes in the video sequences. However, this approach is limited in fast-forward videos that often have large content changes and also might exhibit changes in scene illumination that should be preserved. In this work, we propose a novel photometric stabilization algorithm for fast-forward videos that is robust to large content-variation across frames. We compute pairwise color and tone transformations between neighboring frames and smooth these pair-wise transformations while taking in account the possibility of scene/content variations. This allows us to eliminate high-frequency fluctuations, while still adapting to real variations in scene characteristics. We evaluate our technique on a new dataset consisting of controlled synthetic and real videos, and demonstrate that our techniques outperforms the state-of-the-art.
  • No Thumbnail Available
    Item
    A Probabilistic Framework for Component-based Vector Graphics
    (The Eurographics Association and John Wiley & Sons Ltd., 2016) Lieng, Henrik; Jernej Barbic and Wen-Chieh Lin and Olga Sorkine-Hornung
    We propose a framework for data-driven manipulation and synthesis of component-based vector graphics. Using labelled vector graphical images of a given type of object as input, our processing pipeline produces training data, learns a probabilistic Bayesian network from that training data, and offer various data-driven vector-related tools using synthesis functions. The tools ranges from data-driven vector design to automatic synthesis of vector graphics. Our tools were well received by designers, our model provides good generalisation performance, also from small data sets, and our method for synthesis produces vector graphics deemed significantly more plausible compared with alternative methods.