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    Visual Analysis of Popping in Progressive Visualization
    (The Eurographics Association, 2021) Waterink, Ethan; Kosinka, Jiri; Frey, Steffen; Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
    Progressive visualization allows users to examine intermediate results while they are further refined in the background. This makes them increasingly popular when dealing with large data and computationally expensive tasks. The characteristics of how preliminary visualizations evolve over time are crucial for efficient analysis; in particular unexpected disruptive changes between iterations can significantly hamper the user experience. This paper proposes a visualization framework to analyze the refinement behavior of progressive visualization. We particularly focus on sudden significant changes between the iterations, which we denote as popping artifacts, in reference to undesirable visual effects in the context of level of detail representations in computer graphics. Our visualization approach conveys where in image space and when during the refinement popping artifacts occur. It allows to compare across different runs of stochastic processes, and supports parameter studies for gaining further insights and tuning the algorithms under consideration. We demonstrate the application of our framework and its effectiveness via two diverse use cases with underlying stochastic processes: adaptive image space sampling, and the generation of grid layouts.
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    SlowDeepFood: a Food Computing Framework for Regional Gastronomy
    (The Eurographics Association, 2021) Gilal, Nauman Ullah; Al-Thelaya, Khaled; Schneider, Jens; She, James; Agus, Marco; Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
    Food computing recently emerged as a stand-alone research field, in which artificial intelligence, deep learning, and data science methodologies are applied to the various stages of food production pipelines. Food computing may help end-users in maintaining healthy and nutritious diets by alerting of high caloric dishes and/or dishes containing allergens. A backbone for such applications, and a major challenge, is the automated recognition of food by means of computer vision. It is therefore no surprise that researchers have compiled various food data sets and paired them with well-performing deep learning architecture to perform said automatic classification. However, local cuisines are tied to specific geographic origins and are woefully underrepresented in most existing data sets. This leads to a clear gap when it comes to food computing on regional and traditional dishes. While one might argue that standardized data sets of world cuisine cover the majority of applications, such a stance would neglect systematic biases in data collection. It would also be at odds with recent initiatives such as SlowFood, seeking to support local food traditions and to preserve local contributions to the global variation of food items. To help preserve such local influences, we thus present a full end-to-end food computing network that is able to: (i) create custom image data sets semi-automatically that represent traditional dishes; (ii) train custom classification models based on the EfficientNet family using transfer learning; (iii) deploy the resulting models in mobile applications for real-time inference of food images acquired through smart phone cameras. We not only assess the performance of the proposed deep learning architecture on standard food data sets (e.g., our model achieves 91:91% accuracy on ETH’'s Food-101), but also demonstrate the performance of our models on our own, custom data sets comprising local cuisine, such as the Pizza-Styles data set and GCC-30. The former comprises 14 categories of pizza styles, whereas the latter contains 30 Middle Eastern dishes from the Gulf Cooperation Council members.
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    ProtoSketchAR: Prototyping in Augmented Reality via Sketchings
    (The Eurographics Association, 2021) Arriu, Simone; Cherchi, Gianmarco; Spano, Lucio Davide; Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
    Prototyping is a widely used technique in the early stages of system design, and it is an essential part of a new product development process. During this phase, designers identify the main functionalities, concepts and contents of the system without creating a fully functional system. This paper aims to discuss the development of ProtoSketchAR, a tool enabling Augmented Reality (AR) prototyping by sketching. The application has different interaction modes, depending on the performed functionality. Basically, it is possible to create 2D/3D sketches to be placed in the real environment and to manipulate them. These functionalities allow the creation of virtual elements that can be used to prototype screens of AR applications. The application is web-based so that it can be run on any device with a compatible AR browser, regardless of the operating system used.
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    Remote Volume Rendering with a Decoupled, Ray-Traced Display Phase
    (The Eurographics Association, 2021) Zellmann, Stefan; Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
    We propose an image warping-based remote rendering technique for volumes that decouples the rendering and display phases. For that we build on prior work where we sample the volume on the client using ray casting and reconstruct z-values based on heuristics. Color and depth buffers are then sent to the client, which reuses this depth image as a stand-in for subsequent frames by warping it to reflect the current camera position and orientation until new data was received from the server. The extension we propose in this work represents the depth pixels as spheres and ray traces them on the client side. In contrast to the reference method, this representation adapts the footprint of the depth pixels to the distance to the camera origin, which is more effective at hiding warping artifacts, particularly when applied to volumetric data sets.