344 research outputs found

    Analyzing interfaces and workflows for light field editing

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    With the increasing number of available consumer light field cameras, such as Lytro, Raytrix, or Pelican Imaging, this new form of photography is progressively becoming more common. However, there are still very few tools for light field editing, and the interfaces to create those edits remain largely unexplored. Given the extended dimensionality of light field data, it is not clear what the most intuitive interfaces and optimal workflows are, in contrast with well-studied two-dimensional (2-D) image manipulation software. In this work, we provide a detailed description of subjects' performance and preferences for a number of simple editing tasks, which form the basis for more complex operations. We perform a detailed state sequence analysis and hidden Markov chain analysis based on the sequence of tools and interaction paradigms users employ while editing light fields. These insights can aid researchers and designers in creating new light field editing tools and interfaces, thus helping to close the gap between 4-D and 2-D image editing

    Methods for Real-time Visualization and Interaction with Landforms

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    This thesis presents methods to enrich data modeling and analysis in the geoscience domain with a particular focus on geomorphological applications. First, a short overview of the relevant characteristics of the used remote sensing data and basics of its processing and visualization are provided. Then, two new methods for the visualization of vector-based maps on digital elevation models (DEMs) are presented. The first method uses a texture-based approach that generates a texture from the input maps at runtime taking into account the current viewpoint. In contrast to that, the second method utilizes the stencil buffer to create a mask in image space that is then used to render the map on top of the DEM. A particular challenge in this context is posed by the view-dependent level-of-detail representation of the terrain geometry. After suitable visualization methods for vector-based maps have been investigated, two landform mapping tools for the interactive generation of such maps are presented. The user can carry out the mapping directly on the textured digital elevation model and thus benefit from the 3D visualization of the relief. Additionally, semi-automatic image segmentation techniques are applied in order to reduce the amount of user interaction required and thus make the mapping process more efficient and convenient. The challenge in the adaption of the methods lies in the transfer of the algorithms to the quadtree representation of the data and in the application of out-of-core and hierarchical methods to ensure interactive performance. Although high-resolution remote sensing data are often available today, their effective resolution at steep slopes is rather low due to the oblique acquisition angle. For this reason, remote sensing data are suitable to only a limited extent for visualization as well as landform mapping purposes. To provide an easy way to supply additional imagery, an algorithm for registering uncalibrated photos to a textured digital elevation model is presented. A particular challenge in registering the images is posed by large variations in the photos concerning resolution, lighting conditions, seasonal changes, etc. The registered photos can be used to increase the visual quality of the textured DEM, in particular at steep slopes. To this end, a method is presented that combines several georegistered photos to textures for the DEM. The difficulty in this compositing process is to create a consistent appearance and avoid visible seams between the photos. In addition to that, the photos also provide valuable means to improve landform mapping. To this end, an extension of the landform mapping methods is presented that allows the utilization of the registered photos during mapping. This way, a detailed and exact mapping becomes feasible even at steep slopes

    An Introduction to Variational Autoencoders

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    Variational autoencoders provide a principled framework for learning deep latent-variable models and corresponding inference models. In this work, we provide an introduction to variational autoencoders and some important extensions

    Natural Parameterization

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    The objective of this project has been to develop an approach for imitating physical objects with an underlying stochastic variation. The key assumption is that a set of “natural parameters” can be extracted by a new subdivision algorithm so they reflect what is called the object’s “geometric DNA”. A case study on one hundred wheat grain crosssections (Triticum aestivum) showed that it was possible to extract thirty-six such parameters and to reuse them for Monte Carlo simulation of “new” stochastic phantoms which possessthe same stochastic behavior as the “original” cross-sections

    The Video Mesh: A Data Structure for Image-based Video Editing

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    This paper introduces the video mesh, a data structure for representing video as 2.5D "paper cutouts." The video mesh allows interactive editing of moving objects and modeling of depth, which enables 3D effects and post-exposure camera control. The video mesh sparsely encodes optical flow as well as depth, and handles occlusion using local layering and alpha mattes. Motion is described by a sparse set of points tracked over time. Each point also stores a depth value. The video mesh is a triangulation over this point set and per-pixel information is obtained by interpolation. The user rotoscopes occluding contours and we introduce an algorithm to cut the video mesh along them. Object boundaries are refined with perpixel alpha values. The video mesh is at its core a set of texture mapped triangles, we leverage graphics hardware to enable interactive editing and rendering of a variety of effects. We demonstrate the effectiveness of our representation with a number of special effects including 3D viewpoint changes, object insertion, and depth-of-field manipulation

    Drug-tubulin interactions interrogated by transient absorption spectroscopy

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    [EN] Colchicine (COL) is a bioactive molecule with antitumor properties. When COL binds to tubulin (TU), it inhibits microtubule assembly dynamics. We have investigated COL-TU interactions using laser flash photolysis (LFP) technique and performing fully flexible molecular dynamics simulations. Excitation of COL at 355 nm in aqueous medium did not lead to any transient absorption spectrum. By contrast, in the presence of TU a transient peaking at lambda(max) ca. 420 nm was registered and assigned as triplet excited COL complexed with TU ((COL)-C-3*@TU). In aerated medium, the lifetime was tau ca. 160 mu s and the quantum yield was 0.138. Likewise, when the bicyclic COL analog MTC was submitted to LFP in the presence of TU, (MTC)-M-3@TU* was detected with a lifetime of ca. 62 ms and a quantum yield of 0.296, Aqueous solutions of MTC did not produce any signal in the microsecond timescale. The triplet energy of MTC was obtained by means of emission measurements and found to be ca. 200 kJ mol(-1), a value that matches with that previously reported for COL (188 kJ mol(-1)). Molecular dynamic simulations, both with the ground and triplet excited state, reveal a strong interaction between COL and TU to give stabilized complexes with restricted mobility inside the protein binding site. These results demonstrate that LFP is a useful methodology to study the binding of COL derivatives to TU and open a new way to evaluate the interactions of non-fluorescent anticancer drugs with this protein.Financial support from the Spanish Government (grants CTQ2010-19909; BFU2011-23416 and SEV 2012-0267), the Generalitat Valenciana (Prometeo II/2013/005) and Comunidad de Madrid (S2010/BMD-2353) is gratefully acknowledged. G.S. thanks ASIC-UPV for computing time.Bosca Mayans, F.; Sastre Navarro, GI.; Andreu, JM.; Jornet, D.; Tormos Faus, RE.; Miranda Alonso, MÁ. (2015). 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    Multimodal Controller for Generative Models

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    Class-conditional generative models are crucial tools for data generation from user-specified class labels. Existing approaches for class-conditional generative models require nontrivial modifications of backbone generative architectures to model conditional information fed into the model. This paper introduces a plug-and-play module named `multimodal controller' to generate multimodal data without introducing additional learning parameters. In the absence of the controllers, our model reduces to non-conditional generative models. We test the efficacy of multimodal controllers on CIFAR10, COIL100, and Omniglot benchmark datasets. We demonstrate that multimodal controlled generative models (including VAE, PixelCNN, Glow, and GAN) can generate class-conditional images of significantly better quality when compared with conditional generative models. Moreover, we show that multimodal controlled models can also create novel modalities of images
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