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Your search keyword '"Acquisition, representation and transformations for image synthesis (ARTIS)"' showing total 4 results
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4 results on '"Acquisition, representation and transformations for image synthesis (ARTIS)"'

1. Edge-preserving multiscale image decomposition based on local extrema

Author

Kartic Subr, Frédo Durand, Cyril Soler, Acquisition, representation and transformations for image synthesis (ARTIS), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Massachusetts Institute of Technology (MIT), ANR-07-BLAN-0331,HFIBMR,High-Fidelity Image-Based Modeling and Rendering(2007), Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, and Durand, Fredo

Subjects
Computer science, Property (programming), ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.3: Enhancement/I.4.3.0: Filtering, 02 engineering and technology, computer.software_genre, Image (mathematics), Computational photography, Computer graphics (images), 0202 electrical engineering, electronic engineering, information engineering, Computer vision, Mathematics, image decomposition, Multimedia, business.industry, ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS/I.3.5: Computational Geometry and Object Modeling/I.3.5.7: Physically based modeling, 020207 software engineering, Contrast (music), Computer Graphics and Computer-Aided Design, Computational photography (artistic), Maxima and minima, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Key (cryptography), computational photography, 020201 artificial intelligence & image processing, Artificial intelligence, Enhanced Data Rates for GSM Evolution, business, Maxima, computer, Algorithm
Abstract

We propose a new model for detail that inherently captures oscillations , a key property that distinguishes textures from individual edges. Inspired by techniques in empirical data analysis and morphological image analysis, we use the local extrema of the input image to extract information about oscillations: We define detail as oscillations between local minima and maxima. Building on the key observation that the spatial scale of oscillations are characterized by the density of local extrema, we develop an algorithm for decomposing images into multiple scales of superposed oscillations. Current edge-preserving image decompositions assume image detail to be low contrast variation. Consequently they apply filters that extract features with increasing contrast as successive layers of detail. As a result, they are unable to distinguish between high-contrast, fine-scale features and edges of similar contrast that are to be preserved. We compare our results with existing edge-preserving image decomposition algorithms and demonstrate exciting applications that are made possible by our new notion of detail.

Published
2009

2. Frequency analysis and sheared reconstruction for rendering motion blur

Author

Frédo Durand, Kevin Egan, Yu-Ting Tseng, Nicolas Holzschuch, Ravi Ramamoorthi, Department of Computer Science [New York], Columbia University [New York], Acquisition, representation and transformations for image synthesis (ARTIS), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Massachusetts Institute of Technology (MIT), Computer Science and Artificial Intelligence Laboratory [Cambridge] (CSAIL), Department of Electrical Engineering and Computer Science [Berkeley] (EECS), University of California [Berkeley] (UC Berkeley), University of California (UC)-University of California (UC), ANR-07-BLAN-0331,HFIBMR,High-Fidelity Image-Based Modeling and Rendering(2007), University of California [Berkeley], University of California-University of California, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, and Durand, Fredo

Subjects
Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, Reconstruction filter, Anti-aliasing, law.invention, Rendering (computer graphics), ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS/I.3.7: Three-Dimensional Graphics and Realism, law, 0202 electrical engineering, electronic engineering, information engineering, 0601 history and archaeology, Computer vision, 14. Life underwater, Specular reflection, ComputingMethodologies_COMPUTERGRAPHICS, Frequency analysis, 060102 archaeology, Pixel, business.industry, Motion blur, 020207 software engineering, 06 humanities and the arts, Computer Graphics and Computer-Aided Design, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], Computer Science::Graphics, Frequency domain, 020201 artificial intelligence & image processing, Artificial intelligence, business
Abstract

Motion blur is crucial for high-quality rendering, but is also very expensive. Our first contribution is a frequency analysis of motion-blurred scenes, including moving objects, specular reflections, and shadows. We show that motion induces a shear in the frequency domain, and that the spectrum of moving scenes can be approximated by a wedge. This allows us to compute adaptive space-time sampling rates, to accelerate rendering. For uniform velocities and standard axis-aligned reconstruction, we show that the product of spatial and temporal bandlimits or sampling rates is constant, independent of velocity. Our second contribution is a novel sheared reconstruction filter that is aligned to the first-order direction of motion and enables even lower sampling rates. We present a rendering algorithm that computes a sheared reconstruction filter per pixel, without any intermediate Fourier representation. This often permits synthesis of motion-blurred images with far fewer rendering samples than standard techniques require., United States. Office of Naval Research. Young Investigator Program (N00014-07-1-0900), United States. Office of Naval Research. (Grant number N00014-09-1-0741), National Science Foundation (U.S.). Graduate Research Fellowship Program, National Science Foundation (U.S.). (Grant number 0446916), National Science Foundation (U.S.). (Grant number 044756), National Science Foundation (U.S.). (Grant number 0701775), Alfred P. Sloan Foundation Fellowship, Microsoft Corporation. New Faculty Fellowship

Published
2009

3. Accurate detection of symmetries in 3D shapes

Author

François X. Sillion, Aurélien Martinet, Nicolas Holzschuch, Cyril Soler, Acquisition, representation and transformations for image synthesis (ARTIS), Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), ANR Masses de Données 'SHOW', and Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Inria Grenoble - Rhône-Alpes

Subjects
Work (thermodynamics), Computation, Spherical harmonics, 020207 software engineering, 02 engineering and technology, Topology, 3d shapes, Computer Graphics and Computer-Aided Design, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], Maxima and minima, Range (mathematics), Compression (functional analysis), Homogeneous space, 0202 electrical engineering, electronic engineering, information engineering, ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS/I.3.5: Computational Geometry and Object Modeling/I.3.5.2: Curve, surface, solid, and object representations, 020201 artificial intelligence & image processing, Algorithm, ComputingMethodologies_COMPUTERGRAPHICS, Mathematics
Abstract

We propose an automatic method for finding symmetries of 3D shapes, that is, isometric transforms which leave a shape globally unchanged. These symmetries are deterministically found through the use of an intermediate quantity: the generalized moments. By examining the extrema and spherical harmonic coefficients of these moments, we recover the parameters of the symmetries of the shape. The computation for large composite models is made efficient by using this information in an incremental algorithm capable of recovering the symmetries of a whole shape using the symmetries of its subparts. Applications of this work range from coherent remeshing of geometry with respect to the symmetries of a shape to geometric compression, intelligent mesh editing, and automatic instantiation.

Published
2006

4. Fourier depth of field

Author

Frédo Durand, François X. Sillion, Nicolas Holzschuch, Kartic Subr, Cyril Soler, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Durand, Fredo, Acquisition, representation and transformations for image synthesis (ARTIS), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Massachusetts Institute of Technology (MIT), Computer Science and Artificial Intelligence Laboratory [Cambridge] (CSAIL), ANR-07-BLAN-0331,HFIBMR,High-Fidelity Image-Based Modeling and Rendering(2007), and Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)

Subjects
sampling, Pixel, Aperture, business.industry, Sampling (statistics), 020207 software engineering, 02 engineering and technology, Computer Graphics and Computer-Aided Design, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS/I.3.7: Three-Dimensional Graphics and Realism, 0202 electrical engineering, electronic engineering, information engineering, Radiance, Depth of Field, 020201 artificial intelligence & image processing, Monte Carlo integration, Computer vision, Depth of field, Artificial intelligence, business, Focus (optics), Light field, Mathematics, Fourier analysis of light transport
Abstract

International audience; Optical systems used in photography and cinema produce depth of field effects, that is, variations of focus with depth. These effects are simulated in image synthesis by integrating incoming radiance at each pixel over the lense aperture. Unfortunately, aperture integration is extremely costly for defocused areas where the incoming radiance has high variance, since many samples are then required for a noise-free Monte Carlo integration. On the other hand, using many aperture samples is wasteful in focused areas where the integrand varies little. Similarly, image sampling in defocused areas should be adapted to the very smooth appearance variations due to blurring. This paper introduces an analysis of focusing and depth of field in the frequency domain, allowing a practical characterization of a light field's frequency content both for image and aperture sampling. Based on this analysis we propose an adaptive depth of field rendering algorithm which optimizes sampling in two important ways. First, image sampling is based on conservative bandwidth prediction and a splatting reconstruction technique ensures correct image reconstruction. Second, at each pixel the variance in the radiance over the aperture is estimated, and used to govern sampling. This technique is easily integrated in any sampling-based renderer, and vastly improves performance.

Published
2009

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