Deriving intrinsic images from image sequences

By Yair Weiss. 2001

705 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Learning to Reconstruct Shapes from Unseen Classes (2018, background): “Through years, researchers have explored recovering 2.5D sketches from texture, shading, or color images [Barron and Malik, 2015, Bell et al., 2014, Horn and Brooks, 1989, Tappen et al., 2003, Weiss, 2001, Zhang et al., 1999].”
Learning Shape Priors for Single-View 3D Completion and Reconstruction (2018, background): “In the past, researchers have explored recovering 2.5D sketches from shading, texture, or color images [2,3,20,47,55,63].”
MarrNet: 3D Shape Reconstruction via 2.5D Sketches (2017, background): “In the past, researchers have explored recovering 2.5D shape from shading, texture, or color images [Horn and Brooks, 1989, Zhang et al., 1999, Tappen et al., 2003, Barron and Malik, 2015, Weiss, 2001, Bell et al., 2014].”
Learning Non-Lambertian Object Intrinsics Across ShapeNet Categories (2016, background): “Another line of approaches seek additional information from the input, such as image sequences [33], depth [2, 10] and user strokes [7].”
Intrinsic Decompositions for Image Editing (2017, neutral): “…consider multiple images as input (from multiple viewpoints [CBLD11, LBP ∗ 12, Laf12, LBD13, HWU ∗ 14,Duc15,XLL ∗ 16], varying illumination [Wei01,MLKS04, Yu16], or different focal distances [SSN16]), images with depth information [BM13, CK13, JCTL14, HGW15], multi-spectral images [SW09],…”
Fourier Analysis of the 2D Screened Poisson Equation for Gradient Domain Problems (2008, methodology): “We also note that Weiss [24] provides a method for computing a discrete spatial filter that minimizes squared differences between filtered versions of an image and corresponding inputs.”
On the removal of shadows from images (2006, neutral): “More recently, Weiss [5] proposed a method”
What makes a good model of natural images? (2007, neutral): “[25]).”
Entropy Minimization for Shadow Removal (2009, methodology): “(Weiss, 2001)) but the proposed insensitivity to illumination depends on a slo wly changing penumbra and does not work for strong shadows.”
Ground truth dataset and baseline evaluations for intrinsic image algorithms (2009, methodology): “Some algorithms for estimating intrinsic images, such as [16], require a sequence of photographs of the same scene with many illumination conditions.”
Intrinsic colorization (2008, methodology): “We first describe the basic approach for intrinsic image decomposition in [Weiss 2001].”
User Assisted Separation of Reflections from a Single Image Using a Sparsity Prior (2004, methodology): “Given the histograms over derivative filters, we follow [22] in using it to define a distribution over images by assuming that derivative filters are independent o ver space and orientation so that our prior over images is given by: Pr(I) ≈ ∏”
Contextual Priming for Object Detection (2003, methodology): “The use of such regularities in the statistics finds applications in models of neural coding (Field, 1987), lightness and reflectance perception (Weiss, 2001; Dror, 2001), and camera distortion removal (Farid, 2001 ) among many other applications.”
Rendering synthetic objects into legacy photographs (2011, background): “…of shading do occur at shadow boundaries or normal discontinuities, but cues such as chromaticity [Funt et al. 1992] or differently lit images [Weiss 2001] can control these difficulties, as can methods that classify edges into albedo or shading [Tappen et al. 2005; Farenzena and Fusiello…”