Download presentation
Presentation is loading. Please wait.
Published byBarnard Bishop Modified over 9 years ago
1
Physics-based Illuminant Color Estimation as an Image Semantics Clue Christian Riess Elli Angelopoulou Pattern Recognition Lab (Computer Science 5) University of Erlangen-Nuremberg November 8, 2009
2
Page 2 Christian Riess Nov. 8, 2009Physics-based Illuminant Color Estimation as an Image Semantics Clue Illumination as an Image Semantics Clue Semantic analysis: Cues on objects, location, time Can we add illumination color as a clue?
3
Page 3 Christian Riess Nov. 8, 2009Physics-based Illuminant Color Estimation as an Image Semantics Clue Existing Methods in Illuminant Estimation Machine Learning-based, e.g. Gamut Mapping (e.g. Forsyth) Recent Gray Edge Variants (e.g. Gijsenij, Gevers) Color by Correlation (e.g. Finlayson) Physics-based, e.g. Dichromatic Reflectance-based (e.g. Klinker et al.) Intersection of diffuse color planes (e.g. Finlayson) Inverse-Intensity Chromaticity (Tan et al.) Require proper training Require “clean” input [1] Tan, Nishino, Ikeuchi. “Color Constancy through Inverse-Intensity Chromaticity Space,” Journal of the Optical Society of America A, 21(3): 321-334, 2004. [1]
4
Page 4 Christian Riess Nov. 8, 2009Physics-based Illuminant Color Estimation as an Image Semantics Clue Goal Show that physics-based methods can be used On arbitrary images, Maybe sacrificing the exact estimate, But giving higher level information (semantics).
5
Page 5 Christian Riess Nov. 8, 2009Physics-based Illuminant Color Estimation as an Image Semantics Clue The Inverse Intensity-Chromaticity Approach Neutral Interface Assumption (NIA): specular color = illuminant Image formation Let Chromaticity Diffuse chromaticity Specular chromaticity Rewritten w/ chromaticities Diffuse geometry Diffuse reflectance Specular geometry Specular reflectance
6
Page 6 Christian Riess Nov. 8, 2009Physics-based Illuminant Color Estimation as an Image Semantics Clue The Inverse Intensity-Chromaticity Approach Tan et al. showed, where NIA: specular color = light color =. The illuminant chromaticity relates linearly to the sum of intensities and the pixel chromaticities. Specular regions form a triangle in inverse-intensity space, pointing to the illuminant color.
7
Page 7 Christian Riess Nov. 8, 2009Physics-based Illuminant Color Estimation as an Image Semantics Clue Towards Automated Scene Assessment Tan et al. propose to estimate the illuminant chromaticity using the Hough transform along the y-axis. Hypothesis: The histogram shape gives clues on the illuminant estimate quality, and the illumination environment in general. Hough Space along y-axis Hough Space, as a histogram in its own right
8
Page 8 Christian Riess Nov. 8, 2009Physics-based Illuminant Color Estimation as an Image Semantics Clue Influence of the Specularity Segmentation Experiments with different specularity segmentations R, G, B estimates surprisingly stable Noise component shows as a broader peak.
9
Page 9 Christian Riess Nov. 8, 2009Physics-based Illuminant Color Estimation as an Image Semantics Clue Experiments with similar scenes Illuminant color estimates not sufficiently reliable: Left image illuminant color estimate (0.378, 0.303, 0.318), Right image illuminant color estimate (0.339, 0.341, 0.319). Influence of the Specularity Segmentation
10
Page 10 Christian Riess Nov. 8, 2009Physics-based Illuminant Color Estimation as an Image Semantics Clue Towards automated Self-assessment Is it possible to detect unmet constraints (e.g. poor specularity segmentation)? Shape of the histogram peak Fit Gaussian to peak Fit triangle to peak Optimization criteria Intersection area Sum of squared differences
11
Page 11 Christian Riess Nov. 8, 2009Physics-based Illuminant Color Estimation as an Image Semantics Clue Multiple illuminants mix in the histogram Observed peak might be a mixture of noise components from multiple light sources. Decomposition into ROIs shows the mixing property. Further Challenges on the Histogram Shape
12
Page 12 Christian Riess Nov. 8, 2009Physics-based Illuminant Color Estimation as an Image Semantics Clue Barely specularities, high amount of noise Indoor illumination, reddish interreflections Red/blue channels: Peak can be “hidden” Further Challenges on the Histogram Shape
13
Page 13 Christian Riess Nov. 8, 2009Physics-based Illuminant Color Estimation as an Image Semantics Clue Summary This is preliminary work. Shape of the histogram can be used as a semantic source of information. Method is insensitive to variations in specularity segmentation. Further analysis of histogram shape: Impact of binning Noise vs. multiple illuminants Learn histogram shapes? Iterative simultaneous segmentation and illuminant estimation
14
Page 14 Christian Riess Nov. 8, 2009Physics-based Illuminant Color Estimation as an Image Semantics Clue Image Sources http://www.flickr.com http://www.walks.ru Barnard et al.: A Comparison of Computational Color-Constancy Algorithms, IEEE TIP 11(9), 985- 996, 2002. Thank you for your attention!
Similar presentations
© 2025 SlidePlayer.com. Inc.
All rights reserved.