Light, Color, and Surface Reflectance. Shida Beigpour

Transcription

1 Light, Color, and Surface Reflectance Shida Beigpour

2 Overview Introduction Multi-illuminant Intrinsic Image Estimation Multi-illuminant Scene Datasets Multi-illuminant Color Constancy Conclusions 2

3 Introduction Modeling scene optics plays a crucial role in scene understanding. Multi-illuminant Interreflection Specularities Colored illuminant Colorcast Colored shadows 3

4 Introduction 1. Automatic White Balance (AWB) AWB Original Image Color corrected 4

5 Introduction 1. Automatic White Balance (AWB) 2. Changing the illuminant (Re-lighting) relight Original Image Relighted 5

6 Introduction 1. Automatic White Balance (AWB) 2. Changing the illuminant (Re-lighting) 3. Segmentation Original Image Segmentation 6

7 Introduction 1. Automatic White Balance (AWB) 2. Changing the illuminant (Re-lighting) 3. Segmentation 4. Object Recoloring (physically plausible) Original Image Recolored 7

8 Introduction 1. Automatic White Balance (AWB) 2. Changing the illuminant (Re-lighting) 3. Segmentation 4. Object Recoloring 5. Photo forensics 9

9 Introduction 1. Automatic White Balance (AWB) 2. Changing the illuminant (Re-lighting) 3. Segmentation 4. Object Recoloring 5. Photo forensics 6. Shadow removal 7. Original Image Shadow removed 10

10 Introduction Context: There are mainly two fields which investigate illumination and object modeling in computer vision: Intrinsic image estimation Color constancy 11

11 Introduction Context: Many real-world scenes present complex reflectance under multiple-illuminants. Most existing methods limit their domain by making unrealistic assumptions. Multiple illuminants Interreflections Colored shadows 12

12 Introduction Context: Many real-world scenes present complex reflectance under multiple-illuminants. Most existing methods limit their domain by making unrealistic assumptions. Many fields in computer vision that rely on illuminant and reflectance models can strongly benefit from more realistic models: Object classification methods which use color cues Segmentation algorithms 13

13 Overview Introduction Multi-illuminant Intrinsic Image Estimation Multi-illuminant Scene Datasets Multi-illuminant Color Constancy Conclusions 14

14 Surface Normal Single-illuminant Object Reflectance Model There are two main forms of reflectance: specular and diffuse. Object Image Object Surface Diffuse Reflection 15 Surface Particles

15 Single-illuminant Object Reflectance Model Lambertian Reflection Model Models the interaction of light and matte (diffuse) object surfaces Coordinate Surface albedo Color channel Pixel Value geometry Light Sensor Sensitivity Chromatic component Dichromatic Reflection Model (DRM) [Shafer, JCRA 1985] Frensel reflectance 1 Diffuse component Specular component 16

16 Surface Normal Multi-illuminant Object Reflectance Model Image formation under multiple lights: Object Image Diffuse Reflection Diffuse Reflection Object Surface Surface Particles 19

17 Multi-illuminant Object Reflectance Model * We extend the DRM to account for multiple illuminants: Multi-Illuminant Dichromatic Reflection (MIDR) model Assumption: Single-colored object pre-segmented from its background. Surface light DRM MIDR Geometry Chroma As error metric we define Reconstruction error, the mean square error between the estimated model and the object pixels. * Sh. Beigpour and J. van de Weijer, Object Recoloring based on Intrinsic Image Estimation, Proceedings of IEEE International Conference on Computer Vision (ICCV), November

18 Multi-illuminant Object Reflectance Model An example of modeling: Object is segmented from its background Object Image 23

19 Multi-illuminant Object Reflectance Model An example of algorithm performance: First the object is modeled just by the DRM. But in this case, the greenish inter-reflection is lost. Object Image Diffuse component Object Color Reconstruction DRM Specular component Illuminant Color 24

20 Multi-illuminant Object Reflectance Model An example of algorithm performance: The reconstruction error provides a mask for which the secondary illuminant is dominant. Object Image 2 Reconst. Error Secondary Illuminant Reconstruction Mask Threshold 25

21 Multi-illuminant Object Reflectance Model An example of algorithm performance: Using the illuminant mask, two models are generated. Illuminant Mask Body Reflectance Specular Reflectance Object Image Reflectance Reflectance 26

22 Multi-illuminant Object Reflectance Model An example of algorithm performance: The models and corresponding masks are iteratively refined until convergence. Illuminant Mask Body Reflectance Specular Reflectance Object Image Reflectance Reflectance 27

23 Multi-illuminant Object Reflectance Model An example of modeling: Decomposition 1 st Body Reflectance Object Image Object Color Specular Reflectance Primary Light 2 nd Body Reflectance Secondary Light 28

24 Multi-illuminant Object Reflectance Model Improving photo-fusion: Here the green mug is replaced by a purple mug. But now the greenish interreflection on the blue mug doesn t match the scene. Photo-fusion 29

25 Multi-illuminant Object Reflectance Model Improving photo-fusion: The purple color from the mug is assigned as the secondary illuminant. 1 st Body Reflectance Object Color Specular Reflectance Primary Light 2 nd Body Reflectance 30 Secondary Light

26 Multi-illuminant Object Reflectance Model Improving photo-fusion: The mug is then constructed using the new color. 1 st Body Reflectance Object Color Specular Reflectance Primary Light 2 nd Body Reflectance 31 Secondary Light

27 Multi-illuminant Object Reflectance Model Improving photo-fusion: The greenish inter-reflection on the blue mug is transformed to purple. Photo-fusion 32

28 Single-illuminant Object Reflectance Model Recoloring a video Original Video Recolored Video 33

29 Multi-illuminant Object Reflectance Model Next step: Multi-illuminant dataset with pixel-wise accurate ground-truth for the purpose of quantitative evaluation. Solving the model without the need of segmentation or assumptions on the illuminant. 35

30 Overview Introduction Multi-illuminant Intrinsic Image Estimation Multi-illuminant Scene Datasets Multi-illuminant Color Constancy Conclusions 36

31 Multi-illuminant Datasets Observations: Existing dataset for reflectance and illuminant estimation are mostly limited to single-illuminant scenarios. In addition intrinsic image datasets mainly consist of single-object scenes. We propose two multi-illuminant datasets: Synthetic intrinsic image dataset: Computer graphics generated dataset for intrinsic estimation benchmarking in complex scenes. Multi-Illuminant Multi-Object (MIMO) : Dataset for multi-illuminant color constancy benchmarking. 37

32 Synthetic intrinsic image dataset * Our proposal is to use computer graphics generated data for this purpose: 3D-Modeling : Blender Photo-realistic rendering: Yafaray Multi-spectral images: Using 6-band color images instead of 3-band RGB to improve accuracy We use recorded multi-spectral data for Munsell color patches for the surfaces along with the multi-spectral Planckian and non-planckian lights. * Sh. Beigpour, M. Serra, J. van de Weijer, R. Benavente, M. Vanrell, O. Penacchio, and D. Samaras, Intrinsic Image Evaluation On Synthetic Complex Scenes, Proceedings of IEEE International Conference on Image Processing (ICIP), September

33 Intrinsic Image datasets We propose the use of synthetic data for intrinsic image estimation: Global lighting Photo-realism Inter-reflections Direct lighting Photon-Mapping Colored Shadows 40

34 Intrinsic Image datasets We propose the use of synthetic data for intrinsic image estimation: Global lighting Photo-realism Accurate pixel-wise ground-truth Original Image Reflectance Illumination 41

35 Intrinsic Image datasets An example of benchmarking for three intrinsic image methods: Ground-truth reflectance Reflectance Estimation Results Gehler, NIPS 2011 Barron ECCV 2012 Serra, CVPR

36 Multi-illuminant Datasets Observations: Existing dataset for reflectance and illuminant estimation are mostly limited to single-illuminant scenarios. In addition intrinsic image datasets mainly consist of single-object scenes. We propose two multi-illuminant datasets: Synthetic intrinsic image dataset: Computer graphics generated dataset for intrinsic estimation benchmarking in complex scenes. Multi-Illuminant Multi-Object (MIMO) : Dataset for multi-illuminant color constancy benchmarking. 43

37 Color constancy datasets Examples of some of the most popular single-illuminant color constancy datasets. Ciurea & Funt Barnard et. al. Parraga et. al. Gehler et. al. 44

38 Multi-Iluminant Multi-Object scene dataset (MIMO) We propose our multi-illuminant dataset MIMO * MIMO-Lab: Toy images captured under controlled lab conditions. * Sh. Beigpour and C. Riess and J. van de Weijer, and E. Angelopoulou, Multi-illuminant estimation using Conditional Random Field, IEEE Transactions on Image Processing (TIP), vol.23, no.1, pp.83,96, January

39 Multi-Iluminant Multi-Object scene dataset (MIMO) We propose our multi-illuminant dataset MIMO MIMO-Lab: Toy images captured under controlled lab conditions. MIMO-Realworld: Indoor/outdoor images of some real-world scenes. 46

40 Multi-Iluminant Multi-Object scene dataset (MIMO) We propose our multi-illuminant dataset MIMO MIMO-Lab: Toy images captured under controlled lab conditions. MIMO-Realworld: Indoor/outdoor images of some real-world scenes. 47

41 Multi-Iluminant Multi-Object scene dataset (MIMO) Ground-truth calculation: We use the linearity of the images and illuminants : Illuminant ab Illuminant b Illuminant a DS Image Image of Illuminant a (f a ) Both Illuminants (f ab ) Image of Illuminant b (f b ) Macbeth color checker 49

42 Multi-Iluminant Multi-Object scene dataset (MIMO) Ground-truth calculation: The ratio of the each of the illuminants making up the multi-illuminant scene in the green channel : Using this ratio we can calculate the illuminant at each pixel of the multi-illuminant image (i ab ) by a linear interpolation: Illuminant b The per pixel r a,g ratio Illuminant Chroma Map (ground-truth) 50

43 Multi-Iluminant Multi-Object scene dataset (MIMO) Examples from the MIMO-lab dataset: Original Image Multi-illuminant ratio Illumination ground-truth 51

44 Multi-Iluminant Multi-Object scene DS: Expanding the framework Examples from our new Multi-illuminant dataset in Norway: 6 scenes, 5 illumination conditions : 30 images Blue, Orange, and ceiling lamp Scene with Macbeth Checkered * Imtiaz Masud Ziko, Shida Beigpour, and Jon Yngve Hardeberg, 'Design and Creation of a Multi-Illuminant Scene Image Dataset, International Conference on Image and Signal Processing (ICISP)

45 Multi-Iluminant Multi-Object scene DS: Expanding the framework For each multi-illuminant scene we require the images of each light separately: Blue Light Orange Light Ceiling Lamp 54

46 Multi-Iluminant Multi-Object scene DS: Expanding the framework In the color-coded ratio image, Red, Green and Blue stand for Orange (from left), Ceiling lamp (from top), and Blue (from right). Blue, Orange, and ceiling lamp Pixel-wise illumination Ratio Pixel-wise Groundtruth 59

47 Multi-Iluminant scene datasets Conclusion: We introduced a synthetic dataset for intrinsic image estimation for multiple illuminants and complex scenes. We created the first multi-illuminant complex scene dataset with pixelwise ground-truth accuracy. Next step: We use our multi-illuminant scene dataset for multi-illuminant color constancy. 61

48 Overview Introduction Multi-illuminant Intrinsic Image Estimation Multi-illuminant Scene Datasets Multi-illuminant Color Constancy Conclusions 62

49 Color constancy Can you guess the color of the woman s top? 63

50 Color constancy Can you guess the color of the woman s top? 64

51 Computation Color Constancy Color constancy algorithm mainly consist of two steps: Estimation of chromaticity of light source Statistical methods 1 n c m m ( f ) ( x) c n, m, dx k( e ) n x Minkowski norm using the Minkowski framework: Gray-world (e 0,1,0 ) : The average reflectance is the illuminant color. Max-RGB (e 0,,0 ) : The maximum reflectance is the illuminant color. Gray-edge (e 1,m,σ ) : The average reflectance differences is the illuminant color. Second-order gray-edge (e 2,m,σ ) 65

52 Computation Color Constancy Color constancy algorithm mainly consist of two steps: Estimation of chromaticity of light source Statistical methods Physics-based methods Using R. Tan et. al. 2004, Inverse-Intensity Chromaticity (IIC) space for color constancy: 1. Detection of possible specular regions 2. Illuminant estimation in the detected regions. Using the linear relation between chromaticity and inverse intensity in the image: Inverse intensity Image Chromaticity Diffuse chroma Specularity chroma 66

53 Computation Color Constancy Color constancy algorithm mainly consist of two steps: Estimation of chromaticity of light source Statistical methods Physics-based methods Gamut-mapping Learning methods Image color correction to canonical illumination von Kries model Diagonal-offset model 67

54 Multi-illuminant Color Constancy Motivation: Single-illuminant color constancy methods are not capable of modeling the complexity of illumination in the real-world scenarios. The assumption of uniform illumination made by the single-illuminant methods is unrealistic. Objective: The goal of Multi-illuminant Color Constancy is to provide accurate pixel-wise illuminant estimates. 68

55 Multi-illuminant Color Constancy Multi-Illuminant Random Field (MIRF) method * : Image is divided to patches using grid and illuminant is estimated locally. Unary potentials: Statistical estimation Physics-based estimation Pair-wise potentials Smoothness Factor Pair-wise Potential Unary Potential * Sh. Beigpour and C. Riess and J. van de Weijer, and E. Angelopoulou, Multi-illuminant estimation using Conditional Random Field, IEEE Transactions on Image Processing (TIP), vol.23, no.1, pp.83,96, January

56 Multi-illuminant Color Constancy Multi-Illuminant Random Field (MIRF) method * : Image is divided to patches using grid and illuminant is estimated locally. Unary potentials: Statistical estimation Physics-based estimation Pair-wise potentials Smoothness Factor Pair-wise Potential Unary Potential * Sh. Beigpour and C. Riess and J. van de Weijer, and E. Angelopoulou, Multi-illuminant estimation using Conditional Random Field, IEEE Transactions on Image Processing (TIP), vol.23, no.1, pp.83,96, January

57 Multi-illuminant Color Constancy Multi-Illuminant Random Field (MIRF) method * : Image is divided to patches using grid and illuminant is estimated locally. Unary potentials: Statistical estimation Physics-based estimation Pair-wise potentials Smoothness Factor Pair-wise Potential Unary Potential * Sh. Beigpour and C. Riess and J. van de Weijer, and E. Angelopoulou, Multi-illuminant estimation using Conditional Random Field, IEEE Transactions on Image Processing (TIP), vol.23, no.1, pp.83,96, January

58 Multi-illuminant Color Constancy Multi-Illuminant Random Field (MIRF) method: Image is divided to patches using grid and illuminant is estimated locally. Unary potentials: Error metric: The angular error is the angle between any two color vectors (i 1 and i 2 ) in RGB space. We use the angular error between the label and estimate to define the energy of a graph labeling. 72

59 Multi-illuminant Color Constancy Multi-Illuminant Random Field (MIRF) method: Image is divided to patches using grid and illuminant is estimated locally. Unary potentials: Statistical estimation f j = (f R j, f G j, f B j ) is the j-th pixel and P={p 1,p 2,,p N } are the patches then the local illuminant color for the i-th patch p i using Minkowski norm is: Therefore we can write the unary potential as: Statistical unary potential Local estimate Patch label Error function 73

60 Multi-illuminant Color Constancy Multi-Illuminant Random Field (MIRF) method: Image is divided to patches using grid and illuminant is estimated locally. Unary potentials: Physics-based estimation Therefore: Physics-based unary potential p i ( xi F p ) w ρσ ( ( i p, xi )) i p r i Local illuminant estimate where the binary weighting w is defined using sum of the intensity for possible specular pixels ( s sp ) and a threshold ( t sp ) Original Image Regions which w exists Lehmann and Palm JOSA

61 Multi-illuminant Color Constancy Multi-Illuminant Random Field (MIRF) method: Image is divided to patches using grid and illuminant is estimated locally. Unary potentials: Defining labels: The labels are centers of the clustered formed by the patch-wise local illuminant estimates in RGB space. To improve the performance highly saturated values are discarded : Label set L i (, ) White light l i i w d Each label K-means 1 i w (111,, ) 3 T Statistical estimates Label-set Physics-based estimates 78

62 Multi-illuminant Color Constancy Multi-Illuminant Random Field (MIRF) method: Image is divided to patches using grid and illuminant is estimated locally. Statistical estimates by Gray-word 79

63 Multi-illuminant Color Constancy Multi-Illuminant Random Field (MIRF) method: Image is divided to patches using grid and illuminant is estimated locally. Statistical estimates by Gray-word Results of the energy minimization 80

64 Multi-Illuminant Random Field (MIRF) Experiment results: We compare our method with the single-illuminant and the method of Gijsenij et. al. We use the median angular error over each set as our measure. MIMO-Lab (58 images): Method Single-light Gijsenij et.al. MIRF DN 10.5º N/A N/A GW 2.9º 5.9º 2.8º (-3%) WP 7.6º 4.2º 2.8º (-33%) GE-1 2.8º 4.2º 2.6º (-7%) GE-2 2.9º 5.7º 2.6º (-10%) IEBV 8.3º N/A 3.0º (-64%) 84

65 Multi-Illuminant Random Field (MIRF) Experiment results: We compare our method with the single-illuminant and the method of Gijsenij et. al. We use the median angular error over each set as our measure. Gijsenij-outdoor (9 images): Method Single-light Gijsenij et.al. MIRF DN 3.6 N/A N/A GW 13.8º 13.8º 10.1º (-27%) WP 11.3º 8.4º 6.4º (-24%) GE º 7.6º 4.7º (-38%) GE-2 8.5º 7.4º 5.0º (-32%) IEBV 7.3º N/A 7.3º 86

66 Multi-Illuminant Random Field (MIRF) Experiment results: Qualitative evaluation Original Ground-truth White-balance Single illuminant White-balance Gijsenij White-balance Our method s White-balance

67 Conclusions We proposed an intrinsic image estimation framework for single-colored objects in multi-illuminant scenarios (applied to object recoloring and color transfer). We created two multi-illuminant scene datasets: Synthetic intrinsic image dataset: Computer graphics generated dataset for intrinsic estimation benchmarking in complex scenes. Multi-Illuminant Multi-Object (MIMO) : Dataset for multi-illuminant color constancy benchmarking. We proposed a CRF-based color constancy method for multi-illuminant scenes. State-of-the-art results are obtained on 3 datasets. 92

68 93 Thanks for your attention!

69 Publications 1. Sh. Beigpour and C. Riess and J. van de Weijer, and E. Angelopoulou, Multi-illuminant estimation using Conditional Random Field, IEEE Transactions on Image Processing (TIP) Imtiaz Masud Ziko, Shida Beigpour, and Jon Yngve Hardeberg, Design and Creation of a Multi-Illuminant Scene Image Dataset, International Conference on Image and Signal Processing (ICISP) S. Beigpour, M. Serra, J. van de Weijer, R. Benavente, M. Vanrell, O. Penacchio, and D. Samaras, Intrinsic Image Evaluation On Synthetic Complex Scenes, International Conference on Image Processing (ICIP) Sh. Beigpour and J. van de Weijer, Object Recoloring based on Intrinsic Image Estimation, Proceedings of IEEE International Conference on Computer Vision (ICCV), November J. van de Weijer and Sh. Beigpour, The Dichromatic Reflection Model: Future Research Directions and Applications, invited paper at Int. Conf. on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), Portugal M. Bleier, C. Riess, Sh. Beigpour, E. Eibenberger, E. Angelopoulou, T. Troeger, and A. Kaup. "Color Constancy and Non-Uniform Illumination: Can Existing Algorithms Work?" ICCV Workshop on Color and Photometry in Computer Vision (ICCV Workshop). November Sh. Beigpour and J. van de Weijer, Photo-Realistic Color Alteration For Architecture And Design, Proceedings of Colour Research for European Advanced Technology Employment (CREATE) Conference, Jun (Chosen for oral presentation). 94