tomihisa (tom) welsh michael ashikhmin klaus mueller tomihisa (tom) welsh michael ashikhmin klaus...
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![Page 1: Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Center for Visual Computing Stony Brook University](https://reader038.vdocuments.net/reader038/viewer/2022110100/56649e0c5503460f94af4c5a/html5/thumbnails/1.jpg)
Tomihisa (Tom) WelshTomihisa (Tom) Welsh
Michael AshikhminMichael Ashikhmin
Klaus MuellerKlaus Mueller
Tomihisa (Tom) WelshTomihisa (Tom) Welsh
Michael AshikhminMichael Ashikhmin
Klaus MuellerKlaus Mueller
Center for Visual ComputingCenter for Visual Computing
Stony Brook UniversityStony Brook University
Transferring Color to Greyscale ImagesTransferring Color to Greyscale Images
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The ProblemThe Problem
• How to colorize greyscale images?How to colorize greyscale images?• How to colorize greyscale images?How to colorize greyscale images?
![Page 3: Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Center for Visual Computing Stony Brook University](https://reader038.vdocuments.net/reader038/viewer/2022110100/56649e0c5503460f94af4c5a/html5/thumbnails/3.jpg)
The ProblemThe Problem
• How to colorize greyscale images?How to colorize greyscale images?• How to colorize greyscale images?How to colorize greyscale images?
![Page 4: Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Center for Visual Computing Stony Brook University](https://reader038.vdocuments.net/reader038/viewer/2022110100/56649e0c5503460f94af4c5a/html5/thumbnails/4.jpg)
The ProblemThe Problem
• How to colorize greyscale images?How to colorize greyscale images?• How to colorize greyscale images?How to colorize greyscale images?
•Is his tie blue or green?Is his tie blue or green?
![Page 5: Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Center for Visual Computing Stony Brook University](https://reader038.vdocuments.net/reader038/viewer/2022110100/56649e0c5503460f94af4c5a/html5/thumbnails/5.jpg)
The ProblemThe Problem
• How to colorize greyscale images?How to colorize greyscale images?• How to colorize greyscale images?How to colorize greyscale images?
• Red!!Red!!
![Page 6: Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Center for Visual Computing Stony Brook University](https://reader038.vdocuments.net/reader038/viewer/2022110100/56649e0c5503460f94af4c5a/html5/thumbnails/6.jpg)
The ProblemThe Problem
• How to colorize greyscale images?How to colorize greyscale images?
• Issues:Issues:• No “correct” solution
• Need to be creative
• How to minimize the manual labor involved?
• How to colorize greyscale images?How to colorize greyscale images?
• Issues:Issues:• No “correct” solution
• Need to be creative
• How to minimize the manual labor involved?
![Page 7: Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Center for Visual Computing Stony Brook University](https://reader038.vdocuments.net/reader038/viewer/2022110100/56649e0c5503460f94af4c5a/html5/thumbnails/7.jpg)
MotivationMotivation
• Enhance Scientific Data Enhance Scientific Data • Medical Imaging (MRI, CT, X-Ray)
• Enhance Scientific Data Enhance Scientific Data • Medical Imaging (MRI, CT, X-Ray)
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MotivationMotivation
• Enhance Scientific Data Enhance Scientific Data • Medical Imaging (MRI, CT, X-Ray)
• Satellite Images (Landsat)
• Enhance Scientific Data Enhance Scientific Data • Medical Imaging (MRI, CT, X-Ray)
• Satellite Images (Landsat)
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MotivationMotivation
• Enhance Scientific DataEnhance Scientific Data • Medical Imaging (MRI, CT, X-Ray)
• Satellite Images (Landsat)
• Scanning Electron Microscopy (SEM)
• Enhance Scientific DataEnhance Scientific Data • Medical Imaging (MRI, CT, X-Ray)
• Satellite Images (Landsat)
• Scanning Electron Microscopy (SEM)
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MotivationMotivation
• Enhance Scientific Data Enhance Scientific Data • Medical Imaging (MRI, CT, X-Ray)
• Satellite Images (Landsat)
• Scanning Electron Microscopy (SEM)
• Colorize black/white photographs and moviesColorize black/white photographs and movies
• Enhance Scientific Data Enhance Scientific Data • Medical Imaging (MRI, CT, X-Ray)
• Satellite Images (Landsat)
• Scanning Electron Microscopy (SEM)
• Colorize black/white photographs and moviesColorize black/white photographs and movies
![Page 11: Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Center for Visual Computing Stony Brook University](https://reader038.vdocuments.net/reader038/viewer/2022110100/56649e0c5503460f94af4c5a/html5/thumbnails/11.jpg)
MotivationMotivation
• Enhance Scientific Data Enhance Scientific Data • Medical Imaging (MRI, CT, X-Ray)
• Satellite Images (Landsat)
• Scanning Electron Microscopy (SEM)
• Colorize black/white photographs and moviesColorize black/white photographs and movies
• Artistic EffectsArtistic Effects
• Enhance Scientific Data Enhance Scientific Data • Medical Imaging (MRI, CT, X-Ray)
• Satellite Images (Landsat)
• Scanning Electron Microscopy (SEM)
• Colorize black/white photographs and moviesColorize black/white photographs and movies
• Artistic EffectsArtistic Effects
![Page 12: Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Center for Visual Computing Stony Brook University](https://reader038.vdocuments.net/reader038/viewer/2022110100/56649e0c5503460f94af4c5a/html5/thumbnails/12.jpg)
The TaskThe Task
• The problem is fundamentally ill-posedThe problem is fundamentally ill-posed
• It is an attempt to extrapolate from 1-D to 3-D It is an attempt to extrapolate from 1-D to 3-D • Map scalar luminance (intensity) to vector RGB
• The problem is fundamentally ill-posedThe problem is fundamentally ill-posed
• It is an attempt to extrapolate from 1-D to 3-D It is an attempt to extrapolate from 1-D to 3-D • Map scalar luminance (intensity) to vector RGB
![Page 13: Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Center for Visual Computing Stony Brook University](https://reader038.vdocuments.net/reader038/viewer/2022110100/56649e0c5503460f94af4c5a/html5/thumbnails/13.jpg)
Previous MethodsPrevious Methods
• Coloring Book MethodColoring Book Method• Photoshop: manually paint color with low opacity
• Coloring Book MethodColoring Book Method• Photoshop: manually paint color with low opacity
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Previous MethodsPrevious Methods
• Coloring Book MethodColoring Book Method• Photoshop: manually paint color with low opacity
• Movie Industry: track polygons [Cinesite Press Article]
• Coloring Book MethodColoring Book Method• Photoshop: manually paint color with low opacity
• Movie Industry: track polygons [Cinesite Press Article]
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Previous MethodsPrevious Methods
• Coloring Book MethodColoring Book Method• Photoshop: manually paint color with low opacity
• Movie Industry: track polygons [Cinesite Press Article]
• Pseudo-coloringPseudo-coloring• Global Transformation/Color Map
• Coloring Book MethodColoring Book Method• Photoshop: manually paint color with low opacity
• Movie Industry: track polygons [Cinesite Press Article]
• Pseudo-coloringPseudo-coloring• Global Transformation/Color Map
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Previous MethodsPrevious Methods
• Coloring Book MethodColoring Book Method• Photoshop: manually paint color with low opacity
• Movie Industry: track polygons [Cinesite Press Article]
• Pseudo-coloringPseudo-coloring• Global Transformation/Color Map
• Satellite ImagesSatellite Images• Registration [R2V Software], Orthoimagery [Premoze]
• Coloring Book MethodColoring Book Method• Photoshop: manually paint color with low opacity
• Movie Industry: track polygons [Cinesite Press Article]
• Pseudo-coloringPseudo-coloring• Global Transformation/Color Map
• Satellite ImagesSatellite Images• Registration [R2V Software], Orthoimagery [Premoze]
![Page 17: Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Center for Visual Computing Stony Brook University](https://reader038.vdocuments.net/reader038/viewer/2022110100/56649e0c5503460f94af4c5a/html5/thumbnails/17.jpg)
Previous MethodsPrevious Methods
• Coloring Book MethodColoring Book Method• Photoshop: manually paint color with low opacity
• Movie Industry: track polygons [Cinesite Press Article]
• Pseudo-coloringPseudo-coloring• Global Transformation/Color Map
• Satellite ImagesSatellite Images• Registration [R2V Software], Orthoimagery [Premoze]
• Image AnalogiesImage Analogies• Grey Source : Color Source :: Grey Target : Result
• Coloring Book MethodColoring Book Method• Photoshop: manually paint color with low opacity
• Movie Industry: track polygons [Cinesite Press Article]
• Pseudo-coloringPseudo-coloring• Global Transformation/Color Map
• Satellite ImagesSatellite Images• Registration [R2V Software], Orthoimagery [Premoze]
• Image AnalogiesImage Analogies• Grey Source : Color Source :: Grey Target : Result
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Related WorkRelated Work
• ““Color Transfer between Images” Color Transfer between Images” [Reinhard et al. 2001][Reinhard et al. 2001]
• ““Color Transfer between Images” Color Transfer between Images” [Reinhard et al. 2001][Reinhard et al. 2001]
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Related WorkRelated Work
• ““Color Transfer between Images” Color Transfer between Images” [Reinhard et al. 2001][Reinhard et al. 2001]
• ““Color Transfer between Images” Color Transfer between Images” [Reinhard et al. 2001][Reinhard et al. 2001]
TargetSource
+
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Related WorkRelated Work
TargetSource Final
+ =
• ““Color Transfer between Images” Color Transfer between Images” [Reinhard et al. 2001][Reinhard et al. 2001]
• ““Color Transfer between Images” Color Transfer between Images” [Reinhard et al. 2001][Reinhard et al. 2001]
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Reinhard et al.Reinhard et al.
• Decorrelated color space (Decorrelated color space (llαβ) [Ruderman et al., 1998]αβ) [Ruderman et al., 1998]
• Scale and shift color distributions globallyScale and shift color distributions globally(Using mean and standard deviation)
• Decorrelated color space (Decorrelated color space (llαβ) [Ruderman et al., 1998]αβ) [Ruderman et al., 1998]
• Scale and shift color distributions globallyScale and shift color distributions globally(Using mean and standard deviation)
SourceTarget
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Reinhard et al.Reinhard et al.
• Decorrelated color space (Decorrelated color space (llαβ) [Ruderman et al., 1998]αβ) [Ruderman et al., 1998]
• Scale and shift color distributions globallyScale and shift color distributions globally(Using mean and standard deviation)
• Decorrelated color space (Decorrelated color space (llαβ) [Ruderman et al., 1998]αβ) [Ruderman et al., 1998]
• Scale and shift color distributions globallyScale and shift color distributions globally(Using mean and standard deviation)
Target (Scaled) Source
![Page 23: Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Center for Visual Computing Stony Brook University](https://reader038.vdocuments.net/reader038/viewer/2022110100/56649e0c5503460f94af4c5a/html5/thumbnails/23.jpg)
Reinhard et al.Reinhard et al.
• Decorrelated color space (Decorrelated color space (llαβ) [Ruderman et al., 1998]αβ) [Ruderman et al., 1998]
• Scale and shift color distributions globallyScale and shift color distributions globally(Using mean and standard deviation)
• Decorrelated color space (Decorrelated color space (llαβ) [Ruderman et al., 1998]αβ) [Ruderman et al., 1998]
• Scale and shift color distributions globallyScale and shift color distributions globally(Using mean and standard deviation)
Target (Scaled & Shifted) Source
![Page 24: Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Center for Visual Computing Stony Brook University](https://reader038.vdocuments.net/reader038/viewer/2022110100/56649e0c5503460f94af4c5a/html5/thumbnails/24.jpg)
Our Approach Our Approach
Target
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Our ApproachOur Approach
• Select color source imageSelect color source image• Select color source imageSelect color source image
+???
SourceTarget
???
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Our ApproachOur Approach
• Select color source imageSelect color source image
• Match each target pixel with a few sourceMatch each target pixel with a few source pixelspixels
• Select color source imageSelect color source image
• Match each target pixel with a few sourceMatch each target pixel with a few source pixelspixels
+
SourceTarget
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Our ApproachOur Approach
• Select color source imageSelect color source image
• Match each target pixel with a few sourceMatch each target pixel with a few source pixelspixels• Choose best match using local pixel neighborhood statistics
• Select color source imageSelect color source image
• Match each target pixel with a few sourceMatch each target pixel with a few source pixelspixels• Choose best match using local pixel neighborhood statistics
+
SourceTarget
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Our ApproachOur Approach
• Select color source imageSelect color source image
• Match each target pixel with a few sourceMatch each target pixel with a few source pixelspixels• Choose best match using local pixel neighborhood statistics
• Transfer colorTransfer color
• Select color source imageSelect color source image
• Match each target pixel with a few sourceMatch each target pixel with a few source pixelspixels• Choose best match using local pixel neighborhood statistics
• Transfer colorTransfer color
+
SourceTarget
=
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Our ApproachOur Approach
• Select color source imageSelect color source image
• Match each target pixel with a few sourceMatch each target pixel with a few source pixelspixels• Choose best match using local pixel neighborhood statistics
• Transfer colorTransfer color
• Repeat for all pixelsRepeat for all pixels
• Select color source imageSelect color source image
• Match each target pixel with a few sourceMatch each target pixel with a few source pixelspixels• Choose best match using local pixel neighborhood statistics
• Transfer colorTransfer color
• Repeat for all pixelsRepeat for all pixels
=+
SourceTarget Final
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Global Image Matching ProcedureGlobal Image Matching Procedure
1.1. Convert images to Convert images to llαβ color spaceαβ color space
2.2. Image MatchingImage Matching
3.3. Color TransferColor Transfer
1.1. Convert images to Convert images to llαβ color spaceαβ color space
2.2. Image MatchingImage Matching
3.3. Color TransferColor Transfer
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1. Convert to lαβ Space1. Convert to lαβ Space
• Luminance (Luminance (ll), alpha (α) and beta (β) channels), alpha (α) and beta (β) channels
• Minimizes correlation between axes Minimizes correlation between axes (i.e. cross-channel artifacts)
• Luminance (Luminance (ll), alpha (α) and beta (β) channels), alpha (α) and beta (β) channels
• Minimizes correlation between axes Minimizes correlation between axes (i.e. cross-channel artifacts)
RGB lαβ
0 50 100 150 200 250 3000
1
2x 104 red channel
0 50 100 150 200 250 3000
1
2x 104 green channel
0 50 100 150 200 250 3000
1
2x 104 blue channel
Color
Image
-5 -4 -3 -2 -1 0 1 2 3 4 50
1
2x 104 luminance channel
-5 -4 -3 -2 -1 0 1 2 3 4 50
1
2x 104 alpha channel
-5 -4 -3 -2 -1 0 1 2 3 4 50
1
2x 104 beta channel
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2. Image Matching2. Image Matching
1.1. Remap luminance histograms between src/targetRemap luminance histograms between src/target1.1. Remap luminance histograms between src/targetRemap luminance histograms between src/target
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50
5000
10000
15000luminance channel-source
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50
5000
10000
15000luminance channel-target
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50
5000
10000
15000luminance channel-remapped
Target
Source-Before
Source-After
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2. Image Matching2. Image Matching
1.1. Remap luminance histograms between src/targetRemap luminance histograms between src/target
2.2. Precompute neighborhood statistics for imagesPrecompute neighborhood statistics for images
1.1. Remap luminance histograms between src/targetRemap luminance histograms between src/target
2.2. Precompute neighborhood statistics for imagesPrecompute neighborhood statistics for images
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2. Image Matching2. Image Matching
1.1. Remap luminance histograms between src/targetRemap luminance histograms between src/target
2.2. Precompute neighborhood statistics for imagesPrecompute neighborhood statistics for images
3.3. Reduce samples using jittered samplingReduce samples using jittered sampling- Faster computation due to smaller search space
1.1. Remap luminance histograms between src/targetRemap luminance histograms between src/target
2.2. Precompute neighborhood statistics for imagesPrecompute neighborhood statistics for images
3.3. Reduce samples using jittered samplingReduce samples using jittered sampling- Faster computation due to smaller search space
10 20 30 40 50 60 70 80 90 100
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2. Image Matching2. Image Matching
1.1. Remap luminance histograms between src/targetRemap luminance histograms between src/target
2.2. Precompute neighborhood statistics for imagesPrecompute neighborhood statistics for images
3.3. Reduce samples using jittered samplingReduce samples using jittered sampling- Faster computation due to smaller search space
1.1. Remap luminance histograms between src/targetRemap luminance histograms between src/target
2.2. Precompute neighborhood statistics for imagesPrecompute neighborhood statistics for images
3.3. Reduce samples using jittered samplingReduce samples using jittered sampling- Faster computation due to smaller search space
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2. Image Matching2. Image Matching
1.1. Remap luminance histograms between src/targetRemap luminance histograms between src/target
2.2. Precompute neighborhood statistics for imagesPrecompute neighborhood statistics for images
3.3. Reduce samples using jittered samplingReduce samples using jittered sampling- Faster computation due to smaller search space
1.1. Remap luminance histograms between src/targetRemap luminance histograms between src/target
2.2. Precompute neighborhood statistics for imagesPrecompute neighborhood statistics for images
3.3. Reduce samples using jittered samplingReduce samples using jittered sampling- Faster computation due to smaller search space
10 20 30 40 50 60 70 80 90 100
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2. Image Matching2. Image Matching
1.1. Remap luminance histograms between src/targetRemap luminance histograms between src/target
2.2. Precompute neighborhood statistics for imagesPrecompute neighborhood statistics for images
3.3. Reduce samples using jittered samplingReduce samples using jittered sampling- Faster computation due to smaller search space
4.4. Find best neighborhood match from samplesFind best neighborhood match from samples- Weighted metric of luminance, mean, standard deviation
1.1. Remap luminance histograms between src/targetRemap luminance histograms between src/target
2.2. Precompute neighborhood statistics for imagesPrecompute neighborhood statistics for images
3.3. Reduce samples using jittered samplingReduce samples using jittered sampling- Faster computation due to smaller search space
4.4. Find best neighborhood match from samplesFind best neighborhood match from samples- Weighted metric of luminance, mean, standard deviation
![Page 38: Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Center for Visual Computing Stony Brook University](https://reader038.vdocuments.net/reader038/viewer/2022110100/56649e0c5503460f94af4c5a/html5/thumbnails/38.jpg)
3. Color Transfer3. Color Transfer
• Transfer only alpha and beta channels (color)Transfer only alpha and beta channels (color)• Transfer only alpha and beta channels (color)Transfer only alpha and beta channels (color)
Target Image Colorized Results
![Page 39: Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Center for Visual Computing Stony Brook University](https://reader038.vdocuments.net/reader038/viewer/2022110100/56649e0c5503460f94af4c5a/html5/thumbnails/39.jpg)
3. Color Transfer3. Color Transfer
• Transfer only alpha and beta channels (color)Transfer only alpha and beta channels (color)
• The original luminance value remains unchangedThe original luminance value remains unchanged
• Transfer only alpha and beta channels (color)Transfer only alpha and beta channels (color)
• The original luminance value remains unchangedThe original luminance value remains unchanged
Target Image Colorized Results Convert to Greyscale
(Photoshop)
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Results: satelliteResults: satellite
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TargetSource
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Results: satelliteResults: satellite
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Final
TargetSource
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Results: TexturesResults: Textures
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TargetSource
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Results: TexturesResults: Textures
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TargetSource Final
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Limitations (Global Approach)Limitations (Global Approach)
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TargetSource Final
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Limitations (Global Approach)Limitations (Global Approach)
+ =
TargetSource Final
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Limitations (Global Approach)Limitations (Global Approach)
+ =
TargetSource Final
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User-Assisted ApproachUser-Assisted Approach
1.1. User selects small number of swatchesUser selects small number of swatches
2.2. Transfer color only to swatches Transfer color only to swatches (Global Matching Procedure)
3.3. Color entire target imageColor entire target image (Only use swatch samples)
1.1. User selects small number of swatchesUser selects small number of swatches
2.2. Transfer color only to swatches Transfer color only to swatches (Global Matching Procedure)
3.3. Color entire target imageColor entire target image (Only use swatch samples)
![Page 48: Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Center for Visual Computing Stony Brook University](https://reader038.vdocuments.net/reader038/viewer/2022110100/56649e0c5503460f94af4c5a/html5/thumbnails/48.jpg)
1. Selection of Swatches1. Selection of Swatches
TargetSource
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2. Transfer Color to Swatches2. Transfer Color to Swatches
• Transfer color using Global Matching ProcedureTransfer color using Global Matching Procedure(described previously)
• Transfer color using Global Matching ProcedureTransfer color using Global Matching Procedure(described previously)
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3. Colorize Entire Image3. Colorize Entire Image
• Discard original source imageDiscard original source image• Discard original source imageDiscard original source image
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3. Colorize Entire Image3. Colorize Entire Image
• Colorize the full imageColorize the full image
• Match using L2 Norm
• Colorize the full imageColorize the full image
• Match using L2 Norm
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Swatches: NotesSwatches: Notes
• Expect good results Expect good results betweenbetween swatches swatches• Expect good results Expect good results betweenbetween swatches swatches
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Swatches: NotesSwatches: Notes
• Expect good results Expect good results betweenbetween swatches swatches• Expect good results Expect good results betweenbetween swatches swatches
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50
5000
10000
15000luminance channel
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50
5000
10000
15000luminance channel
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Swatches: NotesSwatches: Notes
• Expect good results Expect good results betweenbetween swatches swatches
• Expect better matching within an imageExpect better matching within an image (Allows more precise metric: L2 Norm)
• Expect good results Expect good results betweenbetween swatches swatches
• Expect better matching within an imageExpect better matching within an image (Allows more precise metric: L2 Norm)
(2L )
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Swatches: NotesSwatches: Notes
• Expect good results Expect good results betweenbetween swatches swatches
• Expect better matching within an imageExpect better matching within an image (L2 Norm is more sensitive to image differences)
• Expect good results Expect good results betweenbetween swatches swatches
• Expect better matching within an imageExpect better matching within an image (L2 Norm is more sensitive to image differences)
Between WithinBetween Within
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Results: SwatchesResults: Swatches
TargetSource Final (Previous Method)
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Results: SwatchesResults: Swatches
TargetSource Final
+ =
+ =
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Results: SwatchesResults: Swatches
TargetSource Final
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Results: SwatchesResults: Swatches
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Results: SwatchesResults: Swatches
Final
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Results: TexturesResults: Textures
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Results: TexturesResults: Textures
Final
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Results: SwatchesResults: Swatches
TargetSource
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Results: SwatchesResults: Swatches
TargetSource Final
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LimitationsLimitations
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CommentsComments
• Works well when Works well when targettarget can be segmented well can be segmented well• Works well when Works well when targettarget can be segmented well can be segmented well
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CommentsComments
• Works well when Works well when targettarget can be segmented well can be segmented well
• Large shadows present a problem Large shadows present a problem • (partial volume effect)
• To be more useful, combine with other toolsTo be more useful, combine with other tools
• Works well when Works well when targettarget can be segmented well can be segmented well
• Large shadows present a problem Large shadows present a problem • (partial volume effect)
• To be more useful, combine with other toolsTo be more useful, combine with other tools
![Page 68: Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Center for Visual Computing Stony Brook University](https://reader038.vdocuments.net/reader038/viewer/2022110100/56649e0c5503460f94af4c5a/html5/thumbnails/68.jpg)
Running TimeRunning Time
• Pentium 3 (800 Mhz): 15 sec – 1 minPentium 3 (800 Mhz): 15 sec – 1 min
• Typical Image size: 640x480Typical Image size: 640x480
• Implemented using MATLAB (Optimized)Implemented using MATLAB (Optimized)
• Factors:Factors:• Image size
• Neighborhood Size
• Pentium 3 (800 Mhz): 15 sec – 1 minPentium 3 (800 Mhz): 15 sec – 1 min
• Typical Image size: 640x480Typical Image size: 640x480
• Implemented using MATLAB (Optimized)Implemented using MATLAB (Optimized)
• Factors:Factors:• Image size
• Neighborhood Size
![Page 69: Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Center for Visual Computing Stony Brook University](https://reader038.vdocuments.net/reader038/viewer/2022110100/56649e0c5503460f94af4c5a/html5/thumbnails/69.jpg)
VideoVideo
1.1. Colorize one frame using swatches Colorize one frame using swatches
2.2. Use swatches to colorize the entire sequenceUse swatches to colorize the entire sequence
If a single frame in a sequence is colorized well, If a single frame in a sequence is colorized well, then the entire sequence can be colorized wellthen the entire sequence can be colorized well
1.1. Colorize one frame using swatches Colorize one frame using swatches
2.2. Use swatches to colorize the entire sequenceUse swatches to colorize the entire sequence
If a single frame in a sequence is colorized well, If a single frame in a sequence is colorized well, then the entire sequence can be colorized wellthen the entire sequence can be colorized well
![Page 70: Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Center for Visual Computing Stony Brook University](https://reader038.vdocuments.net/reader038/viewer/2022110100/56649e0c5503460f94af4c5a/html5/thumbnails/70.jpg)
Video ProcedureVideo Procedure
1.1. Colorize one frame using swatches Colorize one frame using swatches 1.1. Colorize one frame using swatches Colorize one frame using swatches
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Video ProcedureVideo Procedure
1.1. Colorize one frame using swatches Colorize one frame using swatches
2.2. Use swatches to colorize the entire sequenceUse swatches to colorize the entire sequence
1.1. Colorize one frame using swatches Colorize one frame using swatches
2.2. Use swatches to colorize the entire sequenceUse swatches to colorize the entire sequence
… …
… …=
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Video: WavesVideo: Waves
![Page 73: Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Center for Visual Computing Stony Brook University](https://reader038.vdocuments.net/reader038/viewer/2022110100/56649e0c5503460f94af4c5a/html5/thumbnails/73.jpg)
Video: HorsesVideo: Horses
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Video: Visible HumanVideo: Visible Human
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ConclusionsConclusions
• Keep original luminance valuesKeep original luminance values
• Use local pixel neighborhood statistics to matchUse local pixel neighborhood statistics to match
• Simple algorithms provide fast (and good) resultsSimple algorithms provide fast (and good) results
• Keep original luminance valuesKeep original luminance values
• Use local pixel neighborhood statistics to matchUse local pixel neighborhood statistics to match
• Simple algorithms provide fast (and good) resultsSimple algorithms provide fast (and good) results
![Page 76: Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Center for Visual Computing Stony Brook University](https://reader038.vdocuments.net/reader038/viewer/2022110100/56649e0c5503460f94af4c5a/html5/thumbnails/76.jpg)
Future WorkFuture Work
• Robustness: more sophisticated matching Robustness: more sophisticated matching • Multi-resolution, other pattern matching metrics
• VolumesVolumes
• Color CorrectionColor Correction• Use local neighborhood statistics
• Color correct movies automatically
• Robustness: more sophisticated matching Robustness: more sophisticated matching • Multi-resolution, other pattern matching metrics
• VolumesVolumes
• Color CorrectionColor Correction• Use local neighborhood statistics
• Color correct movies automatically
![Page 77: Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Center for Visual Computing Stony Brook University](https://reader038.vdocuments.net/reader038/viewer/2022110100/56649e0c5503460f94af4c5a/html5/thumbnails/77.jpg)
Questions?Questions?