veggie vision: a produce recognition system r.m. bolle j.h. connell n. haas r. mohan g. taubin ibm...
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Veggie Vision:A Produce Recognition
SystemR.M. Bolle J.H. Connell N. Haas R.
Mohan G. TaubinIBM T.J. Watson Resarch Center
Veggie Vision:A Produce Recognition
SystemR.M. Bolle J.H. Connell N. Haas R.
Mohan G. TaubinIBM T.J. Watson Resarch Center
Presented by
Chris McClendon
Presented by
Chris McClendon
What is it?What is it?
Veggie vision in an automated produce ID system
Veggie vision in an automated produce ID system
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HardwareHardware
A scale A polarized light
A camera A PIII 200 MHz A method
A scale A polarized light
A camera A PIII 200 MHz A method
ChallengesChallenges
The segmentation problem Foregroud/background differentiation
Packaging Background variation
The segmentation problem Foregroud/background differentiation
Packaging Background variation
ChallengesChallenges
Color Constancy One element of recognition is based on color profiles.
The lighting in a grocery store is subject to large variation
Color Constancy One element of recognition is based on color profiles.
The lighting in a grocery store is subject to large variation
ChallengesChallenges
Speed of Recognition The system should integrate with the time scale for other checkout operations
The agreed time parameter should be around 1 second
Speed of Recognition The system should integrate with the time scale for other checkout operations
The agreed time parameter should be around 1 second
ChallengesChallenges
Performance Ideally equal to barcode scanning (100%)
Realistic expectations of performance should be at least as good as that of the average checker (~80%)
Performance Ideally equal to barcode scanning (100%)
Realistic expectations of performance should be at least as good as that of the average checker (~80%)
ChallengesChallenges
Ease of Use Integrated into existing barcode reader housing
Minimal operator training
Ease of Use Integrated into existing barcode reader housing
Minimal operator training
ChallengesChallenges
System Training Gradual adaption to variations in season, supplier, and ripeness/freshness
System Training Gradual adaption to variations in season, supplier, and ripeness/freshness
ChallengesChallenges
Database size Seasonal Varies by store, region, and harvest
Database size Seasonal Varies by store, region, and harvest
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SolutionsSolutions Parallel vs. perpendicular polarization for filtering out glare from the light source
Parallel vs. perpendicular polarization for filtering out glare from the light source
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SolutionsSolutions
2 images used for segmentation, one light and one dark
Brightness variation greater than the threshold (T∆) are considered foreground
2 images used for segmentation, one light and one dark
Brightness variation greater than the threshold (T∆) are considered foreground
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SolutionsSolutions
Notice the plastic bag is also illuminated
Another threshold (Tdark) is used to identify the transparent bags
Notice the plastic bag is also illuminated
Another threshold (Tdark) is used to identify the transparent bags
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Question Session #1Question Session #1
Any other illumination-related problems in this scenario?
Any other illumination-related problems in this scenario?
Question Session #1Question Session #1
Any other illumination-related problems in this scenario?
Dark produce
Waxed or shiny produce
Any other illumination-related problems in this scenario?
Dark produce
Waxed or shiny produce
Segmentation ResultsSegmentation Results
Necessary Conditions Stationary
Scale assistance ensures stable produce
Necessary Conditions Stationary
Scale assistance ensures stable produce
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Feature SelectionFeature Selection
Histograms used extensively for feature representation Much smaller than actual images Well researched method for representation of visual cues
Training issues
Histograms used extensively for feature representation Much smaller than actual images Well researched method for representation of visual cues
Training issues
Color FeaturesColor Features
HSI (HSL) color space
HSI (HSL) color space
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Apples vs OrangesApples vs Oranges
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Texture FeaturesTexture Features
More difficult to describe computationally Textel--artichokes or pineapples Random variation--parsley
More difficult to describe computationally Textel--artichokes or pineapples Random variation--parsley
Texture Detection Methods
Texture Detection Methods
Measure A Convolution of crossed bar masks
[ -1 2 -1][-1 -1 2 2 -1 -1]
Measure A Convolution of crossed bar masks
[ -1 2 -1][-1 -1 2 2 -1 -1]
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Ch (x,y)
Cv (x,y)
M(x,y) = Ch (x,y)2 +Cv (x,y)
2
Texture Detection Methods
Texture Detection Methods
Method B Deviation of image intensity from its nearest neighbors
Performed on reduced images for speed
Method B Deviation of image intensity from its nearest neighbors
Performed on reduced images for speed
Question Session #2Question Session #2 Results Results
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Classification & Training
Classification & Training
Symbols Pi, i = 1,2,….,N prototype histograms
Q, a produce histogram to be identified
Each histogram has 4 components F = {hue, saturation, intensity, texture}
Normalized
Symbols Pi, i = 1,2,….,N prototype histograms
Q, a produce histogram to be identified
Each histogram has 4 components F = {hue, saturation, intensity, texture}
Normalized
Classification & Training
Classification & Training
Symbols Each prototype histogram Pi is associated with an identifier I(Pi)
How to compute distance Manhattan style
Symbols Each prototype histogram Pi is associated with an identifier I(Pi)
How to compute distance Manhattan style
Classification & Training
Classification & Training
Distance equation Distance equation
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Classification & Training
Classification & Training
Now that the distance is calculated Decision Rule 1
Now that the distance is calculated Decision Rule 1
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d j < T, j =1...n
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I(P j ) = I(P1), j = 2...nQuickTime™ and a
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Classification & Training
Classification & Training
For acceptable threshold but multiple identifiers Decision Rule 2
For acceptable threshold but multiple identifiers Decision Rule 2
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d j < T, j =1...n
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I(P j ) ≠ I(P1), j = 2...n
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Classification & Training
Classification & Training
Out of bounds Decision Rule 3
Out of bounds Decision Rule 3
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d1 > T QuickTime™ and aTIFF (Uncompressed) decompressor
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Acting on ResultsActing on Results
Sure Directly accepted by the register
Okay & Uncertain Sorted nearest matches displayed
Sure Directly accepted by the register
Okay & Uncertain Sorted nearest matches displayed
Acting on ResultsActing on Results
If none of the options are chosen, a new prototype for Pchosen is added
The correctly identified Q’s prototype is judged for accuracy
All non-chosen prototypes of the P class are ‘aged’
If none of the options are chosen, a new prototype for Pchosen is added
The correctly identified Q’s prototype is judged for accuracy
All non-chosen prototypes of the P class are ‘aged’
Overall ResultsOverall Results
Feature Success trial 1 Feature Success trial 1
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Overall ResultsOverall Results
Feature Success trial 2 Feature Success trial 2
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Overall ResultsOverall Results
Training technique analysis Training technique analysis
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Overall ResultsOverall Results
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