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Leafsnap: classification
Mariia Dmitrieva
Mohamed Elawady
Paper
“Leafsnap: A Computer Vision System for Automatic
Plant Species Identification”
Neeraj Kumar, Peter N. Belhumeur, Ar...
Outline
1.Introduction
2.Recognition Process
2.1. Classification
2.2. Segmentation
2.3. Feature Extraction
2.4. Comparison...
Outline
1.Introduction
2.Recognition Process
2.1. Classification
2.2. Segmentation
2.3. Feature Extraction
2.4. Comparison...
1. Why & Who…
5
Who
Columbia
University
University of
Maryland
Smithsonian
Institution
Why
Book
Scout
6
1. Plant Species Identification
7
1. Framework
Outline
1.Introduction
2.Recognition Process
2.1. Classification
2.2. Segmentation
2.3. Feature Extraction
2.4. Comparison...
2. Recognition Process
4. Comparison
Compare the features to those from a labeled database of leaf image and returning the...
2.1. Classification
Input Image
Pre-
Processing
Step
Compute
GIST
features
Perform
SVM
classifier
Leaf or
Non-leaf
10
2.1. SVM Classifier
11
Which one is the best?
Support
Vectors
SVM
Line that maximizes
the minimum margin
among only suppor...
2.2. Segmentation
Color
• High variable across different
leaves of the same spices
Venation
Pattern
• Undetectable due to ...
2.2. Segmentation
Initial Segmentation using EM I
RGB
Convert
to HSV
HSV
HSV
Hue
HueSaturation
Saturation
Value
Value
SV
S...
2.2. Segmentation
Initial Segmentation using EM II
Leaf
Background
EM
SV
14
2.2. Segmentation
Removing False Positive Regions
INPUT
Initial
Segmentation
Result of
Current Step
Dilation + Elimination...
2.2. Segmentation
Removing the stem
INPUT
Initial
Segmentation
Result of
Current Step
Opening and
Difference
Operations
Re...
2.3. Extraction
17
 Extraction
 Comparison
2.3. Extraction: Curvature
Complications
 Rotations
 Scale Changes
 Axis Alignment
 Complex Boundaries
 Segmentation ...
 coarse scale
 fine scale
19
2.3. Extraction
Integral Measures
20
2.3. Extraction
Multiscale Curvature Measures
2.3. Extraction
Histogram of the Curvature
21
2.3. Extraction
Advantages of the HoCS
 Fast
 Invariant to rotation
 Not requiring alignment
 Insensitive to small seg...
2.4. Comparison
 Nearest neighbors search
 Database:
 23,915 lab images
 5,129 mobile phone images
Broussonettia papyr...
2.4. Comparison
Nearest neighbor search
 Comparison by histogram intersection distance:
 0.31 seconds
 Top 25 results a...
Outline
1.Introduction
2.Recognition Process
2.1. Classification
2.2. Segmentation
2.3. Feature Extraction
2.4. Comparison...
26
1st match is right
69% of time
Within top 5 matches
93% of time
4. Results
5. Future directions
New objects
27
Education
Tracking
around the Earth
6. Demo Video
28
Video By “Into Mobile”
http://www.youtube.com/watch?
v=k02C7p7mQ_c
Bibliography
 “Introduction to Support Vector Machines”
http://docs.opencv.org/doc/tutorials/ml/introduction_to_svm/intro...
30
Thanks For Listening !
Leafsnap: classification
31
QUESTIONS
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Reading Group Activity - May 2013
Scene Segmentation and Interpretation Module
Girona University
VIBOT Promotion 7 (2012-2014)

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Leafsnap: classification

  1. 1. 1 Leafsnap: classification Mariia Dmitrieva Mohamed Elawady
  2. 2. Paper “Leafsnap: A Computer Vision System for Automatic Plant Species Identification” Neeraj Kumar, Peter N. Belhumeur, Arijit Biswas, David W. Jacobs,W. John Kress, Ida C. Lopez, and Joao V.B. Soares European Conference on Computer Vision 2012 2
  3. 3. Outline 1.Introduction 2.Recognition Process 2.1. Classification 2.2. Segmentation 2.3. Feature Extraction 2.4. Comparison 4.Results 5.Future Directions 6.Demo 3
  4. 4. Outline 1.Introduction 2.Recognition Process 2.1. Classification 2.2. Segmentation 2.3. Feature Extraction 2.4. Comparison 4.Results 5.Future Directions 6.Demo 4
  5. 5. 1. Why & Who… 5 Who Columbia University University of Maryland Smithsonian Institution Why Book Scout
  6. 6. 6 1. Plant Species Identification
  7. 7. 7 1. Framework
  8. 8. Outline 1.Introduction 2.Recognition Process 2.1. Classification 2.2. Segmentation 2.3. Feature Extraction 2.4. Comparison 4.Results 5.Future Directions 6.Demo 8
  9. 9. 2. Recognition Process 4. Comparison Compare the features to those from a labeled database of leaf image and returning the species with the closest matches 3. Feature Extraction Select curvature features from the binarized image representing the shape of the leaf 2. Segmentation Obtain a binary image separating the leaf from the background 1. Classification Whether the input image is a valid leaf or not 9
  10. 10. 2.1. Classification Input Image Pre- Processing Step Compute GIST features Perform SVM classifier Leaf or Non-leaf 10
  11. 11. 2.1. SVM Classifier 11 Which one is the best? Support Vectors SVM Line that maximizes the minimum margin among only support vectors
  12. 12. 2.2. Segmentation Color • High variable across different leaves of the same spices Venation Pattern • Undetectable due to the poor image quality of most phone cameras Flowers • Only present at limited times of year Leaf Shape • Good at one condition: photograph them against light and non textured background Initial Segmentation using EM Removing False Positive Regions Removing The Stem 12
  13. 13. 2.2. Segmentation Initial Segmentation using EM I RGB Convert to HSV HSV HSV Hue HueSaturation Saturation Value Value SV SV with H=0 Leaf Background 13
  14. 14. 2.2. Segmentation Initial Segmentation using EM II Leaf Background EM SV 14
  15. 15. 2.2. Segmentation Removing False Positive Regions INPUT Initial Segmentation Result of Current Step Dilation + Elimination Small Regions 15
  16. 16. 2.2. Segmentation Removing the stem INPUT Initial Segmentation Result of Current Step Opening and Difference Operations Remove False Positive Regions 16
  17. 17. 2.3. Extraction 17  Extraction  Comparison
  18. 18. 2.3. Extraction: Curvature Complications  Rotations  Scale Changes  Axis Alignment  Complex Boundaries  Segmentation Errors 18
  19. 19.  coarse scale  fine scale 19 2.3. Extraction Integral Measures
  20. 20. 20 2.3. Extraction Multiscale Curvature Measures
  21. 21. 2.3. Extraction Histogram of the Curvature 21
  22. 22. 2.3. Extraction Advantages of the HoCS  Fast  Invariant to rotation  Not requiring alignment  Insensitive to small segmentation and discretization errors  Independent of the topological complexity 22
  23. 23. 2.4. Comparison  Nearest neighbors search  Database:  23,915 lab images  5,129 mobile phone images Broussonettia papyrifera 23
  24. 24. 2.4. Comparison Nearest neighbor search  Comparison by histogram intersection distance:  0.31 seconds  Top 25 results are presented 24  B i ii baNbad ),min(),(
  25. 25. Outline 1.Introduction 2.Recognition Process 2.1. Classification 2.2. Segmentation 2.3. Feature Extraction 2.4. Comparison 4.Results 5.Future Directions 6.Demo 25
  26. 26. 26 1st match is right 69% of time Within top 5 matches 93% of time 4. Results
  27. 27. 5. Future directions New objects 27 Education Tracking around the Earth
  28. 28. 6. Demo Video 28 Video By “Into Mobile” http://www.youtube.com/watch? v=k02C7p7mQ_c
  29. 29. Bibliography  “Introduction to Support Vector Machines” http://docs.opencv.org/doc/tutorials/ml/introduction_to_svm/introd uction_to_svm.html  “A Computer Vision System for Automatic Plant Species Identification” http://homes.cs.washington.edu/~neeraj/projects/leafsnap/base/pr esentations/2012_leafsnap/leafsnap-eccv2012.pptx  “Integral invariants for robust geometry processing” Pottmann, H., Wallner, J., Huang, Q.X., Yang, Y.L., Computer Aided Geometric Design 26, 37–60 (2009) 29
  30. 30. 30 Thanks For Listening !
  31. 31. Leafsnap: classification 31 QUESTIONS

Reading Group Activity - May 2013 Scene Segmentation and Interpretation Module Girona University VIBOT Promotion 7 (2012-2014)

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