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Leaf Classification from Boundary Analysis. Anne Jorstad AMSC 663 Project Proposal Fall 2007 Advisor: Dr. David Jacobs, Computer Science. Background. Electronic Field Guide for Plants University of Maryland Columbia University National Museum of Natural History Smithsonian Institution - PowerPoint PPT Presentation
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Leaf Classification from Boundary Analysis
Anne JorstadAMSC 663 Project ProposalFall 2007
Advisor: Dr. David Jacobs, Computer Science
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Background
Electronic Field Guide for Plants University of Maryland Columbia University National Museum of Natural History
Smithsonian Institution
Project in development over 4 years
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Background
Current System: Inputs photo of leaf on plain
background Segments leaf from background Compares leaf to all leaves in database,
using global shape information Returns images of closest matches to
the user
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Background
Sean White, Dominic Marino, Steven Feiner. Designing a Mobile User Interface for Automated Species Identification. Columbia University, 2007.
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Background
All leaves assumed to be from woody plants the Baltimore-Washington, DC area
245 species, 8000 images
The proof of concept has been implemented successfully
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Proposal
Current System: All shape information is compared at a
global level, no specific consideration of edge types
My Project: Incorporate local boundary information
to complement existing system
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Proposal
Leaf edges:
smooth
serrated
serrated, finer teeth
“double-toothed”
wavylobed and serrated
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Proposal Specifics
Start with boundary curves as discrete points (already have this data with good accuracy)
Represent as , to use 1-D techniques
Classify!
)()( tiytx
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Method 1: Harmonic Analysis
Harmonic Analysis Decompose boundary into wavelet
basis Different families of species have
distinct serration patterns in the frequency domain
What wavelet basis to choose?
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Aside: What is a wavelet?
Fourier Transform: decomposes a function into frequency components
Wavelet Transform: similar to Fourier, but with quickly decaying or compactly supported basis functions good for feature detection
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Method 1: Harmonic Analysis
Think of the boundary as a texture Several Computer Vision algorithms
exist for classifying textures Example:
Describe texture in terms of a set of fundamental features or patterns (sound like a wavelet basis?), search for them throughout the image
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Method 2: Inner-Distance
“Inner-Distance” on multiple scales Measures the shortest distance between
two points on a path contained entirely within a figure
Good for detecting similarities between deformable structures
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Method 2: Inner-Distance
The inner-distance has been successfully applied in several situations
Used already as part of the global classification
New: sample points on several scales and look for shape discrepancies not previously measured
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Method 2: Inner-Distance
Examining inner-distances over a hierarchy of scales will capture new local information
Large scale: similar inner-distances
Small scale: distinct inner-distances
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Method 3: Convexity
A serrated leaf is much less convex than a smooth one; use convexity measure as a pre-processing classification tool
May not prove useful, but might be worth exploring
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Method 3: Convexity
Several ways to assign a convexity number to a shape:
etc.
))((
)(
objectConvexHullArea
objectAreaConvexity
)(
))((
objectPerimeter
objectConvexHullPerimeterConvexity
object
ConvexHull(object)
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Algorithm Verification
Create artificial “leaves” with known properties
Prove algorithm correctness on these simple known cases
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Algorithm Verification
Run new algorithm on current data sets Demonstrate “reasonable”
classification accuracy for relevant examples
Global information not considered, so expect that not all distinguishing features will be recognized
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Algorithm Verification
Incorporate into existing system
Ideally: Provide classification results
independent from current results, so together a better overall classification is achieved
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Specifications
Current system: MATLAB and C
My contribution: mostly MATLAB Image Processing Toolbox Wavelet Toolbox
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Specifications
End product to run on portable computer Code must run quickly on a small
processor Development and testing from PC
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References “A New Convexity Measure for Polygons”. Jovisa Zunic, Paul L. Rosin.
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 7, July 2004.
“Contour and Texture Analysis for Image Segmentation”. Jitendra Malik, Serge Belongie, thomas Leung, Jainbo Shi. International Journal of Computer Vision, vol. 34, no. 1, July 2001.
“Designing a Mobile User Interface for Automated Species Identification”. Sean White, Dominic Marino, Steven Feiner. Proceedings of the SIGCHI, April 2007.
“First Steps Toward an Electronic Field Guide for Plants”. Gaurav Agarwal, Haibin Ling, David Jacobs, Sameer Shirdhonkar, W. John Kress, Rusty Russell, Peter Belhumeur, Nandan Dixit, Steve Feiner, Dhruv Mahajan, Kalyan Sunkavalli, Ravi Ramamoorthi, Sean White. Taxon, vol. 55, no. 3, Aug. 2006.
“Using the Inner-Distance for Classification of Articulated Shapes”. Haibin Ling, David W. Jacobs. IEEE Conference on Computer Vision and Pattern Recognition, vol. II, June 2005.
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Questions? Comments?
