identifying tree species from bark david gainer. why bark? year-round can be used for dead...
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![Page 1: Identifying Tree Species From Bark David Gainer. Why Bark? Year-Round Can be used for dead trees/logs/stumps Can be observed at ground level Can be combined](https://reader035.vdocuments.site/reader035/viewer/2022062423/56649ebb5503460f94bc3403/html5/thumbnails/1.jpg)
Identifying Tree Species From Bark
David Gainer
![Page 2: Identifying Tree Species From Bark David Gainer. Why Bark? Year-Round Can be used for dead trees/logs/stumps Can be observed at ground level Can be combined](https://reader035.vdocuments.site/reader035/viewer/2022062423/56649ebb5503460f94bc3403/html5/thumbnails/2.jpg)
Why Bark?
• Year-Round
• Can be used for dead trees/logs/stumps
• Can be observed at ground level
• Can be combined with systems for leafs/needles/twigs/etc.
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Inherent Difficulties
• Age: Old Trees Have Very Different Looking Bark than Young Trees (So a complete system would have to identify tree and age range).
Juvenile Bunya Pine Mature Bunya Pine
Pictures from: http://tree-species.blogspot.com/
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Inherent Difficulties
• Shape: Shape of the trunk effects the appearance of the bark.
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Human Performance
• Osterreichische Bundesforste AG Dataset
• 2 Experts: 56.6% and 77.8% (trouble with the different pines)
Source: Fiel and Sablatnig http://cvww2011.icg.tugraz.at/papers_web/p13.pdf
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Prior Results/Approaches
• Wan et al (2004) 77% with GLCM. Higher using color bands separately. 170 images of 9 classes
• Song et al (2004) 87.5% with GLCM and binary features. 180 images of 8 classes
• Huang et al (2006) 92.5% with GLCM and fractal dimensions. 360 images of 24 classes
• Fiel and Sablating (2011) 69.7% vectors of SIFT pattern matches. 1183 images of 11 classes. Found that GLCM only yielded 61% for the same dataset
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My Dataset
• Pictures of species in Central Park• 2 Pictures for each individual tree• Taken with iPhone, for each tree 1 picture
using HDR setting, 1 with the standard• Taken between 14” and 16” away from the
tree• Goal is 10 or more pictures for each
species for 20 or more species• Currently 4-14 pictures for 14 tree species
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My Dataset
• Current Species: American Elm, London Plane, Red Oak, Eastern White Pine, Willow, Beech, Cherry (Kwanzan), Norway Maple, Birch (Himalayan Whitebark), Linden, American Sycamore, Blue Spruce, Sycamore Maple, Swamp Oak
• Identified with Help of Ned Barnard and Ken Chayas “Central Park Entire” map
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Birch (Himalayan Whitebark)
American Elm
London Plane Beech
Birch (Himalayan Whitebark) American Elm
London Plane Beech
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Feature Selection
• From the prior research, I intend to implement GLCM, look at fractal dimensions
• I’d also like to come up with some new statistics especially from various qualities of edge images
• I’d also like to look at statistics derived from line detection
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Feature Selection
• Gradient/Edge images in X and Y directions:
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Classification
• Compare k-nn, multiclass SVM and maximum likelihood approaches