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Identification of Leaves by Interior Shape and Texture

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Presentation on theme: "Identification of Leaves by Interior Shape and Texture"— Presentation transcript:

1 Identification of Leaves by Interior Shape and Texture
Veronica Dooly – A-BTech Jennifer LeBlanc – West Wilkes High School Introduce us

2 ProScope HR Hand-held microscope
Ability to change magnification by changing lenses 30x 100x 400x Ability to save images or videos Found it difficult to focus Refocusing every leaf Compounded by leaves curling All magnify leaves could only have a section photographed

3 Magnification Strengths
30X 100X 400X

4 Magnification Images EDGE CENTER STEM 30 x

5 Patterns Observed Voronoi Polygons
Visual observation to find patterns Saw Voronoi – occur in nature

6 Patterns Observed Giraffe
Observed patterns that reminded of other things In addition to giraffe saw cheetah and reptilian

7 Patterns Observed Transformations
Same shape repeats in some leaves Rotated or dilated (different size)

8 Best Magnification for Visual Pattern from Images per Leaf
Leaf Type Type 1 Type 2 Type 3 Type 4 Type 5 Type 6 Type 7 Type 10 Type 11 Type 12 Type 13 Leaf sample 1 2 3 Magnification 400 100 30 Not “Not” – original photo was best

9 ImageJ Adjusting the color threshold
Attempt to make patterns more visible ImageAdjust Color Threshold Manually adjusted by dragging Same leaf and magnification but optimum RGB varied greatly

10 ImageJ Adjusting the black/white threshold
Attempt to make patterns more visible ImageAdjust B&W Threshold Manually adjusted by dragging Each leaf had different best range

11 OpenSURF Vector of 64 descriptors around each keypoint
SURF – (Speeded Up Robust Features) Detect keypoints and descriptors on images Invariant to size or color K-means clustering Groups the descriptors into N clusters based on their distance from the nearest centroid 100 cluster groups Vector of 64 descriptors around each keypoint Invariance overcame problems with magnification Centroids – Voronoi polygons Classifiers The CrossVal function divides a single database into folds and used one partition of leaves to test identification using training sections External Validation uses separate training and testing directories External Validation

12 Key Points Some keypoints not on leaf
In pictures with multiple leaves keypoints everywhere SURF creates a vector of 64 descriptors gleaned around each key point

13 K-means Clustering Keypoints are clustered into points that are centroids of a voronoi polygon

14 Leaf with All Clusters Displayed

15 Cluster 38 on Common Hazel

16 Results of Automated Processing
Classifying Method Database(s) Number of Samples Linear Discriminate Performance Diagonal Linear Discriminate CrossVal Train Leaves 8,416 61.7% 52.7% Test Leaves 3,150 59.0% 51.8% External Validation Train & Test Leaves 11,566 24.5% 19.7% First set was 389 leaves leaving 10 unique samples to test Chose to use larger data base – with 118 unique samples LeafClef database used (contest) Discrepancy between CrossVal and external validation Batches Test images not in training images Type of image Further – Voronoi, other classifiers (i.e. quadratic), SURF with magnification Scan Photograph Pseudoscan Linear Discriminate Performance 23.9% 5.3% 31.4% Diagonal Linear Discriminate Performance 18.4% 5.2% 22.2%

17 Acknowledgements Dr. Mitch Parry Dr. Rahman Tashakkori Clint Guin
Dr. Mary Beth Searcy The Department of Computer Science at Appalachian State University The National Science Foundation

18 Resources Crisan, S.; Tarnovan, I.; Crisan, T., “Radiation optimization and image processing algorithms in the identification of hand vein patterns.” Computer Standards & Interfaces, vol. 32, no. 3, pp , March 2010, DOI: doi.org/ /j.csi Henries, D.; Tashakkori, R., ‘Extraction of leaves from herbarium images,’ Electro/Information Technology (EIT), 2012 IEEE International Conference, vol. 1, no. 6, pp. 6-8, May DOI: /EIT MathWorks. (n.d.). Kmeans [Online]. Available: Raza, S.; Parry R.; Moffitt, R.; Young, A.; Wang, M., “An analysis of scale and rotation invariance in the bag-of-features method for hitopathological image classification.” MICCAI 2011 Lecture Notes in Computer Science, vol., 6893, pp Available: DOI: / _9. Rebollo-Monedero, D.; Solé, M.; Forné, J., “A modification of the k-means method for quasi-unsupervised learning”, Knowledge-Based Systems, vol. 37, pp , Available: Schmitt, D.; McCoy, N., “Object classification and localization using surf descriptors.” December Available: Xia, Q., “The formation of a tree leaf.” ESAIM.Control, Optimisation and Calculus of Variations, vol. 13, no. 2, pp , Available: DOI: /cocv: Xiaodong, Z.; Xiaojie W., “Leaf vein extraction based on gray-scale morphology”, IJIGSP, vol.2, no.2, pp.25-31, Available:


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