1. Tree and leaf recognition
Team D : Project #4
George Beretas – University College London
David Papp - University of Pannonia
Gabor Retlaki - Pazmany Peter Catholic University
Ovidiu Adrian Turda - Technical University of Cluj-Napoca

2. The Problem

3. Two ways solution:
Recognize using a leaf
Recognize using the trunk

4. Bark recognition Using Laws filters For small texture:
With 4 classes
For bigger texture like tree barks:
With 6 classes
Common Hawthorn
Platanus × hispanica

5. Problems and possible solutions
These filters are not scale invariant, it is the cause of bigger patches, and not a homogenous output image.
We could use Gabor filter to make the system scale invariant.
Other possible solutions for recognition
For feature extraction:
SIFT features
GLCM /gray level co-occurence matrix/
For feature matching
Calculating cross correlation between features
Using mutual information
For clustering
RANSAC
SVM
KNN

6. Leaf recognition Segmentation of leaves - GrabCut
- GrabCut is an iterative image segmentation method based on graph cuts
- Needs user interaction

7. Hu moments
- Hu moments are a set of image moments
- They are invariant under translation, changes in scale, and rotation
Fourier moments
- Calculate the distance between the centroid and the boundary at certain angles
- Calculate DFT on this sequence

8. Classification
- Simple methods are used
- Majority voting
- k-nearest neighbors (with Euclidean distance)

9. Results

10. Problems and solutions
Small data base
More samples
More test samples
Similarity between the testing and the data set leaves
Different descriptors
More complex classifiers

11. Summary Tree recognition based on leaves and bark Bark recognition
Laws filter
Leaf recognition
Segmentation
Feature extraction
Classification

12. References
Krishna Singh, Indra Gupta, Sangeeta Gupta, 2010, “SVM-BDT PNN and Fourier Moment Technique for
Classification of Leaf Shape”, International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 3, No. 4