Transcript 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
The Problem
 Two ways solution:
 Recognize using a leaf
 Recognize using the trunk
Bark recognition
 Using Laws filters
 For small texture:

With 4 classes
 For bigger texture like tree barks:

With 6 classes
Common
Hawthorn
Platanus ×
hispanica
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
Leaf recognition
 Segmentation of leaves - GrabCut
- GrabCut is an iterative image segmentation
method based on graph cuts
- Needs user interaction
 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
 Classification
- Simple methods are used
- Majority voting
- k-nearest neighbors (with Euclidean distance)
Results
accuracy
1
0.9
0.8
0.7
0.6
0.5
accuracy
0.4
0.3
0.2
0.1
0
Fourier
moments with
majority voting
Fourier
moments with 1
nearest
neighbour
Fourier
moments with 3
nearest
neighbours
Hu moments
with majority
voting
Hu moments
with 1 nearest
neighbour
Hu moments
with 3 nearest
neighbours
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
Summary
 Tree recognition based on leaves and bark
 Bark recognition
 Laws filter
 Leaf recognition
 Segmentation
 Feature extraction
 Classification
References
 https://code.ros.org/trac/opencv/browser/trunk/opencv/sample
s/c/grabcut.cpp?rev=2326
 http://en.wikipedia.org/wiki/Image_moment
 http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm
 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