Team D : Project #4

George Beretas – University College LondonDavid 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 recognitionUsing 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 recognitionSegmentation 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

Problems and solutionsSmall data base

More samples

More test samples

Similarity between the testing and the data set leavesDifferent descriptorsMore complex classifiers

SummaryTree recognition based on leaves and barkBark recognition

Laws filterLeaf recognition

SegmentationFeature extractionClassification

Referenceshttps://code.ros.org/trac/opencv/browser/trunk/

opencv/samples/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