Tree and leaf recognition

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Presentation 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

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/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