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Tree and leaf recognition

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:

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Tree and leaf recognition

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  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 • 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

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