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Tree and Leaf Recognition Techniques: A Comprehensive Study

This project explores innovative methods for recognizing trees through their leaves and bark. It employs various techniques such as Gabor filters, SIFT features, and GrabCut segmentation to improve the accuracy of tree identification. Challenges such as scale invariance and sample size are addressed, with potential solutions including more complex classifiers like SVM and KNN. The study highlights the importance of feature extraction and matching in tree recognition and provides insights into overcoming common problems in the field. References and methodologies are discussed.

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Tree and Leaf Recognition Techniques: A Comprehensive Study

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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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