tree and leaf recognition
DESCRIPTION
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: - PowerPoint PPT PresentationTRANSCRIPT
![Page 1: Tree and leaf recognition](https://reader036.vdocuments.mx/reader036/viewer/2022062500/56815946550346895dc68177/html5/thumbnails/1.jpg)
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
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The Problem
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Two ways solution:
Recognize using a leaf
Recognize using the trunk
![Page 4: Tree and leaf recognition](https://reader036.vdocuments.mx/reader036/viewer/2022062500/56815946550346895dc68177/html5/thumbnails/4.jpg)
Bark recognitionUsing Laws filters
For small texture: With 4 classes
For bigger texture like tree barks: With 6 classes
Common Hawthorn
Platanus × hispanica
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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
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Leaf recognitionSegmentation of leaves - GrabCut
- GrabCut is an iterative image segmentation method based on graph cuts
- Needs user interaction
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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
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Classification
- Simple methods are used- Majority voting- k-nearest neighbors (with Euclidean
distance)
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Results
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Problems and solutionsSmall data base
More samples
More test samples
Similarity between the testing and the data set leavesDifferent descriptorsMore complex classifiers
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SummaryTree recognition based on leaves and barkBark recognition
Laws filterLeaf recognition
SegmentationFeature extractionClassification
![Page 12: Tree and leaf recognition](https://reader036.vdocuments.mx/reader036/viewer/2022062500/56815946550346895dc68177/html5/thumbnails/12.jpg)
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