Leaf classification from binary image via artificial intelligence
The invariant recognition of 2D binary images is the main subject of the paper. Two methods for invariant pattern recognition based on 2D Fourier power spectrum with guaranteed translation invariance are proposed. First method introduce the features invariant to translation, scaling, rotation and mi...
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| Published in | Biosystems engineering Vol. 142; pp. 83 - 100 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.02.2016
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| Online Access | Get full text |
| ISSN | 1537-5110 1537-5129 |
| DOI | 10.1016/j.biosystemseng.2015.12.007 |
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| Abstract | The invariant recognition of 2D binary images is the main subject of the paper. Two methods for invariant pattern recognition based on 2D Fourier power spectrum with guaranteed translation invariance are proposed. First method introduce the features invariant to translation, scaling, rotation and mirroring (TSO invariance). The second method introduces the features invariant to general affine transform (A invariance). The methods are used to obtain TSO/A invariant spectra except of the rotation effect which are analysed on circular paths with fixed radii. Harmonic analysis of power fluctuations around paths generates Fourier coefficients and their square absolute values are used as TSO/A invariant descriptors. The proposed methods were tested on two large sets of 2D digital images of tree leaves. After TSO/A invariant processing of thresholded digital images, kernel Support Vector Machine or self-organizing neural network were used for leaf categorisation.
•A TSO and affine invariant systems of 2D binary image descriptors was developed.•Properties of invariant descriptors experimentally verified with digital leaf images.•Ability of invariant systems to retrieve the TSO or affine transformed images proved.•1-NN and Support Vector Machine used for leaf classification.•Kohonen Self-Organising Maps used for leaf categorisation. |
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| AbstractList | The invariant recognition of 2D binary images is the main subject of the paper. Two methods for invariant pattern recognition based on 2D Fourier power spectrum with guaranteed translation invariance are proposed. First method introduce the features invariant to translation, scaling, rotation and mirroring (TSO invariance). The second method introduces the features invariant to general affine transform (A invariance). The methods are used to obtain TSO/A invariant spectra except of the rotation effect which are analysed on circular paths with fixed radii. Harmonic analysis of power fluctuations around paths generates Fourier coefficients and their square absolute values are used as TSO/A invariant descriptors. The proposed methods were tested on two large sets of 2D digital images of tree leaves. After TSO/A invariant processing of thresholded digital images, kernel Support Vector Machine or self-organizing neural network were used for leaf categorisation. The invariant recognition of 2D binary images is the main subject of the paper. Two methods for invariant pattern recognition based on 2D Fourier power spectrum with guaranteed translation invariance are proposed. First method introduce the features invariant to translation, scaling, rotation and mirroring (TSO invariance). The second method introduces the features invariant to general affine transform (A invariance). The methods are used to obtain TSO/A invariant spectra except of the rotation effect which are analysed on circular paths with fixed radii. Harmonic analysis of power fluctuations around paths generates Fourier coefficients and their square absolute values are used as TSO/A invariant descriptors. The proposed methods were tested on two large sets of 2D digital images of tree leaves. After TSO/A invariant processing of thresholded digital images, kernel Support Vector Machine or self-organizing neural network were used for leaf categorisation. •A TSO and affine invariant systems of 2D binary image descriptors was developed.•Properties of invariant descriptors experimentally verified with digital leaf images.•Ability of invariant systems to retrieve the TSO or affine transformed images proved.•1-NN and Support Vector Machine used for leaf classification.•Kohonen Self-Organising Maps used for leaf categorisation. |
| Author | Kukal, Jaromír Horaisová, Kateřina |
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| Cites_doi | 10.1016/S1047-3203(03)00003-8 10.1145/1961189.1961199 10.2478/acsc-2014-0011 10.1016/j.amc.2006.07.072 10.1016/0031-3203(93)90098-H 10.1016/j.patcog.2010.05.015 10.1016/j.cviu.2010.07.007 10.1016/j.eswa.2012.01.073 10.1155/2012/690262 10.1023/A:1022627411411 10.1109/TIT.1962.1057692 10.1155/2012/928161 10.1142/S021969131250035X 10.1016/j.biosystemseng.2013.04.007 10.1109/T-C.1969.222678 10.1109/TPAMI.2009.85 10.1136/bmj.308.6943.1552 10.1109/TPAMI.2007.41 10.1109/TPAMI.2010.224 |
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| Keywords | Binary image Fourier transform Harmonic analysis TSO invariance Pattern recognition Affine invariance |
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| SubjectTerms | Affine invariance Binary image digital images Digital imaging Fourier analysis Fourier transform Harmonic analysis Invariance Invariants leaves Neural networks Pattern recognition support vector machines Translations trees TSO invariance Two dimensional |
| Title | Leaf classification from binary image via artificial intelligence |
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