The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Utilizing venation features for efficient leaf image retrieval
Journal of Systems and Software
Plant species identification using Elliptic Fourier leaf shape analysis
Computers and Electronics in Agriculture
Venation pattern analysis of leaf images
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
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In this paper, a procedure for segmenting and classifying scanned legume leaves based only on the analysis of their veins is proposed (leaf shape, size, texture and color are discarded). Three legume species are studied, namely soybean, red and white beans. The leaf images are acquired using a standard scanner. The segmentation is performed using the unconstrained hit-or-miss transform and adaptive thresholding. Several morphological features are computed on the segmented venation, and classified using four alternative classifiers, namely support vector machines (linear and Gaussian kernels), penalized discriminant analysis and random forests. The performance is compared to the one obtained with cleared leaves images, which require a more expensive, time consuming and delicate procedure of acquisition. The results are encouraging, showing that the proposed approach is an effective and more economic alternative solution which outperforms the manual expert's recognition.