In this article, we present an automatic leaves image classification system for sunflower crops using neural networks, which could be used in selective herbicide applications. The system is comprised of four main stages. First, a segmentation based on rgb color space is performed. Second, many different features are detected and then extracted from the segmented image. Third, the most discriminable set of features are selected. Finally, the Generalized Softmax Perceptron (GSP) neural network architecture is used in conjunction with the recently proposed Posterior Probability Model Selection (PPMS) algorithm for complexity selection in order to select the leaves in an image and then classify them either as sunflower or non-sunflower. The experimental results show that the proposed system achieves a high level of accuracy with only five selected discriminative features obtaining an average Correct Classification Rate of 85\% and an area under the receiver operation curve over 90\%, for the test set. {\^A}{\textcopyright} 2011 Elsevier B.V.

}, keywords = {accuracy assessment, agricultural technology, algorithm, artificial neural network, automation, Classification rates, Computer vision, Crops, dicotyledon, Discriminative features, experimental study, Generalized softmax perceptron, Helianthus, herbicide, Herbicide application, Herbicides, Image classification, Image classification systems, Leaf classification, Learning machines, Model selection, Network architecture, Neural networks, Posterior probability, RGB color space, segmentation, Segmented images, Sunflower, Test sets}, issn = {01681699}, doi = {10.1016/j.compag.2011.05.007}, url = {https://www.sciencedirect.com/science/article/pii/S0168169911001220}, author = {J I Arribas and G V Sanchez-Ferrero and G Ruiz-Ruiz and Jaime Gomez-Gil} } @article {423, title = {Automatic bayesian classification of healthy controls, bipolar disorder, and schizophrenia using intrinsic connectivity maps from fMRI data}, journal = {IEEE Transactions on Biomedical Engineering}, volume = {57}, year = {2010}, pages = {2850-2860}, abstract = {We present a method for supervised, automatic, and reliable classification of healthy controls, patients with bipolar disorder, and patients with schizophrenia using brain imaging data. The method uses four supervised classification learning machines trained with a stochastic gradient learning rule based on the minimization of KullbackLeibler divergence and an optimal model complexity search through posterior probability estimation. Prior to classification, given the high dimensionality of functional MRI (fMRI) data, a dimension reduction stage comprising two steps is performed: first, a one-sample univariate t-test mean-difference Tscore approach is used to reduce the number of significant discriminative functional activated voxels, and then singular value decomposition is performed to further reduce the dimension of the input patterns to a number comparable to the limited number of subjects available for each of the three classes. Experimental results using functional brain imaging (fMRI) data include receiver operation characteristic curves for the three-way classifier with area under curve values around 0.82, 0.89, and 0.90 for healthy control versus nonhealthy, bipolar disorder versus nonbipolar, and schizophrenia patients versus nonschizophrenia binary problems, respectively. The average three-way correct classification rate (CCR) is in the range of 70\%-72\%, for the test set, remaining close to the estimated Bayesian optimal CCR theoretical upper bound of about 80\%, estimated from the one nearest-neighbor classifier over the same data. {\^A}{\textcopyright} 2010 IEEE.

}, keywords = {Algorithms, area under the curve, article, Artificial Intelligence, Bayesian learning, Bayesian networks, Bayes Theorem, Biological, bipolar disorder, Brain, Case-Control Studies, classification, Classifiers, Computer-Assisted, controlled study, Diseases, functional magnetic resonance imaging, Functional MRI (fMRI), human, Humans, Learning machines, Learning systems, machine learning, Magnetic Resonance Imaging, major clinical study, Models, neuroimaging, Operation characteristic, Optimization, patient coding, receiver operating characteristic, reliability, Reproducibility of Results, ROC Curve, schizophrenia, Signal Processing, Singular value decomposition, Statistical tests, Stochastic models, Student t test}, issn = {00189294}, doi = {10.1109/TBME.2010.2080679}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-78649311169\&partnerID=40\&md5=d3b90f1a3ee4ef209d131ef986e142db}, author = {J I Arribas and V D Calhoun and T Adali} }