We propose using a deep convolutional neural network (CNN) for the problem of plant identification from leaf vein patterns. In particular we consider classifying three different legume species: white bean, red bean and soybean. The introduction of a CNN avoids using handcrafted feature extractors as in state of the art pipeline. Furthermore, this deep learning approach significantly improves the accuracy of the referred pipeline. We also show that this accuracy is reached by increasing the depth of the model. Finally, by analyzing the resulting models with a simple visualization technique, we are able to discover which vein patterns are relevant.
In this work we propose an objective function to guide the search for a state space reconstruction of a dynamical system from a time series of measurements. These statistics can be evaluated on any reconstructed attractor, thereby allowing a direct comparison among different approaches: (uniform or nonuniform) delay vectors, PCA, Legendre coordinates, etc. It can also be used to select the most appropriate parameters of a reconstruction strategy. In the case of delay coordinates this translates into finding the optimal delay time and embedding dimension from the absolute minimum of the advocated cost function. Its definition is based on theoretical arguments on noise amplification, the complexity of the reconstructed attractor, and a direct measure of local stretch which constitutes an irrelevance measure. The proposed method is demonstrated on synthetic and experimental time series.
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