3 resultados para OC-SVM

em Universidad Politécnica de Madrid


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In this work we propose an image acquisition and processing methodology (framework) developed for performance in-field grapes and leaves detection and quantification, based on a six step methodology: 1) image segmentation through Fuzzy C-Means with Gustafson Kessel (FCM-GK) clustering; 2) obtaining of FCM-GK outputs (centroids) for acting as seeding for K-Means clustering; 3) Identification of the clusters generated by K-Means using a Support Vector Machine (SVM) classifier. 4) Performance of morphological operations over the grapes and leaves clusters in order to fill holes and to eliminate small pixels clusters; 5)Creation of a mosaic image by Scale-Invariant Feature Transform (SIFT) in order to avoid overlapping between images; 6) Calculation of the areas of leaves and grapes and finding of the centroids in the grape bunches. Image data are collected using a colour camera fixed to a mobile platform. This platform was developed to give a stabilized surface to guarantee that the images were acquired parallel to de vineyard rows. In this way, the platform avoids the distortion of the images that lead to poor estimation of the areas. Our preliminary results are promissory, although they still have shown that it is necessary to implement a camera stabilization system to avoid undesired camera movements, and also a parallel processing procedure in order to speed up the mosaicking process.

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For solar cells dominated by radiative recombination, the performance can be significantly enhanced by improving the internal optics. Internally radiated photons can be directly emitted from the cell, but if confined by good internal reflectors at the front and back of the cell they can also be re-absorbed with a significant probability. This so-called photon recycling leads to an increase in the equilibrium minority carrier concentration and therefore the open-circuit voltage, Voc. In multijunction cells, the internal luminescence from a particular junction can also be coupled into a lower bandgap junction where it generates photocurrent in addition to the externally generated photocurrent, and affects the overall performance of the tandem. We demonstrate and discuss the implications of a detailed model that we have developed for real, non-idealized solar cells that calculates the external luminescent efficiency, accounting for wavelength-dependent optical properties in each layer, parasitic optical and electrical losses, multiple reflections within the cell and isotropic internal emission. The calculation leads to Voc, and we show data on high quality GaAs cells that agree with the trends in the model as the optics are systematically varied. For multijunction cells the calculation also leads to the luminescent coupling efficiency, and we show data on GaInP/GaAs tandems where the trends also agree as the coupling is systematically varied. In both cases, the effects of the optics are most prominent in cells with good material quality. The model is applicable to any solar cell for which the optical properties of each layer are well-characterized, and can be used to explore a wide phase space of design for single junction and multijunction solar cells.

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This paper presents a novel background modeling system that uses a spatial grid of Support Vector Machines classifiers for segmenting moving objects, which is a key step in many video-based consumer applications. The system is able to adapt to a large range of dynamic background situations since no parametric model or statistical distribution are assumed. This is achieved by using a different classifier per image region that learns the specific appearance of that scene region and its variations (illumination changes, dynamic backgrounds, etc.). The proposed system has been tested with a recent public database, outperforming other state-of-the-art algorithms.