Comportamento de linhagens e cultivares de caupi (Vigna unguiculata (L. Walp.) de porte ereto, no estado de Rondonia.

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Competição de linhas melhoradas de feijão (Phaseolus vulgaris L.).

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Comportamento de linhagens de feijão (Phaseolus vulgaris L.) no municipio de Ouro Preto D'Oeste em Rondônia.

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Adaptabilidade de linhagens e cultivares de feijao (Phaseolus vulgaris L.) em Rondonia e resistencia a "mela" (Thanatephorus cucumeris (Frank.) Donk).

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Background modeling for video sequences by stacked denoising autoencoders

Nowadays, the analysis and extraction of relevant information in visual data flows is of paramount importance. These images sequences can last for hours, which implies that the model must adapt to all kinds of circumstances so that the performance of the system does not decay over time. In this paper we propose a methodology for background modeling and foreground detection, whose main characteristic is its robustness against stationary noise. Thus, stacked denoising autoencoders are applied to generate a set of robust characteristics for each region or patch of the image, which will be the input of a probabilistic model to determine if that region is background or foreground. The evaluation of a set of heterogeneous sequences results in that, although our proposal is similar to the classical methods existing in the literature, the inclusion of noise in these sequences causes drastic performance drops in the competing methods, while in our case the performance stays or falls slightly.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Competicao de linhas melhoradas de feijao (Phaseolus vulgaris L.) em Rondonia.

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Background modeling by shifted tilings of stacked denoising autoencoders

The effective processing of visual data without interruption is currently of supreme importance. For that purpose, the analysis system must adapt to events that may affect the data quality and maintain its performance level over time. A methodology for background modeling and foreground detection, whose main characteristic is its robustness against stationary noise, is presented in the paper. The system is based on a stacked denoising autoencoder which extracts a set of significant features for each patch of several shifted tilings of the video frame. A probabilistic model for each patch is learned. The distinct patches which include a particular pixel are considered for that pixel classification. The experiments show that classical methods existing in the literature experience drastic performance drops when noise is present in the video sequences, whereas the proposed one seems to be slightly affected. This fact corroborates the idea of robustness of our proposal, in addition to its usefulness for the processing and analysis of continuous data during uninterrupted periods of time.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Competicao de cultivares e linhagens de caupi (Vigna unguiculata (l.) Walp.) em Rondonia.

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