A Contrast/Filling-In Model of 3-D Lightness Perception

Wallach's ratio hypothesis states that local luminance ratios clr!termine lightness perception under variable illumination. While local luminance ratios successfully discount gradual variations in illumination (illumination constancy or Type I constancy), they fail to explain lightness constancy in general. Some examples of failures of the ratio hypothesis include effects suggesting the coplanar ratio hypothesis (Gilchrist 1977), "assimilation" effects, and configural effects such as the Benary cross, and White's illusion. The present article extends the Boundary Contour System/Feature Contour System (BCS/FCS) approach to provide an explanation of these effects in terms of a neural model of 3-D lightness perception. Lightness constancy of objects in front of different backgrounds (background constancy or Type II constancy) is used to provide functional constraints to the theory and suggest a contrast negation hypothesis which states that ratio measures between coplanar regions are given more weight in the determination of lightness. Simulations of the model applied to several stimuli including Benary cross and White's illusion show that contrast negation mechanisms modulate illumination constancy mechanisms to extend the explanatory power of the model. The model is also used to devise new stimuli that test theoretical predictions

Avaliação da técnica de registro óptico para estudo do crescimento de sementes durante a fase de embebição.

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Representação de células vegetais em duas dimensões.

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Redes neurais para predição de variáveis climáticas relevantes para a agricultura1.

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Reformulação das deduções de Lockhart levando em conta a turgescência e a sucção como fatores de crescimento.

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Estendendo o modelo de Lockhart de crescimento vegetal levando em conta a sucção da parede celular.

O modelo proposto por Lockhart em 1965 para descrever o crescimento de uma célula vegetal foi ampliado para levar em conta a pressão hidrostática do apoplasma.bitstream/CNPDIA/8895/1/BP02_96.pd

Rational first integrals of the Liénard equations : The solution to the Poincaré problem for the Liénard equations

Poincaré in 1891 asked about the necessary and sufficient conditions in order to characterize when a polynomial differential system in the plane has a rational first integral. Here we solve this question for the class of Liénard differential equations ẍ + f (x)ẋ + x = 0, being f (x) a polynomial of arbitrary degree. As far as we know it is the first time that all rational first integrals of a relevant class of polynomial differential equations of arbitrary degree has been classified

A Learning Framework for Morphological Operators using Counter-Harmonic Mean

We present a novel framework for learning morphological operators using counter-harmonic mean. It combines concepts from morphology and convolutional neural networks. A thorough experimental validation analyzes basic morphological operators dilation and erosion, opening and closing, as well as the much more complex top-hat transform, for which we report a real-world application from the steel industry. Using online learning and stochastic gradient descent, our system learns both the structuring element and the composition of operators. It scales well to large datasets and online settings.Comment: Submitted to ISMM'1