Visual evaluation of color stability after accelerated aging of pigmented and nonpigmented silicones to be used in facial prostheses

Objectives: The objective of this study was to evaluate by a visual method of comparison the color stability of nonpigmented and pigmented facial silicones after accelerated aging. Materials and Methods: Two kinds of silicones were used in this study; one specifically formulated for facial prostheses and the other an acetic silicone for industrial use. Twenty-four trial bodies were made for each silicone. These were divided into colorless and intrinsically pigmented groups: ceramic, make-up, and iron oxide. The groups were submitted to accelerated aging for nonmetallic materials. An initial reading and subsequent readings were made at 163, 351, 692, and 1000 hours using a visual method of comparison. The values were annotated in a spreadsheet by two observers, according to scores elaborated for this study. Results: All groups presented color stability in the visual method. According to the results obtained and analyzed in this study, we can conclude that both silicones, Silastic 732 RTV and Silastic MDX 4-4210, behaved similarly, they can therefore be indicated for use in maxillofacial prosthesis. The time factor of aging influenced negatively, independently of the pigmentation, or lack of it, and of silicones and no group had visually noticeable alterations in any of the accelerated aging time, independently of the addition or not of pigments.

Mapas de guia de rota em automóvel para motoristas com deficiência na visão de cores

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Measuring and analyzing color and texture information in anatomical leaf cross sections: An approach using computer vision to aid plant species identification

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

JSEG-based image segmentation in computer vision for agricultural mobile robot navigation

This project aims to apply image processing techniques in computer vision featuring an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained.

ANN statistical image recognition method for computer vision in agricultural mobile robot navigation

The main application area in this project, is to deploy image processing and segmentation techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. Thereby, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for image recognition. Hence, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave computational platforms, along with the application of customized Back-propagation Multilayer Perceptron (MLP) algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of segmented images in which reasonably accurate results were obtained. © 2010 IEEE.

A computer vision approach to quantify leaf anatomical plasticity: A case study on gochnatia polymorpha (less.) cabrera

Inferences about leaf anatomical characteristics had largely been made by manually measuring diverse leaf regions, such as cuticle, epidermis and parenchyma to evaluate differences caused by environmental variables. Here we tested an approach for data acquisition and analysis in ecological quantitative leaf anatomy studies based on computer vision and pattern recognition methods. A case study was conducted on Gochnatia polymorpha (Less.) Cabrera (Asteraceae), a Neotropical savanna tree species that has high phenotypic plasticity. We obtained digital images of cross-sections of its leaves developed under different light conditions (sun vs. shade), different seasons (dry vs. wet) and in different soil types (oxysoil vs. hydromorphic soil), and analyzed several visual attributes, such as color, texture and tissues thickness in a perpendicular plane from microscopic images. The experimental results demonstrated that computational analysis is capable of distinguishing anatomical alterations in microscope images obtained from individuals growing in different environmental conditions. The methods presented here offer an alternative way to determine leaf anatomical differences. © 2013 Elsevier B.V.