Lighting conditions and optical filters effects on visual performance of speleologists exposed to cave environments

The aim of this study is to evaluate lighting conditions and speleologists’ visual performance using optical filters when exposed to the lighting conditions of cave environments. A crosssectional study was conducted. Twenty-three speleologists were submitted to an evaluation of visual function in a clinical lab. An examination of visual acuity, contrast sensitivity, stereoacuity and flashlight illuminance levels was also performed in 16 of the 23 speleologists at two caves deprived of natural lightning. Two organic filters (450 nm and 550 nm) were used to compare visual function with and without filters. The mean age of the speleologists was 40.65 (± 10.93) years. We detected 26.1% participants with visual impairment of which refractive error (17.4%) was the major cause. In the cave environment the majority of the speleologists used a head flashlight with a mean illuminance of 451.0 ± 305.7 lux. Binocular visual acuity (BVA) was -0.05 ± 0.15 LogMAR (20/18). BVA for distance without filter was not statistically different from BVA with 550 nm or 450 nm filters (p = 0.093). Significant improved contrast sensitivity was observed with 450 nm filters for 6 cpd (p = 0.034) and 18 cpd (p = 0.026) spatial frequencies. There were no signs and symptoms of visual pathologies related to cave exposure. Illuminance levels were adequate to the majority of the activities performed. The enhancement in contrast sensitivity with filters could potentially improve tasks related with the activities performed in the cave.

Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approaches

In the last decade, local image features have been widely used in robot visual localization. In order to assess image similarity, a strategy exploiting these features compares raw descriptors extracted from the current image with those in the models of places. This paper addresses the ensuing step in this process, where a combining function must be used to aggregate results and assign each place a score. Casting the problem in the multiple classifier systems framework, in this paper we compare several candidate combiners with respect to their performance in the visual localization task. For this evaluation, we selected the most popular methods in the class of non-trained combiners, namely the sum rule and product rule. A deeper insight into the potential of these combiners is provided through a discriminativity analysis involving the algebraic rules and two extensions of these methods: the threshold, as well as the weighted modifications. In addition, a voting method, previously used in robot visual localization, is assessed. Furthermore, we address the process of constructing a model of the environment by describing how the model granularity impacts upon performance. All combiners are tested on a visual localization task, carried out on a public dataset. It is experimentally demonstrated that the sum rule extensions globally achieve the best performance, confirming the general agreement on the robustness of this rule in other classification problems. The voting method, whilst competitive with the product rule in its standard form, is shown to be outperformed by its modified versions.

Identidade visual corporativa enquanto recurso estratégico na competitividade entre organizações em Portugal

Dissertação apresentada à Escola Superior de Comunicação Social como parte dos requisitos para obtenção de grau de mestre em Publicidade e Marketing.