Dataset Information

Increasing the Complexity of the Illumination May Reduce Gloss Constancy.

ABSTRACT: We examined in which way gradual changes in the geometric structure of the illumination affect the perceived glossiness of a surface. The test stimuli were computer-generated three-dimensional scenes with a single test object that was illuminated by three point light sources, whose relative positions in space were systematically varied. In the first experiment, the subjects were asked to adjust the microscale smoothness of a match object illuminated by a single light source such that it has the same perceived glossiness as the test stimulus. We found that small changes in the structure of the light field can induce dramatic changes in perceived glossiness and that this effect is modulated by the microscale smoothness of the test object. The results of a second experiment indicate that the degree of overlap of nearby highlights plays a major role in this effect: Whenever the degree of overlap in a group of highlights is so large that they perceptually merge into a single highlight, the glossiness of the surface is systematically underestimated. In addition, we examined the predictability of the smoothness settings by a linear model that is based on a set of four different global image statistics.

SUBMITTER: Wendt G

PROVIDER: S-EPMC5726623 | biostudies-other | 2017 Nov-Dec

REPOSITORIES: biostudies-other

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Increasing the Complexity of the Illumination May Reduce Gloss Constancy.

Wendt Gunnar G Faul Franz F

i-Perception 20171101 6

We examined in which way gradual changes in the geometric structure of the illumination affect the perceived glossiness of a surface. The test stimuli were computer-generated three-dimensional scenes with a single test object that was illuminated by three point light sources, whose relative positions in space were systematically varied. In the first experiment, the subjects were asked to adjust the microscale smoothness of a match object illuminated by a single light source such that it has the ...[more]

PMID: 29250308

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Project description:BackgroundIn microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes.ResultsIn this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes.ConclusionsWe show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events.

Dataset Information

Increasing the Complexity of the Illumination May Reduce Gloss Constancy.

Publications

Increasing the Complexity of the Illumination May Reduce Gloss Constancy.

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