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Efficient On-line Computation of Visibility Graphs
A visibility algorithm maps time series into complex networks following ...
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Evaluating time series forecasting models: An empirical study on performance estimation methods
Performance estimation aims at estimating the loss that a predictive mod...
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Information theoretic results for stationary time series and the Gaussian-generalized von Mises time series
This chapter presents some novel information theoretic results for the a...
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On the Duality Between Retinex and Image Dehazing
Image dehazing deals with the removal of undesired loss of visibility in...
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Horizon Visibility Graphs and Time Series Merge Trees are Dual
In this paper we introduce the horizon visibility graph, a simple extens...
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Variations of images to increase their visibility
The calculus of variations applied to the image processing requires some...
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What happens for a ToF LiDAR in fog?
This article focuses on analyzing the performance of a typical time-of-f...
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Expressway visibility estimation based on image entropy and piecewise stationary time series analysis
Vision-based methods for visibility estimation can play a critical role in reducing traffic accidents caused by fog and haze. To overcome the disadvantages of current visibility estimation methods, we present a novel data-driven approach based on Gaussian image entropy and piecewise stationary time series analysis (SPEV). This is the first time that Gaussian image entropy is used for estimating atmospheric visibility. To lessen the impact of landscape and sunshine illuminance on visibility estimation, we used region of interest (ROI) analysis and took into account relative ratios of image entropy, to improve estimation accuracy. We assume fog and haze cause blurred images and that fog and haze can be considered as a piecewise stationary signal. We used piecewise stationary time series analysis to construct the piecewise causal relationship between image entropy and visibility. To obtain a real-world visibility measure during fog and haze, a subjective assessment was established through a study with 36 subjects who performed visibility observations. Finally, a total of two million videos were used for training the SPEV model and validate its effectiveness. The videos were collected from the constantly foggy and hazy Tongqi expressway in Jiangsu, China. The contrast model of visibility estimation was used for algorithm performance comparison, and the validation results of the SPEV model were encouraging as 99.14 errors were less than 10
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