3D watertight mesh generation with uncertainties from ubiquitous data

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Abstract

In this paper, we propose a generic framework for watertight mesh generation with uncertainties that provides a confidence measure on each reconstructed mesh triangle. Its input is a set of vision-based or Lidar-based 3D measurements which are converted to a set of mass functions that characterize the level of ...

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Second order MRF with QPBO Optimization

Second order Markov random field is dedicated to models scenes composed by plan, which is good for urban scenes for example. Maximizing a MRF energy can be formulated as minimizing a function. It is known that it is not possible to find which set of label minimize the energy related ...

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Extending α-expansion to a larger set of regularization functions

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Abstract

Many problems of image processing lead to the minimization of an energy, which is a function of one or several given images, with respect to a binary or multi-label image. When this energy is made of unary data terms and of pairwise regularization terms, and when the pairwise regularization ...

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Graduate non convexity approach for robust fitting

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This example shows the estimation of the mean with the graduate non convexity approach using a newton optimization at each step for locally minimizing the energy. Green points are a realization of a normal distribution (vertical line shows the mean), red crosses are outliers. The blue line is the normalized ...

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Quadratic pseudo boolean optimization introduction

Graph-based algorithms have been used for a decade in many applications for optimization. It produces very good results. First used empirically in image processing, it has been shown that graph-based approach algorithms in image processing are a reduction of optimizing pseudo Boolean function in binary case. In pseudo Boolean optimization ...

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Daytime Fog Detection and Density Estimation with Entropy Minimisation

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Abstract

The fog disturbs the proper image processing of many outdoor observation tools. For instance, fog reduces the obstacle v isibility in vehicle driving applications. Usually, the estimation of t he amount of fog in the scene image allows to greatly improve t he image processing, and thus to better ...

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Markov Random Field Model for Single Image Defogging

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Abstract

Fog reduces contrast and thus the visibility of vehicles and obstacles for drivers. Each year, this causes traffic accidents. Fog is caused by a high concentration of very fine water droplets in the air. When light hits these droplets, it is scattered and this results in a dense white ...

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