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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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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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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Vision Enhancement in Homogeneous and Heterogeneous Fog

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Abstract

One source of accidents when driving a vehicle is the presence of fog. Fog fades the colors and reduces the contrasts in the scene with respect to their distances from the driver. Various camera-based Advanced Driver Assistance Systems (ADAS) can be improved if efficient algorithms are designed for visibility ...

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