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General Road Detection From a Single Image
Hui Kong, Member, IEEE, Jean-Yves Audibert, and Jean
Ponce, Fellow, IEEE
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Outline Introduction
Confidence-Rated Texture Orientation Estimation
LOCALLY ADAPTIVE SOFT-VOTING
ROAD SEGMENTATION
EXPERIMENTAL RESULTS
CONCLUSION
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Introduction Image-based road detection
algorithms
• Most of the early systems focused on following the well-paved road
• This paper proposing a novel framework for segmenting the road area based upon the estimation of the vanishing point associated with the main (straight) part of the road.
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Introduction
Given a road image, can the computer roughly determine where the road is?
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Outline Introduction
Confidence-Rated Texture Orientation Estimation
LOCALLY ADAPTIVE SOFT-VOTING
ROAD SEGMENTATION
EXPERIMENTAL RESULTS
CONCLUSION
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Confidence-Rated Texture Orientation Estimation
Gabor filterFor an orientation and a scale (radial frequency)
Where ,
We consider 5 scales( , , k=0,1, 2, 3, 4) on a geometric grid and 36 orientations (180 divided by 5).
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Confidence-Rated Texture Orientation Estimation
Let I(x,y) be the gray level value of an image at (x,y). The convolution of image I and a Gabor kernel of scale and orientation is defined as follows:
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Confidence-Rated Texture Orientation Estimation
To best characterize the local texture properties, we compute the square normof this “complex response” of the Gabor filter for each 36 evenly spaced Gabor filter orientations
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Confidence-Rated Texture Orientation Estimation
The response image for an orientation is then defined as the average of the responses at the different scales
The texture orientation is chosen as the filter orientation which gives the maximum average complex response at that location
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Confidence-Rated Texture Orientation Estimation
From the convolution theorem applied to
we have hence
where and denote the Fourier and inverse Fourier transform,respectively.
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Confidence-Rated Texture Orientation Estimation
The use of the fast Fourier transform
With and allows fast computation of the response image.
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Confidence-Rated Texture Orientation Estimation
Confidence level