Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Image Representation Using 2D Gabor Wavelets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Manhattan world: orientation and outlier detection by Bayesian inference
Neural Computation
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Team Cornell's Skynet: Robust perception and planning in an urban environment
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part I
Autonomous driving in urban environments: Boss and the Urban Challenge
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part I
Junior: The Stanford entry in the Urban Challenge
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part II
A perception-driven autonomous urban vehicle
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part III
An Improved Algorithm for TV-L1 Optical Flow
Statistical and Geometrical Approaches to Visual Motion Analysis
General road detection from a single image
IEEE Transactions on Image Processing
Total variation minimization and a class of binary MRF models
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation
IEEE Transactions on Intelligent Transportation Systems
Off-Road Path and Obstacle Detection Using Decision Networks and Stereo Vision
IEEE Transactions on Intelligent Transportation Systems
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We propose a new method for robust road detection under noise and illumination varying conditions. Original input image is first divided into smooth and detailed component through structure-texture decomposition, where we verify the texture image is robust to various complicated road conditions. The texture image is then be used to compute each pixel's dominant orientation through Gabor wavelet analysis, followed by generating the vanishing point via grouping voters, which has an orientation confidence larger than a fixed threshold, in corresponding voting region through soft voting. Finally the road borders are constructed by feature inconsistency maximization criterion. Experiments on various road, weather, noise and lighting conditions are justified the accuracy and robust of our method. Furthermore, we analyze the applicability of texture based vanishing point method and conclude the main factors that degenerate the performance of this class method.