Recursive 3-D Road and Relative Ego-State Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Determination of road directions using feedback neural nets
Signal Processing - Intelligent systems for signal and image understanding
Neural Network Perception for Mobile Robot Guidance
Neural Network Perception for Mobile Robot Guidance
GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection
IEEE Transactions on Image Processing
An extended hyperbola model for road tracking for video-based personal navigation
Knowledge-Based Systems
Lane following and lane departure using a linear-parabolic model
Image and Vision Computing
A robust lane detection and verification method for intelligent vehicles
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
A DSP-based lane departure warning system
MMACTEE'06 Proceedings of the 8th WSEAS international conference on Mathematical methods and computational techniques in electrical engineering
A novel system for robust lane detection and tracking
Signal Processing
A robust lane detection approach based on MAP estimate and particle swarm optimization
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part II
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This paper proposes a novel image-processing algorithm to recognize the lane-curve of a structured road. The proposed algorithm uses an lane-curve function (LCF) obtained by the transformation of the defined parabolic function on the world coordinates into the image coordinates. Unlike other existing methods, this algorithm needs no transformation of the image pixels into the world coordinates. The main idea of the algorithm is to search for the best-described LCF of the lane-curve on an image. In advance, several LCFs are assumed by changing the curvature. Then, the comparison is carried out between the slope of an assumed LCF and the phase angle of the edge pixels in the lane region of interest constructed by the assumed LCF. The LCF with the minimum difference in the comparison becomes the true LCF corresponding to the lane-curve. The proposed method is proved to be efficient through experiments for the various kinds of images, providing the reliable curve direction and the valid curvature compared to the actual road.