Edge Detection with Embedded Confidence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Mean Shift Analysis and Applications
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Bi-Layer Segmentation of Binocular Stereo Video
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Object tracking using CamShift algorithm and multiple quantized feature spaces
VIP '05 Proceedings of the Pan-Sydney area workshop on Visual information processing
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Two-dimension road following is one of the crucial tasks of vision navigation. For the reasons of environment complexity and the discrepancy between motion images, the robust outdoor road following for two-dimension image sequence is still a challenging task. This paper proposes a novel road following method, which firstly uses the Mean Shift algorithm with embedded edge confidence to partition the images into homogenous regions with precise boundary. Then, according to the color statistic information of the road/non-road model obtained from previous frames, the Graph Cuts (GC) algorithm is used to achieve the final binary images and update the road/non-road model simultaneously. This algorithm combines the advantages of Graph Cuts algorithm and Mean Shift algorithm, and effectively solves some difficult problems of conventional methods, such as the adaptive selection of road model under complex environments, and the choice of effective criteria for the region merging. Experiment results indicate our method possesses excellent performance under complicated environment, and meets the requirements of fast computing.