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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jun Yuan Shuming Tang Fei Wang Hong Zhang |
| Copyright Year | 2014 |
| Description | Author affiliation: Sch. of Inf. Eng., Minzu Univ. of China, Beijing, China (Hong Zhang) || State Key Lab. of Manage. & Control for Complex Syst., Inst. of Autom., Beijing, China (Jun Yuan) || High-Tech Innovation Eng. Center, Inst. of Autom., Beijing, China (Shuming Tang) || Inst. of Artificial Intell. & Robot., Xi'an Jiaotong Univ., Xi'an, China (Fei Wang) |
| Abstract | Road region detection is a crucial functionality for road following in advanced driver assistance systems (ADAS). To address the problem of environment interference in road segmentation through a monocular vision approach, a novel graph-cut based method is proposed in this paper. The novelty of this proposal is that weights of neighboring links (n-links) in a s-t graph are estimated by Multilayer Perceptrons (MLPs) rather than calculating by the neighboring contrast simply in previous graph-cut based methods. Estimating n-link weights by MLPs reinforces the ability of graph-cut based road segmentation algorithms to tolerate the complex and changeable appearance of road surfaces. Additionally, the Gentle AdaBoost algorithm is integrated into the graph-cut framework to estimate the terminal link (t-link) weights in the s-t graph. Experiments are conducted to show the robustness and efficiency of the proposed method. |
| Starting Page | 1644 |
| Ending Page | 1649 |
| File Size | 1827361 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479960781 |
| DOI | 10.1109/ITSC.2014.6957929 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-10-08 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Roads Image segmentation Robustness Lighting Image edge detection Estimation Image color analysis |
| Content Type | Text |
| Resource Type | Article |
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