Paper Info
Title
A robust road segmentation method based on graph cut with learnable neighboring link weights
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.
Year
DOI
Venue
2014
10.1109/ITSC.2014.6957929
ITSC
Keywords
Field
DocType
computer vision,driver information systems,graph theory,image segmentation,learning (artificial intelligence),adas,mlp,advanced driver assistance systems,environment interference,gentle adaboost algorithm,graph-cut based method,graph-cut based road segmentation algorithms,learnable neighboring link weights,monocular vision approach,multilayer perceptrons,n-links,neighboring links,road region detection,road surfaces,robust road segmentation method,s-t graph,monocular vision,algorithms
Cut,Monocular vision,Computer vision,Scale-space segmentation,Pattern recognition,Segmentation,Computer science,Advanced driver assistance systems,Robustness (computer science),Image segmentation,Artificial intelligence,Perceptron
Conference
Citations 
PageRank 
References 
3
0.39
7
Authors
4
Name
Order
Citations
PageRank
Jun Yuan124423.10
Shuming Tang2717.46
Fei Wang324151.35
Hong Zhang4196.36