Unstructured road detection based on fuzzy clustering arithmetic

The unstructured road detection plays a key role in an autonomous vehicle navigation system. However, the unstructured road images often contain shadows and are easily affected by ambient light, resulting to an inaccuracy with road detection. A robust road detection technique is required. In this paper, we adopted an improved fuzzy c-means(FCM) clustering algorithm to address these issues. The new technique considered the neighborhood impact factor when calculating distances between the cluster center and a pixel. Our experimental results show that the improved FCM have better outcomes.

[1]  Jing Li Wang,et al.  Color image segmentation: advances and prospects , 2001, Pattern Recognit..

[2]  Qingquan Li,et al.  Block-constraint line scanning method for lane detection , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[3]  Friedrich M. Wahl,et al.  Vector-entropy optimization-based neural-network approach to image reconstruction from projections , 1997, IEEE Trans. Neural Networks.

[4]  Jian Wang,et al.  Unstructured road detection using hybrid features , 2009, 2009 International Conference on Machine Learning and Cybernetics.

[5]  Lipo Wang,et al.  On competitive learning , 1997, IEEE Trans. Neural Networks.

[6]  Chunru Wan,et al.  Unsupervised gene selection via spectral biclustering , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[7]  V.S. Tseng,et al.  Efficiently mining gene expression data via a novel parameterless clustering method , 2005, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[8]  Daewon Lee,et al.  Dynamic Characterization of Cluster Structures for Robust and Inductive Support Vector Clustering , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Shengyan Zhou,et al.  Self-supervised learning method for unstructured road detection using Fuzzy Support Vector Machines , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  W. A. Sandham,et al.  Fuzzy clustering based applications to medical image segmentation , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[11]  Shang Keke,et al.  Study on skin color image segmentation used by Fuzzy-c-means arithmetic , 2010, 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery.

[12]  Yunpeng Zhao,et al.  Unstructured Road Edge Detection and Initial Positioning Approach based on Monocular Vision , 2012 .

[13]  Jian Li,et al.  Robust Unstructured Road Detection: The Importance of Contextual Information , 2013 .

[14]  Max Mignotte.  A de-texturing and spatially constrained K-means approach for image segmentation , 2011, Pattern Recognit. Lett..

[15]  Peng Zhou,et al.  Study on skin color image segmentation used by Fuzzy-c-means arithmetic , 2010, FSKD.

[16]  Lipo Wang,et al.  Data Mining With Computational Intelligence , 2006, IEEE Transactions on Neural Networks.

[17]  Zhenmin Tang,et al.  The Algorithm for Unstructured Road to Abstract Road Surface based on Color Feature , 2012, ITCS 2012.

[18]  Charles E. Thorpe,et al.  Color Vision For Road Following , 1989, Other Conferences.

[19]  Daewon Lee,et al.  An improved cluster labeling method for support vector clustering , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.