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Laser Range-, Color-, and Texture-Based Classifiers for Segmenting Marginal Roads

Published

Author(s)

C E. Rasmussen

Abstract

We describe preliminary results on combining depth information from a laser range-finder and color and texture image cues to train classifiers to segment ill-structured dirt, gravel, and asphalt roads as input to an autonomous road following system. A large number of registered laser and camera images were captured at frame-rate on a variety of rural roads, allowing laser features such as 3-D height and smoothness to be correlated with image features such color histograms and Gabor filter responses. A small set of road models were generated by training separate neural networks on labeled feature vectors clustered by road type.'' By first classifying the type of a novel road image, an appropriate second-stage classifier was selected to segment individual pixels, achieving a high degree of accuracy on arbitrary images from the dataset.
Proceedings Title
Proceedings of the Conference on Computer Vision & Pattern Recognition Technical Sketches
Conference Dates
December 1, 2001
Conference Title
Conference on Computer Vision & Pattern Recognition Technical Sketches

Keywords

classification, color histogram, Gabor filter, k-means clustering, laser range-finder, neural network, road following, road segmentation

Citation

Rasmussen, C. (2001), Laser Range-, Color-, and Texture-Based Classifiers for Segmenting Marginal Roads, Proceedings of the Conference on Computer Vision & Pattern Recognition Technical Sketches (Accessed April 18, 2024)
Created December 1, 2001, Updated February 17, 2017