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

Published

December 1, 2001

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

Pub Type

Conferences

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 November 3, 2025)