ICIP 2006, Atlanta, GA
 

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Paper Detail

Paper:WP-P1.9
Session:Visual Object/Event Detection, Segmentation, and Classification
Time:Wednesday, October 11, 14:20 - 17:00
Presentation: Poster
Topic: Image & Video Segmentation: Statistical methods
Title: DETECTION OF DRIVABLE CORRIDORS FOR OFF-ROAD AUTONOMOUS NAVIGATION
Authors: Ara Nefian; Intel 
 Gary Bradski; Intel 
Abstract: This paper describes a hierarchical Bayesian network used for segmenting desert images and detecting off road drivable corridors for autonomous navigation. Unlike the embedded hidden Markov model the Bayesian network presented in this paper can successfully account for natural dependencies between neighboring pixels in both image dimensions making it more suitable for a larger class of images. The method described here was developed within the Stanford racing team that won the DARPA Grand Challenge 2005 after driving over 130 miles autonomously in the Nevada desert.