Self-Supervised Segmentation of River Scenes
Here we consider the problem of automatically segmenting images taken from a boat or low-flying aircraft. Such a capability is important for autonomous river following and mapping. The need for accurate segmentation in a wide variety of riverine environments challenges the state of the art vision-based methods that have been used in more structured environments such as roads and highways. Apart from the lack of structure, the principal difficulty is the large spatial and temporal variations in the appearance of water in the presence of nearby vegetation and with reflections from the sky. We propose a self-supervised method to segment images into ‘sky’, ‘river’ and ‘shore’ (vegetation + structures) regions. Our approach uses assumptions about river scene structure to learn appearance models based on features like color, texture and image location which are used to segment the image. We validated our algorithm by testing on four datasets captured under varying conditions on different rivers. Our self-supervised algorithm had higher accuracy rates than a supervised alternative, often significantly more accurate, and does not need to be retrained to work under different conditions.
Supreeth Achar Bharath Sankaran Stephen Nuske Sebastian Scherer Sanjiv Singh
The Robotics Institute,Carnegie Mellon University,Pittsburgh,PA 15213,U.S.A. GRASP Laboratory,University of Pennsylvania,Philadelphia,PA,19104,U.S.A
国际会议
2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
上海
英文
6227-6232
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）