Sky segmentation by fusing clustering with neural networks
Sky segmentation is an important task for many applications related to obstacle detection and path planning for autonomous air and ground vehicles. In this paper, we present a method for the automated sky segmentation by fusing K-means clustering and Neural Network (NN) classifications. The performance of the method has been tested on images taken by two Hazcams (ie., Hazard Avoidance Cameras) on NASA’s Mars rover. Our experimental results show high accuracy in determining the sky area. The effect of various parameters is demonstrated using Receiver Operating Characteristic (ROC) curves.
Biomedical | Controls and Control Theory | Electrical and Computer Engineering | Electrical and Electronics | Electromagnetics and Photonics | Power and Energy | Signal Processing
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Yazdanpanah, A. P.,
Mandava, A. K.,
Sky segmentation by fusing clustering with neural networks.
9th International Symposium on Advances in Visual Computing, ISVC 2013, July 29, 2013 - July 31, 8034