Sky Segmentation By Fusing Clustering with neural networks

Authors

Ali Pour Yazdanpanah, University of Nevada, Las VegasFollow
Emma Regentova, University of Nevada, Las VegasFollow
Ajay Kumar Mandava, University of Nevada, Las VegasFollow
Touqeer Ahmad, University of Nevada - RenoFollow
George Bebis, University of Nevada - RenoFollow

Document Type

Conference Proceeding

Publication Date

7-29-2013

Publication Title

9th International Symposium on Advances in Visual Computing, ISVC 2013, July 29, 2013 - July 31

Publisher

Springer

Volume

8034

First page number:

663

Last page number:

672

Abstract

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.

Disciplines

Biomedical | Controls and Control Theory | Electrical and Computer Engineering | Electrical and Electronics | Electromagnetics and Photonics | Power and Energy | Signal Processing

Language

English

Permissions

Use Find in Your Library, contact the author, or interlibrary loan to garner a copy of the item. Publisher policy does not allow archiving the final published version. If a post-print (author's peer-reviewed manuscript) is allowed and available, or publisher policy changes, the item will be deposited.

Repository Citation

Yazdanpanah, A. P., Regentova, E., Mandava, A. K., Ahmad, T., Bebis, G. (2013). Sky Segmentation By Fusing Clustering with neural networks. 9th International Symposium on Advances in Visual Computing, ISVC 2013, July 29, 2013 - July 31, 8034 663-672. Springer.