Sky Segmentation by Fusing Clustering with Neural Networks
| Autor: | Emma E. Regentova, Touqeer Ahmad, Ali Pour Yazdanpanah, George Bebis, Ajay K. Mandava |
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| Rok vydání: | 2013 |
| Předmět: |
Artificial neural network
Receiver operating characteristic Computer science business.industry media_common.quotation_subject ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Mars rover ComputingMethodologies_PATTERNRECOGNITION Sky Obstacle Segmentation Computer vision Motion planning Artificial intelligence Cluster analysis business media_common |
| Zdroj: | Advances in Visual Computing ISBN: 9783642419386 ISVC (2) |
| DOI: | 10.1007/978-3-642-41939-3_65 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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