Mars image segmentation with most relevant features among wavelet and color features

Rashno, A; Saraee, MH; Sadri, S

Abstract

Mars rover is a robot which explores the Mars surface, is equipped to front-line Panoramic Camera (Pancam). Automatic processing and segmentation of images taken by Pancam is one of the most important and most significant tasks of Mars rover since the transformation cost of images from Mars to earth is extremely high. In this paper, a new feature vector for image pixels will be proposed as well as a new feature selection schema based on ant colony optimization (ACO). Then, the most relevant features are presented for multiclass Support Vector Machine (SVM) classifier which led to high accuracy pixel classification and then image segmentation. Our proposed method is compared with genetic algorithm feature selection, experimental results show that the proposed method outperforms this method in the terms of accuracy and efficiently.

Citation

Rashno, A., Saraee, M., & Sadri, S. (2015, April). Mars image segmentation with most relevant features among wavelet and color features. Presented at AI & Robotics (IRANOPEN), 2015

• Presentation Conference Type: Other
• Conference Name: AI & Robotics (IRANOPEN), 2015
• Start Date: Apr 12, 2015
• Publication Date: Apr 15, 2015
• Deposit Date: Oct 24, 2016
• Book Title: 2015 AI & Robotics (IRANOPEN)
• DOI: https://doi.org/10.1109/RIOS.2015.7270747
• Publisher URL: http://dx.doi.org/10.1109/RIOS.2015.7270747
• Related Public URLs: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7231141; http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7231141
• Additional Information: Event Type : Conference