| Autor: |
Jalali, Sepehr, Seekings, Paul J., Tan, Cheston, Ratheesh, Aiswarya, Lim, Joo-Hwee, Taylor, Elizabeth A. |
| Zdroj: |
2013 International Joint Conference on Neural Networks (IJCNN); 2013, p1-8, 8p |
| Abstrakt: |
In this paper we propose a new biologically inspired model which simulates the visual pathways in the human brain used for classification of matching optical and sonar derived images. Marine mammals, such as dolphins, that live in waters with poor optical clarity and low light levels such as littoral zones, use a combination of optical vision and biosonar to navigate and hunt for prey. Given that dolphins have evolved a synergistic combination of optical visual input and acoustic/sonar input, the primary focus of this paper is on reaching a similar level of synergy for a diver or Autonomous Underwater Vehicle (AUV) platform equipped with a system to extend the range and resolution of vision in poor ambient visibility. We propose a biologically inspired model that combines and processes visual images acquired via optical and acoustic pathways and show that the combined model enhances the accuracy of automatic classification of target objects in underwater images. [ABSTRACT FROM PUBLISHER] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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