Rugged terrain segmentation based on salient features

ICCAS 2010 - International Conference on Control, Automation and Systems

Volumn , Issue , 2010, Pages 1737-1740

  Lenskiy, Artem A ^a   Lee, Jong Soo ^a  

^a University of Ulsan   (South Korea)

Author keywords

Salinet features; SURF; Terrain segmenation; Texture; Unmanned ground vehicle

Indexed keywords

CLASS MODELS; COUNTRY ROADS; IR CAMERA; LASER RANGE INFORMATION; MEAN ERRORS; MEMBERSHIP VALUES; RUGGED TERRAIN; SALIENT FEATURES; SALINET FEATURES; SEGMENTATION SYSTEM; SPATIAL POSITIONS; SURF; TERRAIN SEGMENATION; TEXTURAL INFORMATION; TRAINING IMAGE; TWO LAYERS; UNMANNED GROUND VEHICLES; VISIBLE SPECTRA; VISION BASED CONTROL;

COMPUTER GRAPHICS; COMPUTER VISION; FEATURE EXTRACTION; GROUND VEHICLES; IMAGE SEGMENTATION; LANDFORMS; NAVIGATION; NEURAL NETWORKS; TEXTURES; UNMANNED VEHICLES;

INTELLIGENT VEHICLE HIGHWAY SYSTEMS;

EID: 78751506595     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

1. Lalonde, J.-F., et al., Natural terrain classification using three-dimensional ladar data for ground robot mobility. Journal of Field Robotics, 2006. 23: p. 839-861.
2. Manduchi, R., Learning Outdoor Color Classification. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2006. 28(11): p. 1713-1723.
3. Castano, R., R. Manduchi, and J. Fox. Classification experiments on real-world texture. in Third Workshop on Empirical Evaluation Methods in Computer Vision. 2001. Kauai, Hawaii.
4. Sung, G.-Y., D.-M. Kwak, and J. Lyou, Neural Network Based Terrain Classification Using Wavelet Features. Journal of Intelligent and Robotic Systems, 2010.
5. Media lab, M., Vision texture. 2002.
6. Brodatz, P., Texture: A Photographic Album for Artists and Designers. 1966: Dover, New York.
7. Bay, H., et al., Speeded-Up Robust Features (SURF). Computer Vision and Image Understanding, 2008. 110(3): p. 346-359.
8. Lenskiy, A.A. and J.-S. Lee, Machine learning algorithms for visual navigation of unmanned ground vehicles, in Computational Modeling and Simulation of Intellect: Current State and Future Perspectives, B. Igelnik, Editor. 2010, IGI Global.
9. Cybenko, G., Approximation by superposition of a sigmoidal function. Math. Control. Signals, Syst., 1989. 2: p. 303-314.
10. Chester, D.L. Why Two Hidden Layers are Better than One. in IJCNN. 1990. Washington, DC.
11. Riedmiller, M. and H. Braun, A Direct Adaptive Method for Faster Backpropagation Learning The RPROP Algorithm, in IEEE International Conference on Neural Networks (ICNN). 1993: San Francisco. p. 586-591.
12. Martin Hagan and M. Menhaj, Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 1994. 5(6): p. 989-993.