Opto-Electronic Engineering, Volume. 34, Issue 10, 59(2007)
Terrain re-sampling of imaging lidar based on support vector machines
[2] [2] Greer Donald R,Fung Irene,Shapiro Jeffrey H.Maximum-Likelihood Multiresolution Laser Radar Range Imaging[J].IEEE Trans.on Image Processing,1997,6(1):37-46.
[4] [4] Vapnik V.The Nature of Statistical Learning Theory[M].New York:Springer,1995.
[5] [5] Vapnik V.Statistical Learning Theory[M].New York:Wiley,1998.
[7] [7] Suykens J A K,Vandewalle J.Nonlinear Modeling:Advanced Black-box Techniques[M].Boston:Kluwer Academic Publishers,1998.
[8] [8] Suykens J A K.Weighted Least Squares Support Vector Machines:Robustness and Sparse Approximation[J].Neurocomputing,2002,48:85-105.
[10] [10] Chua K S.Efficient Computations for Large Least Square Support Vector Machine Classifiers[J].Pattern Recognition Letters,2003,24:75-80.
[11] [11] Suykens J A K.Recurrent Least Squares Support Vector Machines[J].IEEE Transactions on circuits and systems-I:Fundamental theory and applications,2000,47(7):1109-1114.
[12] [12] Suykens J A K.Optimal Control by Least Squares Support Vector Machines[J].Neural Networks,2001,14:23-35.
[13] [13] Zheng S.Novel Algorithm for Image Interpolation[J].Opt.Eng,2004,43(4):856-865.
[14] [14] Lee Seungyong,Wolberg George,Shin Sungyong.Scattered Data Interpolation with Multilevel B-Splines[J].IEEE Trans.on Visualization and Computer Graphics,1997,3(3):228-245.
Get Citation
Copy Citation Text
[in Chinese], [in Chinese], [in Chinese]. Terrain re-sampling of imaging lidar based on support vector machines[J]. Opto-Electronic Engineering, 2007, 34(10): 59