Due to the features of the multi-spectral images, the result with the usual methods based on the support vector machine (SVM) and binary tree is not satisfactory. In this paper, a fuzzy SVM multi-class classifier with the binary tree is proposed for the classification of multi-spectral images. The experiment is conducted on a multi-spectral image with 6 bands which contains three classes of terrains. The experimental results show that this method can improve the segmentation accuracy.
Humanoid walking planning is a complicated task because of the high number of degrees of freedom （DOFs） and the variable mechanical structure during walking. In this paper, a planning method for 3- dimensional （3-D） walking movements was developed based on a model of a typical humanoid robot with 12 DOFs on the lower body. The planning process includes trajectory generation for the hip, ankle, and knee joints in the Cartesian space. The balance of the robot was ensured by adjusting the hip motion. The angles for each DOF were obtained from 3-D kinematics calculation. The calculation gave reference trajectories of all the DOFs on the humanoid robot which were used to control the real robot. The simulation results show that the method is effective.
