Improved extended Gaussian image and its application on 3D point clouds registration

To solve the problem of lacking geometric and topological information for conventional 3D point clouds registration algorithm, this paper proposed a novel 3D point clouds registration algorithm based on improved extended Gaussian image. The proposed registration algorithm first estimates a normal vector and curvature of every point by least square method. Then, according to the normal vector to calculate extended Gaussian image (EGI) and complex extended Gaussian image (CEGI). By using the calculated EGI/CEGI and spherical harmonic function, a correlated function is constructed to calculate 3D rotation space to obtain initial positions coarse registration result. At last, by using the Fourier transform to estimate translation vector and coarse registration, the iterative closest point algorithm is used to obtain the fine registration results. Experiments on three groups of different 3D point clouds are performed to validate the proposed registration algorithm. Experimental results have shown that the proposed algorithm has good performances on registration of different forms of 3D point clouds. The robustness and efficiency of our proposed algorithm can effectively solve the problem that it is difficult to find the target or the homonymic feature points in the registration process of 3D point clouds.

Analysis of Gait Flow Image and Gait Gaussian Image Using Extension Neural Network for Gait Recognition

This paper proposes a new technique to recognize human gait by combining model free feature extraction approaches and a classifier. Gait flow image (GFI) and gait Gaussian image (GGI) are the two feature extraction techniques used in combination with ENN. GFI is a gait period based technique, uses optical flow features. So it directly focuses on dynamic part of human gait. GGI is another gait period based technique, computed by applying Gaussian membership function on human silhouettes. Next, ENN has been used as a classifier which combines the extension theory and neural networks. All the study has been done on CASIA-A and OU-ISIR treadmill B databases. The results derived using ENN are compared with SVM (support vector machines) and NN (Nearest neighbor) classifiers. ENN proved to give good accuracy and less iteration as compared to other traditional methods.