Object recognition using Radon transform based RST parameter estimation

In the random bivector choice experiment, GCFD1 and GCFD2 standard deviation are quite important compared to the ones of GFD1 and GFD2. This clearly reveals that the GCFD depends on

The main motivation is that there exists no normalized shape- based measure which considers both FPs and FNs distances ca- pable of recording a desired evolution of the object

Complex coordinates, centroid distance function, tangent angle (turning angles), curvature function, area function, trian- gle-area representation and chord length function are

RT: Right Turn, ST: Stop, RR: Reverse Right, LT: Left Turn MA: Move Ahead, RL: Reverse Left, RV: Reverse, SW: Slow It can be seen from Fig.7 that most of the gestures in the

Comparison results with common and powerful shape descriptors testify the effectiveness of the proposed method in recognizing binary images, RST transformed, distorted, occluded

By using the Radon transform properties, the issue is to recover the transformation parameters regarding the rotation, scaling and translation, by handling only the image

Abstract : In this paper, the Radon transform is used to design a new descriptor called Phi-signature invariant to usual geometric transformations. Experiments show the effectiveness

3-D skeleton joints-based action recognition using covariance descriptors on discrete spherical harmonics transform.. International Conference on Image Processing (ICIP 2015), IEEE,