Digital Image Processing
Explosives detection systems (EDS) for aviation security
Signal Processing
Background Modeling and Subtraction of Dynamic Scenes
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Improving kernel Fisher discriminant analysis for face recognition
IEEE Transactions on Circuits and Systems for Video Technology
| Hi-index | 0.00 |
The Negative Coefficient Polynomial (NCoP) employs nonparametric Kernel Density Estimation (KDE) technique to post process images and to produce difference images and statistics that offer meaningful measures to determine the kernel function effectiveness in object extraction. In this paper, three NEW kernel functions of NCoP was developed (Hyperbolic Cosecant, Skewed Polynomial and Negative Polynomial kernel functions) to compare with commonly used kernel functions. Four experiments were designed to evaluate the KDE functions: 1) moving object 2) lighting change 3) moving background and 4) missing object. Results indicate that the polynomial functions yielded a 70% false detection rate compared to