Nonlinear 3D and 2D Transforms for Image Processing and Surveillance

Abstract

Linear transforms such as bidimensional and tridimensional spatial Fourier transforms for image applications have their limitations due to the uncertainty principle. Also, Fourier transforms allow the existence of negative luminance, which is not physically possible. Wavelet transforms alleviate that through the use of a non-negative wavelet function base, but it still leads to wide spectrum representations. This paper discusses the deployment of new nonlinear methods such as Hilbert-Huang transform for low-cost embedded applications using microprocessors and field programmable gate arrays. Basically, we extract a set of intrinsic mode functions (IMFs), which represent the spectrum of the 3D or 2D scene of a space using these functions as a Hilbert base. Immediate applications for our low cost high performance hardware oriented architecture include image processing for biomedical applications (e.g. pattern recognition and image compression telemedicine) and surveillance.