In INS/GPS integration, the data fusion algorithm involves properly handling of nonlinear models. Therefore the nonlinear filtering methods have been commonly applied in the INS/GPS integration to estimate the state vector. The most popular and commonly used method is the Extended Kalman Filter (EKF) which approximates the nonlinear state and measurement equations using the first order Taylor s...

Summed area tables, also known as integral images, allow image averaging to be performed very efficiently at a small fixed cost per pixel, independent of averaging filter size. Repeated filtering with averaging filters can be used to approximate Gaussian filtering. Thus a good approximation to Gaussian filtering can be achieved at a fixed cost per pixel independent of filter size. This note des...

A new class of algorithms is described for the analysis of boundaries in a discrete image. The methods are based on well known methods of Gaussian filtering coupled with the use of numerical methods. Instead of the conventional discrete filter and exhaustive evaluation by convolution the filter output is computed only at points of interest. In addition derivative information is gathered at thes...

The selection of appropriate wavelets is an important target for any application. In this paper Face recognition has been performed using Principal component analysis (PCA), Gaussian based PCA and Gabor based PCA. PCA extracts the relevant information from complex data sets and provides a solution to reduce dimensionality. PCA is based on Euclidean distance calculation which is minimized by app...

The selection of appropriate wavelets is an important target for any application. In this paper Face recognition has been performed using Principal component analysis (PCA), Gaussian based PCA and Gabor based PCA. PCA extracts the relevant information from complex data sets and provides a solution to reduce dimensionality. PCA is based on Euclidean distance calculation which is minimized by app...

We have developed a method for approximating a Gaussian decay low-pass filter using subthreshold CMOS VLSI circuits. The time constant (or u) of the delay is adjustable over several orders of magnitude and the DC gain is guaranteed to be unity. Since the design is compact and straightforward, the proposed circuit can be used as a building block that can replace first-order low-pass filters in m...

Estimation of a dynamical system’s latent state subject to sensor noise and model inaccuracies remains critical yet difficult problem in robotics. While Kalman filters provide the optimal solution least squared sense for linear Gaussian problems, general nonlinear non-Gaussian case is significantly more complicated, typically relying on sampling strategies that are limited low-dimensional space...

The cardinalized probability hypothesis density (CPHD) filter is an alternative approximation to the full multi-target Bayesian filter for tracking multiple targets. However, although the joint propagation of the posterior intensity and cardinality distribution in its recursion allows more reliable estimates of the target number than the PHD filter, the CPHD filter suffers from the spooky effec...

A spatial domain non linear filtering algorithm for the restoration of gray scale, and Color images that are corrupted by high density impulse noise is proposed. The proposed algorithm well suited for suppressing impulse and also Gaussian noises with different noise leves.It shows better results than the Standard Median Filter (MF), Trimmed Median Filter (TMF) and Weighted Median Filter. Experi...