A~trac t -A recursive, nonparametric method is developed for performing density estimation derived from mixture models, kernel estimation and stochastic approximation. The asymptotic performance of the method, dubbed "adaptive mixtures" (Priebe and Marchette, Pattern Recognition 24, 1197-1209 (1991)) for its data-driven development of a mixture model approximation to the true density, is invest...

Adaptive background techniques are useful for a wide spectrum of applications, ranging from security surveillance, traffic monitoring to medical and space imaging. With a properly estimated background, moving or new objects can be easily detected and tracked. Existing techniques are not suitable for real-world implementation, either because they are slow or because they do not perform well in t...

This paper focuses on directional textures. It provides a new framework for the design of convolution masks dedicated to orientation estimation and an adaptive algorithm which chooses the best mask size for each pixel. The design of the adaptive algorithm is based on the combination of two complementary operators: a gradient based operator which is adapted to sloped regions and a valleyness det...

General results on adaptive function estimation are obtained with respect to a collection of estimation strategies for both density estimation and nonparametric regression under square L 2 loss. It is shown that without knowing which strategy in a given countable collection works best for the underlying function, a single strategy can be constructed by mixing the proposed ones so that it is ada...

This insert describes the module akdensity. akdensity extends the official kdensity that estimates density functions by the kernel method. The extensions are of two types: akdensity allows the use of an “adaptive kernel” approach with varying, rather than fixed, bandwidths; and akdensity estimates pointwise variability bands around the estimated density functions.

In a recent work we recast the problem of estimating the minimum eigenvector (eigenvector corresponding to the minimum eigenvalue) of a symmetric positive definite matrix into a neural network framework. We now extend this work using an inflation technique to estimate all or some of the orthogonal eigenvectors of the given matrix. Based on these results, we form a cost function for the finite d...

This paper presents Adaptive Population Sizing Genetic Algorithm (AGA) assisted Maximum Likelihood (ML) estimation of Orthogonal Frequency Division Multiplexing (OFDM) symbols in the presence of Nonlinear Distortions. The proposed algorithm is simulated in MATLAB and compared with existing estimation algorithms such as iterative DAR, decision feedback clipping removal, iteration decoder, Geneti...