A new multiresolution algorithm for image compression based on projection pursuit neural networks is presented. High quality low bit-rate image compression is achieved first by segmenting an image into regions of different sizes based on perceptual variation in each region and then constructing a distinct code for each block by using the orthogonal projection pursuit neural networks. This algor...

We present an iterative approach to solve separable nonlinear least squares problems arising in the estimation of wavelength-dependent point spread function (PSF) parameters for hyperspectral imaging. A variable projection Gauss-Newton method is used to solve the nonlinear least squares problem. An analysis shows that the Jacobian can be potentially very ill-conditioned. To deal with this ill-c...

Independent Component Analysis (ICA) is a multivariate data analysis process largely sudied these last years in the signal processing community for blind source separation. This paper proposes to show the interest of ICA as a tool for unsupervised analysis of hyperspectral images. The commonly used Principal Component Analysis (PCA) is the mean square optimal projection for gaussian data leadin...

Introduction: Lifting-based motion-compensated temporal filtering (MCTF) is used widely as the preferred temporal transformation technique in state-of-the-art three-dimensional subband video coding. It involves two basic stages: the prediction stage that takes the original input frames to generate the highpass frames and the update stage that uses the available highpass frames and even frames t...

Combining the advantages of genetic algorithm (GA) and projection pursuit regression (PPR), this article firstly uses improved Hermit polynomial as the ridge function of projection pursuit regression model. And then adopted the real-coded genetic algorithm to optimize the projection direction, a forecasting model for peak flow of short flood forecasting is presented. Applied the presented model...

hyperspectral sensors provide a large number of spectral bands. this massive and complex data structure of hyperspectral images presents a challenge to traditional data processing techniques. therefore, reducing the dimensionality of hyperspectral images without losing important information is a very important issue for the remote sensing community. we propose to use overlap-based feature weigh...

We present a new and systematic method of approximating exact nonlinear lters with nite dimensional lters, using the diierential geometric approach to statistics. We deene rigorously the projection lter in the case of exponential families. We propose a convenient exponential family, which allows to simplify the projection lter equation, and to deene an a posteriori measure of the performance of...

This paper proposes a novel method of segment-tree filtering to improve the classification accuracy of hyperspectral image (HSI). Segment-tree filtering is a versatile method that incorporates spatial information and has been widely applied in image preprocessing. However, to use this powerful framework in hyperspectral image classification, we must reduce the original feature dimensionality to...

We extend a reinforcement learning algorithm which has previously been shown to cluster data. We have previously applied the method to unsupervised projection methods, principal component analysis, exploratory projection pursuit and canonical correlation analysis. We now show how the same methods can be used in feature spaces to perform kernel principal component analysis and kernel canonical c...