The coded aperture snapshot spectral imaging (CASSI) architecture has been employed widely for capturing hyperspectral video. Despite allowing concurrent capture of hyperspectral video, spatial modulation in CASSI sacrifices image resolution significantly while reconstructing spectral projection via sparse sampling. Several multiview alternatives have been proposed to handle this low spatial re...

—Natural images tend to mostly consist of smooth regions with individual pixels having highly correlated spectra. This information can be exploited to recover hyperspectral images of natural scenes from their incomplete and noisy measurements. To perform the recovery while taking full advantage of the prior knowledge, we formulate a composite cost function containing a square-error data-fitting...

This paper introduces a new supervised classification method for hyperspectral images that combines spectral and spatial information. A support vector machine (SVM) classifier, integrated with a subspace projection method to address the problems of mixed pixels and noise, is first used to model the posterior distributions of the classes based on the spectral information. Then, the spatial infor...

The effectiveness of many hyperspectral feature extraction algorithms involving classification (and linear spectral unmixing) are dependent on the use of spectral signature libraries. If two or more signatures are roughly similar to each other, these methods which use algorithms such as singular value decomposition (SVD) or least squares to identify the object will not work well. This especiall...

In this paper we introduce for the first time particle filtering for an exponential family of densities. We prove that under certain conditions the approximated conditional density converges to the true conditional density. In the realistic setting where the conditional density does not lie in an exponential family but stays close to it, we show that under certain assumptions the error of the e...

This paper introduces the R package FKSUM, which offers fast and exact evaluation of univariate kernel smoothers. The main computations are implemented in C++, wrapped simple, intuitive versatile functions. based on recursive expressions involving order statistics, allows for smoothers at all sample points log-linear time. In addition to general purpose smoothing functions, built readyto-use im...

In medical imaging a high image quality is required. Exact cone-beam reconstruction algorithms, e.g. the Mline method, provide excellent image quality without any cone artifacts. Although the M-line approach is still of a filtered backprojection style, it has an increased computational complexity as it requires additional computations for the filtering of the projection images, e.g. derivative ...