41,988 research outputs found
Strong edge features for image coding
A two-component model is proposed for perceptual image coding. For the first component of the model, the watershed operator is used to detect strong edge features. Then, an efficient morphological interpolation algorithm reconstructs the smooth areas of the image from the extracted edge information, also known as sketch data. The residual component, containing fine textures, is separately coded by a subband coding scheme. The morphological operators involved in the coding of the primary component perform very efficiently compared to conventional techniques like the LGO operator, used for the edge extraction, or the diffusion filters, iteratively applied for the interpolation of smooth areas in previously reported sketch-based coding schemes.Peer ReviewedPostprint (published version
Image interpolation using Shearlet based iterative refinement
This paper proposes an image interpolation algorithm exploiting sparse
representation for natural images. It involves three main steps: (a) obtaining
an initial estimate of the high resolution image using linear methods like FIR
filtering, (b) promoting sparsity in a selected dictionary through iterative
thresholding, and (c) extracting high frequency information from the
approximation to refine the initial estimate. For the sparse modeling, a
shearlet dictionary is chosen to yield a multiscale directional representation.
The proposed algorithm is compared to several state-of-the-art methods to
assess its objective as well as subjective performance. Compared to the cubic
spline interpolation method, an average PSNR gain of around 0.8 dB is observed
over a dataset of 200 images
XAFS spectroscopy. I. Extracting the fine structure from the absorption spectra
Three independent techniques are used to separate fine structure from the
absorption spectra, the background function in which is approximated by (i)
smoothing spline. We propose a new reliable criterion for determination of
smoothing parameter and the method for raising of stability with respect to
k_min variation; (ii) interpolation spline with the varied knots; (iii) the
line obtained from bayesian smoothing. This methods considers various prior
information and includes a natural way to determine the errors of XAFS
extraction. Particular attention has been given to the estimation of
uncertainties in XAFS data. Experimental noise is shown to be essentially
smaller than the errors of the background approximation, and it is the latter
that determines the variances of structural parameters in subsequent fitting.Comment: 16 pages, 7 figures, for freeware XAFS analysis program, see
http://www.crosswinds.net/~klmn/viper.htm
Automated Markerless Extraction of Walking People Using Deformable Contour Models
We develop a new automated markerless motion capture system for the analysis of walking people. We employ global evidence gathering techniques guided by biomechanical analysis to robustly extract articulated motion. This forms a basis for new deformable contour models, using local image cues to capture shape and motion at a more detailed level. We extend the greedy snake formulation to include temporal constraints and occlusion modelling, increasing the capability of this technique when dealing with cluttered and self-occluding extraction targets. This approach is evaluated on a large database of indoor and outdoor video data, demonstrating fast and autonomous motion capture for walking people
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