549 research outputs found
Seismic Fault Preserving Diffusion
This paper focuses on the denoising and enhancing of 3-D reflection seismic
data. We propose a pre-processing step based on a non linear diffusion
filtering leading to a better detection of seismic faults. The non linear
diffusion approaches are based on the definition of a partial differential
equation that allows us to simplify the images without blurring relevant
details or discontinuities. Computing the structure tensor which provides
information on the local orientation of the geological layers, we propose to
drive the diffusion along these layers using a new approach called SFPD
(Seismic Fault Preserving Diffusion). In SFPD, the eigenvalues of the tensor
are fixed according to a confidence measure that takes into account the
regularity of the local seismic structure. Results on both synthesized and real
3-D blocks show the efficiency of the proposed approach.Comment: 10 page
Spatial image polynomial decomposition with application to video classification
International audienceThis paper addresses the use of orthogonal polynomial basis transform in video classification due to its multiple advantages, especially for multiscale and multiresolution analysis similar to the wavelet transform. In our approach, we benefit from these advantages to reduce the resolution of the video by using a multiscale/multiresolution decomposition to define a new algorithm that decomposes a color image into geometry and texture component by projecting the image on a bivariate polynomial basis and considering the geometry component as the partial reconstruction and the texture component as the remaining part, and finally to model the features (like motion and texture) extracted from reduced image sequences by projecting them into a bivariate polynomial basis in order to construct a hybrid polynomial motion texture video descriptor. To evaluate our approach, we consider two visual recognition tasks, namely the classification of dynamic textures and recognition of human actions. The experimental section shows that the proposed approach achieves a perfect recognition rate in the Weizmann database and highest accuracy in the Dyntex++ database compared to existing methods
Principled Design and Implementation of Steerable Detectors
We provide a complete pipeline for the detection of patterns of interest in
an image. In our approach, the patterns are assumed to be adequately modeled by
a known template, and are located at unknown position and orientation. We
propose a continuous-domain additive image model, where the analyzed image is
the sum of the template and an isotropic background signal with self-similar
isotropic power-spectrum. The method is able to learn an optimal steerable
filter fulfilling the SNR criterion based on one single template and background
pair, that therefore strongly responds to the template, while optimally
decoupling from the background model. The proposed filter then allows for a
fast detection process, with the unknown orientation estimation through the use
of steerability properties. In practice, the implementation requires to
discretize the continuous-domain formulation on polar grids, which is performed
using radial B-splines. We demonstrate the practical usefulness of our method
on a variety of template approximation and pattern detection experiments
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