59,050 research outputs found
Detection of dirt impairments from archived film sequences : survey and evaluations
Film dirt is the most commonly encountered artifact in archive restoration applications. Since dirt usually appears as a temporally impulsive event, motion-compensated interframe processing is widely applied for its detection. However, motion-compensated prediction requires a high degree of complexity and can be unreliable when motion estimation fails. Consequently, many techniques using spatial or spatiotemporal filtering without motion were also been proposed as alternatives. A comprehensive survey and evaluation of existing methods is presented, in which both qualitative and quantitative performances are compared in terms of accuracy, robustness, and complexity. After analyzing these algorithms and identifying their limitations, we conclude with guidance in choosing from these algorithms and promising directions for future research
Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification
Dynamic Textures (DTs) are sequences of images of moving scenes that exhibit
certain stationarity properties in time such as smoke, vegetation and fire. The
analysis of DT is important for recognition, segmentation, synthesis or
retrieval for a range of applications including surveillance, medical imaging
and remote sensing. Deep learning methods have shown impressive results and are
now the new state of the art for a wide range of computer vision tasks
including image and video recognition and segmentation. In particular,
Convolutional Neural Networks (CNNs) have recently proven to be well suited for
texture analysis with a design similar to a filter bank approach. In this
paper, we develop a new approach to DT analysis based on a CNN method applied
on three orthogonal planes x y , xt and y t . We train CNNs on spatial frames
and temporal slices extracted from the DT sequences and combine their outputs
to obtain a competitive DT classifier. Our results on a wide range of commonly
used DT classification benchmark datasets prove the robustness of our approach.
Significant improvement of the state of the art is shown on the larger
datasets.Comment: 19 pages, 10 figure
A Bayesian fusion model for space-time reconstruction of finely resolved velocities in turbulent flows from low resolution measurements
The study of turbulent flows calls for measurements with high resolution both
in space and in time. We propose a new approach to reconstruct
High-Temporal-High-Spatial resolution velocity fields by combining two sources
of information that are well-resolved either in space or in time, the
Low-Temporal-High-Spatial (LTHS) and the High-Temporal-Low-Spatial (HTLS)
resolution measurements. In the framework of co-conception between sensing and
data post-processing, this work extensively investigates a Bayesian
reconstruction approach using a simulated database. A Bayesian fusion model is
developed to solve the inverse problem of data reconstruction. The model uses a
Maximum A Posteriori estimate, which yields the most probable field knowing the
measurements. The DNS of a wall-bounded turbulent flow at moderate Reynolds
number is used to validate and assess the performances of the present approach.
Low resolution measurements are subsampled in time and space from the fully
resolved data. Reconstructed velocities are compared to the reference DNS to
estimate the reconstruction errors. The model is compared to other conventional
methods such as Linear Stochastic Estimation and cubic spline interpolation.
Results show the superior accuracy of the proposed method in all
configurations. Further investigations of model performances on various range
of scales demonstrate its robustness. Numerical experiments also permit to
estimate the expected maximum information level corresponding to limitations of
experimental instruments.Comment: 15 pages, 6 figure
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