537 research outputs found
Molecular Surface Mesh Generation by Filtering Electron Density Map
Bioinformatics applied to macromolecules are now widely spread and in continuous expansion. In this context, representing external molecular surface such as the Van der Waals Surface or the Solvent Excluded Surface can be useful for several applications. We propose a fast and parameterizable algorithm giving good visual quality meshes representing molecular surfaces. It is obtained by isosurfacing a filtered electron density map. The density map is the result of the maximum of Gaussian functions placed around atom centers. This map is filtered by an ideal low-pass filter applied on the Fourier Transform of the density map. Applying the marching cubes algorithm on the inverse transform provides a mesh representation of the molecular surface
Post-Reconstruction Deconvolution of PET Images by Total Generalized Variation Regularization
Improving the quality of positron emission tomography (PET) images, affected
by low resolution and high level of noise, is a challenging task in nuclear
medicine and radiotherapy. This work proposes a restoration method, achieved
after tomographic reconstruction of the images and targeting clinical
situations where raw data are often not accessible. Based on inverse problem
methods, our contribution introduces the recently developed total generalized
variation (TGV) norm to regularize PET image deconvolution. Moreover, we
stabilize this procedure with additional image constraints such as positivity
and photometry invariance. A criterion for updating and adjusting automatically
the regularization parameter in case of Poisson noise is also presented.
Experiments are conducted on both synthetic data and real patient images.Comment: First published in the Proceedings of the 23rd European Signal
Processing Conference (EUSIPCO-2015) in 2015, published by EURASI
Forward Error Correction applied to JPEG-XS codestreams
JPEG-XS offers low complexity image compression for applications with
constrained but reasonable bit-rate, and low latency. Our paper explores the
deployment of JPEG-XS on lossy packet networks. To preserve low latency,
Forward Error Correction (FEC) is envisioned as the protection mechanism of
interest. Despite the JPEG-XS codestream is not scalable in essence, we observe
that the loss of a codestream fraction impacts the decoded image quality
differently, depending on whether this codestream fraction corresponds to
codestream headers, to coefficients significance information, or to low/high
frequency data, respectively. Hence, we propose a rate-distortion optimal
unequal error protection scheme that adapts the redundancy level of
Reed-Solomon codes according to the rate of channel losses and the type of
information protected by the code. Our experiments demonstrate that, at 5% loss
rates, it reduces the Mean Squared Error by up to 92% and 65%, compared to a
transmission without and with optimal but equal protection, respectively
Optimal measurement budget allocation for particle filtering
Particle filtering is a powerful tool for target tracking. When the budget
for observations is restricted, it is necessary to reduce the measurements to a
limited amount of samples carefully selected. A discrete stochastic nonlinear
dynamical system is studied over a finite time horizon. The problem of
selecting the optimal measurement times for particle filtering is formalized as
a combinatorial optimization problem. We propose an approximated solution based
on the nesting of a genetic algorithm, a Monte Carlo algorithm and a particle
filter. Firstly, an example demonstrates that the genetic algorithm outperforms
a random trial optimization. Then, the interest of non-regular measurements
versus measurements performed at regular time intervals is illustrated and the
efficiency of our proposed solution is quantified: better filtering
performances are obtained in 87.5% of the cases and on average, the relative
improvement is 27.7%.Comment: 5 pages, 4 figues, conference pape
A Three-Level Computational Attention Model
This article deals with a biologically-motivated three-level computational attention model architecture based on the rarity and the information theory framework. It mainly focuses on a low-level step which aims in fastly highlighting important areas and a middle-level step which analyses the behaviour of the detected areas. Their application on both still images and videos provide results to be used by the third high-level step
A Three-Level Computational Attention Model
This article deals with a biologically-motivated three-level computational attention model architecture based on the rarity and the information theory framework. It mainly focuses on a low-level step which aims in fastly highlighting important areas and a middle-level step which analyses the behaviour of the detected areas. Their application on both still images and videos provide results to be used by the third high-level step
Security and robustness constraints for spread-spectrum Tardos fingerprinting
International audienceThis paper presents a practical analysis of the impact of robustness and security on Tardos' collusion-secure fingerprinting codes using spread-spectrum watermarking modulations. In this framework, we assume that the coalition has to face an embedding scheme of given security level and consequently has to suffer a probability of wrongly estimating their embedded symbols. We recall the Worst Case Attack associated to this probability, e.g. the optimal attack which minimises the mutual information between the sequence of a colluder and the pirated one. For a given achievable rate of the Tardos' fingerprinting model, we compare the Improved Spread-Spectrum embedding versus a new secure embedding (called rho-Circular Watermarking) considering the AWGN channel. We show that secure embeddings are more immune to decoding errors than non-secure ones while keeping the same fingerprinting capacity
Computational Attention for Defect Localisation
This article deals with a biologically-motivated three-level computational attention model architecture based on the rarity and the information theory framework. It mainly focuses on a low-level step and its application in pre-attentive defect localisation for apple quality grading and tumour localisation for medical images
Computational Attention for Event Detection
This article deals with a biologically-motivated three-level computational attention model architecture based on the rarity and the information theory framework. It mainly focuses on low-level and medium-level steps and their application in pre-attentive detection of tumours in CT scans and unusual events in audio recordings
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