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A Probabilistic Framework for Adaptive Texture Description
This report details the development of a probabilistic framework for adaptive texture description. Starting with a probability distribution on the space of infinite... -
The Methodology and Practice of the Evaluation of Image Retrieval Systems and...
Content-Based Image Retrieval is important for two reasons. First, the oft-cited growth of image archives in many fields, and the rapid expansion of the Web, mean that... -
Image Retrieval by Range Query Composition of Region Categories
In this paper, we present an original and new framework to allow complex multiple regions queries based on composition of regions categories. This problematic has... -
Image segmentation using active contours: calculus of variations or shape gra...
We consider the problem of segmenting an image through the minimization of an energy criterion involving region and boundary functionals. We show that one can go from... -
EM-based image segmentation using Potts models with external field
Image segmentation using Markov random fields involves parameter estimation in hidden Markov models for which the EM algorithm is widely used. In practice, a simple... -
Hidden Markov Models Selection Criteria based on Mean Field-like approximations
Hidden Markov random fields appear naturally in problems such as image segmentation where an unknown class assignment has to be estimated from the observations for... -
4D Numerical Schemes for Cell Image Segmentation and Tracking.
6th International Symposium on Finite Volumes for Complex Applications Location: Prague, CZECH REPUBLIC JUN 06-10, 2011 -
A subjective surfaces based segmentation for the reconstruction of biological...
3rd International Conference on Computer Vision Theory and Applications: Funchal, PORTUGAL, JAN 22-25, 2008 -
Convergence of MCEM and Related Algorithms for Hidden Markov Random Field
The Monte Carlo EM (MCEM) algorithm is a stochastic version of the EM algorithm using MCMC methods to approximate the conditional distribution of the hidden data. In... -
EM Procedures Using Mean Field-Like Approximations for Markov Model-Based Ima...
This paper deals with Markov random field model-based image segmentation. This involves parameter estimation in hidden Markov models for which one of the most widely... -
Coupled Geodesic Active Regions for Image Segmentation
This paper presents a novel variational method for image segmentation which is obtained by unifying boundary and region-based information sources under the Geodesic... -
General Object Reconstruction based on Simplex Meshes
In this paper, we propose a general tridimensional reconstruction algorithm of range and volumetric images, based on deformable simplex meshes. The algorithm is able... -
An Analysis of Some Models Used in Image Segmentation
This report describes an investigation into the characteristics of a number of image models and algorithms used for the segmentation of SPOT images. The initial goals... -
Fully Bayesian Image Segmentation - an Engineering Perspective
Developments in Markov Chain Monte Carlo procedures have made it possible to perform fully Bayesian image segmentation. By this we mean that all the parameters are... -
Estimation of Markov Random Field Prior Parameters Using Markov Chain Monte C...
Recent developments in statistics now allow maximum likelihood estimators for the parameters of Markov Random Fields to be constructed. We detail the theory required,... -
Hyperconnections and Hierarchical Representations for Grayscale and Multiband...
International audience -
Curvilinear structure enhancement with the polygonal path image - Application...
International audience -
Graph-based variational optimization and applications in computer vision
Many computer vision applications such as image filtering, segmentation and stereovision can be formulated as optimization problems. Recently discrete, convex,... -
Soil porosity resulting from the assemblage of silt grains with a clay phase:...
International audience -
Tree-walk kernels for computer vision
International audience
