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Publications of type 'inproceedings'
Result of the query in the list of publications :
245 Conference articles |
91 - Sur la complexite et la rapidite d’algorithmes pour la minimisation de la variation totale sous contraintes.
P. Weiss and L. Blanc-Féraud and G. Aubert. In Proc. Symposium on Signal and Image Processing (GRETSI), Troyes, France, September 2007.
Keywords : l1 norm minimization, compression noise denoising, optimal algorithm, convex analysis, Total variation, nesterov scheme.
@INPROCEEDINGS{Pierre Weiss,
|
| author |
= |
{Weiss, P. and Blanc-Féraud, L. and Aubert, G.}, |
| title |
= |
{Sur la complexite et la rapidite d’algorithmes pour la minimisation de la variation totale sous contraintes}, |
| year |
= |
{2007}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. Symposium on Signal and Image Processing (GRETSI)}, |
| address |
= |
{Troyes, France}, |
| url |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/Gretsi_WeissBlancFeraudAubert_2010.PDF}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_Pierre Weiss.pdf}, |
| keyword |
= |
{l1 norm minimization, compression noise denoising, optimal algorithm, convex analysis, Total variation, nesterov scheme} |
| } |
|
92 - A Multispectral Data Model for Higher-Order Active Contours and its Application to Tree Crown Extraction.
P. Horvath. In Proc. Advanced Concepts for Intelligent Vision Systems, Delft, Netherlands, August 2007.
Keywords : Higher-order, Tree Crown Extraction, Colour.
@INPROCEEDINGS{Horvath07c,
|
| author |
= |
{Horvath, P.}, |
| title |
= |
{A Multispectral Data Model for Higher-Order Active Contours and its Application to Tree Crown Extraction}, |
| year |
= |
{2007}, |
| month |
= |
{August}, |
| booktitle |
= |
{Proc. Advanced Concepts for Intelligent Vision Systems}, |
| address |
= |
{Delft, Netherlands}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_Horvath07c.pdf}, |
| keyword |
= |
{Higher-order, Tree Crown Extraction, Colour} |
| } |
Abstract :
Forestry management makes great use of statistics concerning the
individual trees making up a forest, but the acquisition of this
information is expensive. Image processing can potentially both
reduce this cost and improve the statistics. The key problem is the
delineation of tree crowns in aerial images. The automatic solution
of this problem requires considerable prior information to be built
into the image and region models. Our previous work has focused on
including shape information in the region model; in this paper we
examine the image model. The aerial images involved have three
bands. We study the statistics of these bands, and construct both
multispectral and single band image models. We combine these with a
higher-order active contour model of a `gas of circles' in order to
include prior shape information about the region occupied by the
tree crowns in the image domain. We compare the results produced by
these models on real aerial images and conclude that multiple bands
improves the quality of the segmentation. The model has many other
potential applications, e.g. to nano-technology, microbiology,
physics, and medical imaging.
|
|
93 - A New Phase Field Model of a `Gas of Circles' for Tree Crown Extraction from Aerial Images.
P. Horvath and I. H. Jermyn. In Proc. International Conference on Computer Analysis of Images and Patterns (CAIP), Vienna, Austria, August 2007.
Keywords : Phase Field, Tree Crown Extraction.
@INPROCEEDINGS{Horvath07b,
|
| author |
= |
{Horvath, P. and Jermyn, I. H.}, |
| title |
= |
{A New Phase Field Model of a `Gas of Circles' for Tree Crown Extraction from Aerial Images}, |
| year |
= |
{2007}, |
| month |
= |
{August}, |
| booktitle |
= |
{Proc. International Conference on Computer Analysis of Images and Patterns (CAIP)}, |
| address |
= |
{Vienna, Austria}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_Horvath07b.pdf}, |
| keyword |
= |
{Phase Field, Tree Crown Extraction} |
| } |
Abstract :
We describe a model for tree crown extraction from aerial images, a
problem of great practical importance for the forestry industry. The
novelty lies in the prior model of the region occupied by tree
crowns in the image, which is a phase field version of the
higher-order active contour inflection point `gas of circles' model.
The model combines the strengths of the inflection point model with
those of the phase field framework: it removes the `phantom circles'
produced by the original `gas of circles' model, while executing two
orders of magnitude faster than the contour-based inflection point
model. The model has many other areas of application e.g., to
imagery in nanotechnology, biology, and physics. |
|
94 - Removing Shape-Preserving Transformations in Square-Root Elastic (SRE) Framework for Shape Analysis of Curves.
S. Joshi and E. Klassen and A. Srivastava and I. H. Jermyn. In Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), Ezhou, China, August 2007.
Keywords : Shape, Reparameterization, Metric, Geodesic.
Copyright : The original publication is available at www.springerlink.com.
@INPROCEEDINGS{Joshi07b,
|
| author |
= |
{Joshi, S. and Klassen, E. and Srivastava, A. and Jermyn, I. H.}, |
| title |
= |
{Removing Shape-Preserving Transformations in Square-Root Elastic (SRE) Framework for Shape Analysis of Curves}, |
| year |
= |
{2007}, |
| month |
= |
{August}, |
| booktitle |
= |
{Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}, |
| address |
= |
{Ezhou, China}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_Joshi07b.pdf}, |
| keyword |
= |
{Shape, Reparameterization, Metric, Geodesic} |
| } |
Abstract :
This paper illustrates and extends an efficient framework, called the square-root-elastic (SRE) framework, for studying shapes of closed curves, that was first introduced in [2]. This framework combines the strengths of two important ideas - elastic shape metric and path-straightening methods - for finding geodesics in shape spaces of curves. The elastic metric allows for optimal matching of features between curves while path-straightening ensures that the algorithm results in geodesic paths. This paper extends this framework by removing two important shape preserving transformations: rotations and re-parameterizations, by forming quotient spaces and constructing geodesics on these quotient spaces. These ideas are demonstrated using experiments involving 2D and 3D curves. |
|
95 - Parametric blind deconvolution for confocal laser scanning microscopy.
P. Pankajakshan and B. Zhang and L. Blanc-Féraud and Z. Kam and J.C. Olivo-Marin and J. Zerubia. In Proc. 29th International Conference of IEEE EMBS (EMBC-07), pages 6531-6534, August 2007.
Keywords : Confocal microscopy, Blind Deconvolution, Poisson noise, Total variation, EM algorithm, Bayesian estimation.
Copyright : ©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
@INPROCEEDINGS{Pankajakshan07a,
|
| author |
= |
{Pankajakshan, P. and Zhang, B. and Blanc-Féraud, L. and Kam, Z. and Olivo-Marin, J.C. and Zerubia, J.}, |
| title |
= |
{Parametric blind deconvolution for confocal laser scanning microscopy}, |
| year |
= |
{2007}, |
| month |
= |
{August}, |
| booktitle |
= |
{Proc. 29th International Conference of IEEE EMBS (EMBC-07)}, |
| pages |
= |
{6531-6534}, |
| pdf |
= |
{http://ieeexplore.ieee.org/iel5/4352184/4352185/04353856.pdf?tp=&isnumber=&arnumber=4353856}, |
| keyword |
= |
{Confocal microscopy, Blind Deconvolution, Poisson noise, Total variation, EM algorithm, Bayesian estimation} |
| } |
Abstract :
In this paper, we propose a method for the
iterative restoration of fluorescence Confocal Laser Scanning
Microscopic (CLSM) images and parametric estimation of the
acquisition system’s Point Spread Function (PSF). The CLSM is
an optical fluorescence microscope that scans a specimen in 3D
and uses a pinhole to reject most of the out-of-focus light. However,
the quality of the images suffers from two basic physical
limitations. The diffraction-limited nature of the optical system,
and the reduced amount of light detected by the photomultiplier
cause blur and photon counting noise respectively. These images
can hence benefit from post-processing restoration methods
based on deconvolution. An efficient method for parametric
blind image deconvolution involves the simultaneous estimation
of the specimen 3D distribution of fluorescent sources and
the microscope PSF. By using a model for the microscope
image acquisition physical process, we reduce the number of
free parameters describing the PSF and introduce constraints.
The parameters of the PSF may vary during the course of
experimentation, and so they have to be estimated directly from
the observed data. A priori model of the specimen is further
applied to stabilize the alternate minimization algorithm and to
converge to the solutions. |
|
96 - Assessment of different classification algorithms for burnt land discrimination.
O. Zammit and X. Descombes and J. Zerubia. In Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pages 3000-3003, Barcelone, Spain, July 2007.
Keywords : Satellite images, Burnt areas, Support Vector Machines, Forest fires, Classification.
Copyright : IEEE
|
97 - A Hierarchical Texture Model for Unsupervised Segmentation of Remotely Sensed Images.
G. Scarpa and M. Haindl and J. Zerubia. In Scandinavian Conference on Image Analysis, Vol. 4522/2007, pages 303-312, series LNCS 4522, Ed. Springer Berlin / Heidelberg, Aalborg, Denmark, June 2007.
@INPROCEEDINGS{scarpa_scia_07,
|
| author |
= |
{Scarpa, G. and Haindl, M. and Zerubia, J.}, |
| title |
= |
{A Hierarchical Texture Model for Unsupervised Segmentation of Remotely Sensed Images}, |
| year |
= |
{2007}, |
| month |
= |
{June}, |
| booktitle |
= |
{Scandinavian Conference on Image Analysis}, |
| volume |
= |
{4522/2007}, |
| pages |
= |
{303-312}, |
| series |
= |
{LNCS 4522}, |
| editor |
= |
{Springer Berlin / Heidelberg}, |
| address |
= |
{Aalborg, Denmark}, |
| url |
= |
{http://link.springer.com/chapter/10.1007%2F978-3-540-73040-8_31}, |
| keyword |
= |
{} |
| } |
|
98 - A Novel Representation for Riemannian Analysis of Elastic Curves in R^n.
S. Joshi and E. Klassen and A. Srivastava and I. H. Jermyn. In Proc. IEEE Computer Vision and Pattern Recognition (CVPR), Minneapolis, USA, June 2007.
Keywords : Shape, Metric, Geodesic, Prior.
@INPROCEEDINGS{Joshi07a,
|
| author |
= |
{Joshi, S. and Klassen, E. and Srivastava, A. and Jermyn, I. H.}, |
| title |
= |
{A Novel Representation for Riemannian Analysis of Elastic Curves in R^n}, |
| year |
= |
{2007}, |
| month |
= |
{June}, |
| booktitle |
= |
{Proc. IEEE Computer Vision and Pattern Recognition (CVPR)}, |
| address |
= |
{Minneapolis, USA}, |
| url |
= |
{http://dx.doi.org/10.1109/CVPR.2007.383185}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_Joshi07a.pdf}, |
| keyword |
= |
{Shape, Metric, Geodesic, Prior} |
| } |
Abstract :
We propose an efficient representation for studying shapes of closed curves in R^n. This paper combines the strengths of two important ideas---elastic shape metric and path-straightening methods---and results in a very fast algorithm for finding geodesics in shape spaces. The elastic metric allows for optimal matching of features between the two curves while path-straightening ensures that the algorithm results in geodesic paths. For the novel representation proposed here, the elastic metric becomes the simple L^2 metric, in contrast to the past usage where more complex forms were used. We present the step-by-step algorithms for computing geodesics and demonstrate them with 2-D as well as 3-D examples. |
|
99 - Indexing Satellite Images with Features Computed from Man-Made Structures on the Earth’s Surface.
A. Bhattacharya and M. Roux and H. Maitre and I. H. Jermyn and X. Descombes and J. Zerubia. In Proc. International Workshop on Content-Based Multimedia Indexing, Bordeaux, France, June 2007.
Keywords : Indexation, Road network, Semantic, Retrieval, Feature statistics.
@INPROCEEDINGS{Bhattacharya07a,
|
| author |
= |
{Bhattacharya, A. and Roux, M. and Maitre, H. and Jermyn, I. H. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Indexing Satellite Images with Features Computed from Man-Made Structures on the Earth’s Surface}, |
| year |
= |
{2007}, |
| month |
= |
{June}, |
| booktitle |
= |
{Proc. International Workshop on Content-Based Multimedia Indexing}, |
| address |
= |
{Bordeaux, France}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_Bhattacharya07a.pdf}, |
| keyword |
= |
{Indexation, Road network, Semantic, Retrieval, Feature statistics} |
| } |
Abstract :
Indexing and retrieval from remote sensing image databases relies on the extraction of appropriate information from the data about the entity of interest (e.g. land cover type) and on the robustness of this extraction to nuisance variables. Other entities in an image may be strongly correlated with the entity of interest and their properties can therefore be used to characterize this entity. The road network contained in an image is one example. The properties of road networks vary considerably from one geographical environment to another, and they can therefore be used to classify and retrieve such environments. In this paper, we define several such environments, and classify them with the aid of geometrical and topological features computed from the road networks occurring in them. The relative failure of network extraction methods in certain types of urban area obliges us to segment such areas and to add a second set of geometrical and topological features computed from the segmentations. To validate the approach, feature selection and SVM linear kernel classification are performed on the feature set arising from a diverse image database. |
|
100 - Riemannian Analysis of Probability Density Functions with Applications in Vision.
S. Joshi and A. Srivastava and I. H. Jermyn. In Proc. IEEE Computer Vision and Pattern Recognition (CVPR), Minneapolis, USA, June 2007.
Keywords : Probability density function, Metric, Geodesic, Reparameterization.
@INPROCEEDINGS{Joshi07,
|
| author |
= |
{Joshi, S. and Srivastava, A. and Jermyn, I. H.}, |
| title |
= |
{Riemannian Analysis of Probability Density Functions with Applications in Vision}, |
| year |
= |
{2007}, |
| month |
= |
{June}, |
| booktitle |
= |
{Proc. IEEE Computer Vision and Pattern Recognition (CVPR)}, |
| address |
= |
{Minneapolis, USA}, |
| url |
= |
{http://dx.doi.org/10.1109/CVPR.2007.383188 }, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_Joshi07.pdf}, |
| keyword |
= |
{Probability density function, Metric, Geodesic, Reparameterization} |
| } |
Abstract :
Applications in computer vision involve statistically analyzing an important class of constrained, non- negative functions, including probability density functions (in texture analysis), dynamic time-warping functions (in activity analysis), and re-parametrization or non-rigid registration functions (in shape analysis of curves). For this one needs to impose a Riemannian structure on the spaces formed by these functions. We propose a em spherical version of the Fisher-Rao metric that provides closed form expressions for geodesics and distances, and allows an efficient computation of statistics. We compare this metric with some previously used metrics and present an application in planar shape classification. |
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