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Publications de A. Srivastava
Résultat de la recherche dans la liste des publications :
2 Articles |
1 - Shape Analysis of Elastic Curves in Euclidean Spaces.
S. Joshi et E. Klassen et W. Liu et I. H. Jermyn et A. Srivastava. IEEE Trans. Pattern Analysis and Machine Intelligence, 33(7): pages 1415-1428, 2010.
Note : to appear
Mots-clés : shape analysis, elastic deformations, Riemannian elastic metric.
@ARTICLE{Joshi2010,
|
| author |
= |
{Joshi, S. and Klassen, E. and Liu, W. and Jermyn, I. H. and Srivastava, A.}, |
| title |
= |
{Shape Analysis of Elastic Curves in Euclidean Spaces}, |
| year |
= |
{2010}, |
| journal |
= |
{IEEE Trans. Pattern Analysis and Machine Intelligence}, |
| volume |
= |
{33}, |
| number |
= |
{7}, |
| pages |
= |
{1415-1428}, |
| note |
= |
{to appear}, |
| pdf |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5601739}, |
| keyword |
= |
{shape analysis, elastic deformations, Riemannian elastic metric} |
| } |
|
2 - Looking for shapes in two-dimensional, cluttered point clouds.
A. Srivastava et I. H. Jermyn. IEEE Trans. Pattern Analysis and Machine Intelligence, 31(9): pages 1616-1629, septembre 2009.
Mots-clés : Forme, Bayesian, Point cloud, Diffeomorphism, Sampling, Fisher-Rao.
Copyright : ©2008 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.
@ARTICLE{SrivastavaJermyn09,
|
| author |
= |
{Srivastava, A. and Jermyn, I. H.}, |
| title |
= |
{Looking for shapes in two-dimensional, cluttered point clouds}, |
| year |
= |
{2009}, |
| month |
= |
{septembre}, |
| journal |
= |
{IEEE Trans. Pattern Analysis and Machine Intelligence}, |
| volume |
= |
{31}, |
| number |
= |
{9}, |
| pages |
= |
{1616-1629}, |
| url |
= |
{http://dx.doi.org/10.1109/TPAMI.2008.223}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/SrivastavaJermyn09.pdf}, |
| keyword |
= |
{Forme, Bayesian, Point cloud, Diffeomorphism, Sampling, Fisher-Rao} |
| } |
Abstract :
We study the problem of identifying shape classes in point clouds. These clouds contain sampled contours and are
corrupted by clutter and observation noise. Taking an analysis-by-synthesis approach, we simulate high-probability configurations of
sampled contours using models learnt from training data to evaluate the given test data. To facilitate simulations, we develop statistical
models for sources of (nuisance) variability: (i) shape variations within classes, (ii) variability in sampling continuous curves, (iii) pose
and scale variability, (iv) observation noise, and (v) points introduced by clutter. The variability in sampling closed curves into finite
points is represented by positive diffeomorphisms of a unit circle. We derive probability models on these functions using their squareroot
forms and the Fisher-Rao metric. Using a Monte Carlo approach, we simulate configurations from a joint prior on the shape-sample
space and compare them to the data using a likelihood function. Average likelihoods of simulated configurations lead to estimates of
posterior probabilities of different classes and, hence, Bayesian classification. |
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4 Articles de conférence |
1 - Removing Shape-Preserving Transformations in Square-Root Elastic (SRE) Framework for Shape Analysis of Curves.
S. Joshi et E. Klassen et A. Srivastava et I. H. Jermyn. Dans Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), Ezhou, China, août 2007.
Mots-clés : Forme, Reparameterization, Metrique, 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 |
= |
{août}, |
| 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 |
= |
{Forme, Reparameterization, Metrique, 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. |
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2 - A Novel Representation for Riemannian Analysis of Elastic Curves in R^n.
S. Joshi et E. Klassen et A. Srivastava et I. H. Jermyn. Dans Proc. IEEE Computer Vision and Pattern Recognition (CVPR), Minneapolis, USA, juin 2007.
Mots-clés : Forme, Metrique, Geodesic, A priori.
@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 |
= |
{juin}, |
| 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 |
= |
{Forme, Metrique, Geodesic, A priori} |
| } |
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. |
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3 - Riemannian Analysis of Probability Density Functions with Applications in Vision.
S. Joshi et A. Srivastava et I. H. Jermyn. Dans Proc. IEEE Computer Vision and Pattern Recognition (CVPR), Minneapolis, USA, juin 2007.
Mots-clés : Probability density function, Metrique, 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 |
= |
{juin}, |
| 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, Metrique, 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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4 - Tree Species Classification Using Radiometry, Texture and Shape Based Features.
M. S. Kulikova et M. Mani et A. Srivastava et X. Descombes et J. Zerubia. Dans Proc. European Signal Processing Conference (EUSIPCO), 2007.
Mots-clés : shape based features, SVM, tree classification.
@INPROCEEDINGS{Kulikova07,
|
| author |
= |
{Kulikova, M. S. and Mani, M. and Srivastava, A. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Tree Species Classification Using Radiometry, Texture and Shape Based Features}, |
| year |
= |
{2007}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/docs/00/46/55/05/PDF/Kulikova_EUSIPCO2007.pdf}, |
| keyword |
= |
{shape based features, SVM, tree classification} |
| } |
Abstract :
We consider the problem of tree species classification from high resolution aerial images based on radiometry, texture and a shape modeling. We use the notion of shape space proposed by Klassen et al., which provides a shape description invariant to translation, rotation and scaling. The shape features are extracted within a geodesic distance in the shape space. We then perform a classification using a SVM approach. We are able to show that the shape descriptors improve the classification performance relative to a classifier based on radiometric and textural descriptors alone. We obtain these results using high resolution Colour InfraRed (CIR) aerial images provided by the Swedish University of Agricultural Sciences. The image viewpoint is close to the nadir, i.e. the tree crowns are seen from above. |
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