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Displaying 1-29 out of 29 total
Riemannian Analysis of Probability Density Functions with Applications in Vision
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Anuj Srivastava, Ian Jermyn, Shantanu Joshi
Issue Date:June 2007
pp. 1-8
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-parametr...
 
A Novel Representation for Riemannian Analysis of Elastic Curves in Rn
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Shantanu H. Joshi, Eric Klassen, Anuj Srivastava, Ian Jermyn
Issue Date:June 2007
pp. 1-7
We propose a novel representation of continuous, closed curves in Rn that is quite efficient for analyzing their shapes. We combine the strengths of two important ideas - elastic shape metric and path-straightening methods - in shape analysis and present a...
 
2D Affine and Projective Shape Analysis
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Darshan Bryner,Eric Klassen,Huiling Le,Anuj Srivastava
Issue Date:May 2014
pp. 1-1
Current techniques for shape analysis tend to seek invariance to similarity transformations (rotation, translation, and scale), but certain imaging situations require invariance to larger groups, such as affine or projective groups. Here we present a gener...
 
Parallel Transport of Deformations in Shape Space of Elastic Surfaces
Found in: 2013 IEEE International Conference on Computer Vision (ICCV)
By Qian Xie,Sebastian Kurtek,Huiling Le,Anuj Srivastava
Issue Date:December 2013
pp. 865-872
Statistical shape analysis develops methods for comparisons, deformations, summarizations, and modeling of shapes in given data sets. These tasks require a fundamental tool called parallel transport of tangent vectors along arbitrary paths. This tool is es...
 
Structure-based RNA Function Prediction Using Elastic Shape Analysis
Found in: Bioinformatics and Biomedicine, IEEE International Conference on
By Jose Laborde,Anuj Srivastava,Jinfeng Zhang
Issue Date:November 2011
pp. 16-21
In recent years, RNAs have been found to have diverse functions beyond being a messenger in gene transcription. The functions of non-coding RNAs are determined by their structures. Structure comparison/alignment of RNAs provides an effective means to predi...
 
Blurring-invariant Riemannian metrics for comparing signals and images
Found in: Computer Vision, IEEE International Conference on
By Zhengwu Zhang,Eric Klassen,Anuj Srivastava,Pavan Turaga,Rama Chellappa
Issue Date:November 2011
pp. 1770-1775
We propose a novel Riemannian framework for comparing signals and images in a manner that is invariant to their levels of blur. This framework uses a log-Fourier representation of signals/images in which the set of all possible Gaussian blurs of a signal, ...
 
Detection of Shapes in 2D Point Clouds Generated from Images
Found in: Pattern Recognition, International Conference on
By Jingyong Su, Zhiqiang Zhu, Anuj Srivastava, Fred Huffer
Issue Date:August 2010
pp. 2640-2643
We present a novel statistical framework for detecting pre-determined shape classes in 2D cluttered point clouds, which are in turn extracted from images. In this model based approach, we use a 1D Poisson process for sampling points on shapes, a 2D Poisson...
 
Local 3D Shape Analysis for Facial Expression Recognition
Found in: Pattern Recognition, International Conference on
By Ahmed Maalej, Boulbaba Ben Amor, Mohamed Daoudi, Anuj Srivastava, Stefano Berretti
Issue Date:August 2010
pp. 4129-4132
We investigate the problem of facial expression recognition using 3D face data. Our approach is based on local shape analysis of several relevant regions of a given face scan. These regions or patches from facial surfaces are extracted and represented by s...
 
A novel riemannian framework for shape analysis of 3D objects
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Sebastian Kurtek, Eric Klassen, Zhaohua Ding, Anuj Srivastava
Issue Date:June 2010
pp. 1625-1632
In this paper we introduce a novel Riemannian framework for shape analysis of parameterized surfaces. We derive a distance function between any two surfaces that is invariant to rigid motion, global scaling, and re-parametrization. It is the last part that...
 
Guest Editors' Introduction to the Special Section on Shape Analysis and Its Applications in Image Understanding
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Anuj Srivastava, James N. Damon, Ian L. Dryden, Ian H. Jermyn
Issue Date:April 2010
pp. 577-578
No summary available.
 
A Framework of Calculus on Facial Surfaces
Found in: Image Analysis and Processing Workshops, International Conference of
By Chafik Samir, Mohamed Daoudi, Anuj Srivastava
Issue Date:September 2007
pp. 27-32
Facial surfaces play an important role in different ap- plications such as computer graphics and biometric. A few works have been proposed to study the space of facial surfaces. In this paper, we represent a facial surface as a path on the space of closed ...
 
Jump-Diffusion Processes on Matrix Lie Groups for Bayesian Inference
Found in: Signal Processing Workshop on High-Order Statistics, IEEE
By Anuj Srivastava, Michael I. Miller, Ulf Grenander
Issue Date:June 1999
pp. 0126
A variety of engineering problems can be studied as inferences on constrained sets, Lie groups in particular. Additionally, the number of parameters to be estimated, namely the model-order, may also be unknown a-priori. We present a Bayesian approach by bu...
 
Statistical Computations on Grassmann and Stiefel Manifolds for Image and Video-Based Recognition
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Pavan Turaga,Ashok Veeraraghavan,Anuj Srivastava,Rama Chellappa
Issue Date:November 2011
pp. 2273-2286
In this paper, we examine image and video-based recognition applications where the underlying models have a special structure—the linear subspace structure. We discuss how commonly used parametric models for videos and image sets can be described using the...
 
Shape Analysis of Elastic Curves in Euclidean Spaces
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Anuj Srivastava, Eric Klassen, Shantanu H. Joshi, Ian H. Jermyn
Issue Date:July 2011
pp. 1415-1428
This paper introduces a square-root velocity (SRV) representation for analyzing shapes of curves in euclidean spaces under an elastic metric. In this SRV representation, the elastic metric simplifies to the {\hbox{\rlap{I}\kern 2.0pt{\hbox{L}}}}^2 metric, ...
 
Looking for Shapes in Two-Dimensional Cluttered Point Clouds
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Anuj Srivastava, Ian H. Jermyn
Issue Date:September 2009
pp. 1616-1629
We study the problem of identifying shape classes in point clouds. These clouds contain sampled points along contours and are corrupted by clutter and observation noise. Taking an analysis-by-synthesis approach, we simulate high-probability configurations ...
 
Statistical Shape Analysis: Clustering, Learning, and Testing
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Anuj Srivastava, Shantanu H. Joshi, Washington Mio, Xiuwen Liu
Issue Date:April 2005
pp. 590-602
Using a differential-geometric treatment of planar shapes, we present tools for: 1) hierarchical clustering of imaged objects according to the shapes of their boundaries, 2) learning of probability models for clusters of shapes, and 3) testing of newly obs...
 
Elastic-String Models for Representation and Analysis of Planar Shapes
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Washington Mio, Anuj Srivastava
Issue Date:July 2004
pp. 10-15
We develop a new framework for the quantitative analysis of shapes of planar curves. Shapes are modeled on elastic strings that can be bent, stretched or compressed at different rates along the curve. Shapes are treated as elements of a space obtained as t...
 
Optimal Linear Representations of Images for Object Recognition
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Xiuwen Liu, Anuj Srivastava, Kyle Gallivan
Issue Date:May 2004
pp. 662-666
<p><b>Abstract</b>—Although linear representations are frequently used in image analysis, their performances are seldom optimal in specific applications. This paper proposes a stochastic gradient algorithm for finding optimal linear repre...
 
Analysis of Planar Shapes Using Geodesic Paths on Shape Spaces
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Eric Klassen, Anuj Srivastava, Washington Mio, Shantanu H. Joshi
Issue Date:March 2004
pp. 372-383
<p><b>Abstract</b>—For analyzing shapes of planar, closed curves, we propose differential geometric representations of curves using their direction functions and curvature functions. Shapes are represented as elements of infinite-dimensio...
 
Statistical Search for Hierarchical Linear Optimal Representations of Images
Found in: Computer Vision and Pattern Recognition Workshop
By Qiang Zhang, Xiuwen Liu, Anuj Srivastava
Issue Date:June 2003
pp. 93
Although linear representations of images are widely used in appearance-based recognition of objects, the frequently used ideas, such as PCA, ICA, and FDA, are often found to be suboptimal. A stochastic search algorithm has been proposed recently [4] for f...
 
Optimal Linear Representations of Images for Object Recognition
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Xiuwen Liu, Anuj Srivastava, Kyle Gallivan
Issue Date:June 2003
pp. 229
Simplicity of linear representations (of images) makes them a popular tool in imaging analysis applications such as object recognition and image classification. Although several linear representations, namely PCA, ICA, and FDA, have frequently been used, t...
 
A Spectral Representation for Appearance-Based Classification and Recognition
Found in: Pattern Recognition, International Conference on
By Xiuwen Liu, Anuj Srivastava
Issue Date:August 2002
pp. 10037
We present a spectral representation for appearance-based image classification and object recognition. Based on a generative process, the representation is derived by partitioning the frequency domain into small disjoint regions. This gives rise to a set o...
 
Probability Models for Clutter in Natural Images
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Ulf Grenander, Anuj Srivastava
Issue Date:April 2001
pp. 424-429
<p><b>Abstract</b>—We propose a framework for modeling clutter in natural images. Assuming that: 1) images are made up of 2D (projected) views of 3D (real) objects and 2) certain simplifying conditions hold, we derive an analytical densit...
 
Hilbert-Schmidt Lower Bounds for Estimators on Matrix Lie Groups for ATR
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Ulf Grenander, Michael I. Miller, Anuj Srivastava
Issue Date:August 1998
pp. 790-802
<p><b>Abstract</b>—Deformable template representations of observed imagery, model the variability of target pose via the actions of the matrix Lie groups on rigid templates. In this paper, we study the construction of minimum mean squared...
 
An efficient multiple protein structure comparison method and its application to structure clustering and outlier detection
Found in: 2013 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
By Wei Wu,Anuj Srivastava,Jose Laborde,Jinfeng Zhang
Issue Date:December 2013
pp. 69-73
Despite many years of research, comparing multiple protein structures simultaneously (multiple structure comparison) is still a challenging problem. Most of the previous studies have focused on similarities among subsets of residues (or atoms) from a group...
   
2D Affine and Projective Shape Analysis
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Darshan Bryner,Eric Klassen,Huiling Le,Anuj Srivastava
Issue Date:October 2013
pp. 1
Current techniques for shape analysis tend to seek invariance to similarity transformations (rotation, translation and scale), but certain imaging situations require invariance to larger groups, such as affine or projective groups. Here we present a genera...
 
Universal Analytical Forms for Modeling Image Probabilities
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Anuj Srivastava, Xiuwen Liu, Ulf Grenander
Issue Date:September 2002
pp. 1200-1214
<p><b>Abstract</b>—Seeking probability models for images, we employ a spectral approach where the images are decomposed using bandpass filters and probability models are imposed on the filter outputs (also called spectral components). We ...
 
Elastic radial curves to model 3D facial deformations
Found in: Proceedings of the ACM workshop on 3D object retrieval (3DOR '10)
By Anuj Srivastava, Boulbaba Ben Amor, Hassen Drira, Mohamed Daoudi
Issue Date:October 2010
pp. 75-80
In this paper we study the challenging problem of 3D face recognition in presence of facial expressions. Therefore, the facial surfaces are represented by an indexed collections of radial curves on them, emanating from the nose tips. Facial shapes comparis...
     
Elastic Riemannian frameworks and statistical tools for shape analysis
Found in: Proceedings of the ACM workshop on 3D object retrieval (3DOR '10)
By Anuj Srivastava
Issue Date:October 2010
pp. 1-1
Interest in shapes of 3D objects naturally leads to shape analysis of curves and surfaces. The theme of this talk is Riemannian frameworks that offer certain distinct advantages. In addition to providing measures for shape comparisons, clustering and retri...
     
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