2012 Visual Communications and Image Processing, 2012
Abstract This paper presents a novel 2D-TO-3D conversion approach from a monoscopic 2D image sequ... more Abstract This paper presents a novel 2D-TO-3D conversion approach from a monoscopic 2D image sequence. We propose a particle filter framework for recursive recovery of point-wise depth from feature correspondences matched through image sequences. We formulate a novel 2D dynamics model for recursive depth estimation with the combination of camera model, structure model and translation model. The proposed method utilizes edge-detection-assisted scale-invariant features to avoid lack of edge features in scale-invariant features ( ...
2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008
Abstract In this paper, we present two density estimation methods based on constrained expectatio... more Abstract In this paper, we present two density estimation methods based on constrained expectation-maximization (EM) algorithm. We propose a penalty-based maximum-entropy expectation-maximization (MEEM) algorithm to obtain a smooth estimate of the density function. We further propose an attraction-repulsion expectation-maximization (AREM) algorithm for density estimation in order to determine equilibrium between over-smoothing and over-fitting of the estimated density function. Computer simulation results are used to ...
Abstract. In this paper, we investigate methods for optimal morphological pattern recognition. Th... more Abstract. In this paper, we investigate methods for optimal morphological pattern recognition. The task of optimal pattern recognition is posed as a solution to a hypothesis testing problem. A minimum probability of error decision rule—maximum a posteriori filter—is sought. The classical solution to the minimum probability of error hypothesis testing problem, in the presence of independent and identically distributed noise degradation, is provided by template matching (TM). A modification of this task, seeking a solution to the minimum ...
IEEE transactions on pattern analysis and machine intelligence, 2014
In the past decade, great efforts have been made to extend linear discriminant analysis for highe... more In the past decade, great efforts have been made to extend linear discriminant analysis for higher-order data classification, generally referred to as multilinear discriminant analysis (MDA). Existing examples include general tensor discriminant analysis (GTDA) and discriminant analysis with tensor representation (DATER). Both the two methods attempt to resolve the problem of tensor mode dependency by iterative approximation. GTDA is known to be the first MDA method that converges over iterations. However, its performance relies highly on the tuning of the parameter in the scatter difference criterion. Although DATER usually results in better classification performance, it does not converge, yet the number of iterations executed has a direct impact on DATER's performance. In this paper, we propose a closed-form solution to the scatter difference objective in GTDA, namely, direct GTDA (DGTDA) which also gets rid of parameter tuning. We demonstrate that DGTDA outperforms GTDA in t...
In this paper, we develop a new algorithm to estimate an unknown probability density function giv... more In this paper, we develop a new algorithm to estimate an unknown probability density function given a finite data sample using a tree shaped kernel density estimator. The algorithm formulates an integrated squared error based cost function which minimizes the quadratic divergence between the kernel density and the Parzen density estimate. The cost function reduces to a quadratic programming problem which is minimized within the maximum entropy framework. The maximum entropy principle acts as a regularizer which yields a smooth solution. A smooth density estimate enables better generalization to unseen data and offers distinct advantages in high dimensions and cases where there is limited data. We demonstrate applications of the hierarchical kernel density estimator for function interpolation and texture segmentation problems. When applied to function interpolation, the kernel density estimator improves performance considerably in situations where the posterior conditional density of the dependent variable is multimodal. The kernel density estimator allows flexible non parametric modeling of textures which improves performance in texture segmentation algorithms. We demonstrate performance on a text labeling problem which shows performance of the algorithm in high dimensions. The hierarchical nature of the density estimator enables multiresolution solutions depending on the complexity of the data. The algorithm is fast and has at most quadratic scaling in the number of kernels.
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013
ABSTRACT Game-theoretic methods based on Nash equilibria have been widely used in various fields ... more ABSTRACT Game-theoretic methods based on Nash equilibria have been widely used in various fields including signal processing and communication applications such as cognitive radio systems, sensor networks, defense networks and gene regulatory networks. Solving the Nash equilibria, however, has been proven to be a difficult problem, in general. It is therefore desired to obtain efficient algorithms for solving the Nash equilibria in various special cases. In this paper, we propose a Compressed-Sensing Game Theory (CSGT) framework to solve the Nash equilibria. We demonstrate that the proposed CSGT framework provides a polynomial complexity solution to the Nash Equilibria, thus allowing more general pay-off functions for certain classes of two-player dynamic games. We also provide numerical examples that demonstrate the efficiency of proposed CSGT framework in solving the Nash equilibria for two-player games in comparison to existing algorithms.
2009 16th IEEE International Conference on Image Processing (ICIP), 2009
Abstract In this paper, we develop novel solutions for particle filtering on graphs. An exact sol... more Abstract In this paper, we develop novel solutions for particle filtering on graphs. An exact solution of particle filtering for conditional density propagation on directed cycle-free graphs is performed by a sequential updating scheme in a predetermined order. We also provide an approximate solution for particle filtering on general graphs by splitting the graphs with cycles into multiple directed cycle-free subgraphs. We utilize the proposed solution for distributed multiple object tracking. Experimental results show the improved performance ...
2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008
Abstract This paper presents a novel particle allocation approach to particle filtering for artic... more Abstract This paper presents a novel particle allocation approach to particle filtering for articulated object tracking which minimizes the total tracking distortion given a fixed number of particles over a video sequence. Under the framework of decentralized articulated object tracking, we propose the dynamic proposal variance and optimal particle number allocation algorithm for articulated object tracking to allocate particles among different parts of the articulated object as well as different frames. Experimental results show the superior ...
Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1989
Abstract A general theory for the morphological representation of discrete and binary images is p... more Abstract A general theory for the morphological representation of discrete and binary images is presented. The theory relies on the generation of a set of nonoverlapping segments of an image by repeated erosions and set transformations, which in turn produces a decomposition that guarantees exact reconstruction. The morphological image-representation transform and its properties are examined, with focus on the relationship between the transform and some existing shape-analysis tools, thus introducing the ...
2012 19th IEEE International Conference on Image Processing, 2012
ABSTRACT In this paper, we propose a novel statistical estimation algorithm to stochastic context... more ABSTRACT In this paper, we propose a novel statistical estimation algorithm to stochastic context-sensitive grammars (SCSGs). First, we show that a SCSG model can be solved by decomposing it into several causal stochastic context-free grammars (SCFGs) models and each of these SCFGs models can be solved simultaneously using a fully synchronous distributed computing framework. An alternate updating scheme based approximate solution to multiple SCFGs is also provided under the assumption of a realistic sequential computing framework. A series of statistical algorithms are expected to learn SCFGs subsequently. The SCSGs can be then used to represent multiple-trajectory. Experimental results demonstrate the improved performance of our method compared with existing methods for multiple-trajectory classification.
Proceedings., International Conference on Image Processing, 1995
Abstract The method of projections onto convex sets has been used effectively in the solution man... more Abstract The method of projections onto convex sets has been used effectively in the solution many important signal and image processing applications. This method however fails in circumstances when the desired constraints form non-convex sets. A new approach to image recovery based on a stochastic projections method is presented. At its core the method relies on the iteration of projections onto closed sets (not necessarily convex). A stochastic parameter is used to update the projections among the images in their ...
2012 Visual Communications and Image Processing, 2012
Abstract In this paper, we propose a novel statistical estimation algorithm to stochastic context... more Abstract In this paper, we propose a novel statistical estimation algorithm to stochastic context-sensitive grammars (SCSGs). First, we show that the SCSGs model can be solved by decomposing it into several causal stochastic context-free grammars (SCFGs) models and each of these SCFGs models can be solved simultaneously using a fully synchronous distributed computing framework. An alternate updating scheme based approximate solution to multiple SCFGs is also provided under the assumption of a realistic sequential ...
2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, 2009
Abstract Summary form only given. This talk will explore the use of motion information in video s... more Abstract Summary form only given. This talk will explore the use of motion information in video surveillance. We will focus primarily on retrieval and activity recognition of motion events in video and sensor databases characterized by multiple interactive motion trajectories. We will present a framework based on tensor decomposition for indexing and retrieval of multiple motion trajectories in video databases. An efficient method for extraction and insertion of partial information in video databases used for multiple motion trajectory ...
2012 Visual Communications and Image Processing, 2012
Abstract This paper presents a novel 2D-TO-3D conversion approach from a monoscopic 2D image sequ... more Abstract This paper presents a novel 2D-TO-3D conversion approach from a monoscopic 2D image sequence. We propose a particle filter framework for recursive recovery of point-wise depth from feature correspondences matched through image sequences. We formulate a novel 2D dynamics model for recursive depth estimation with the combination of camera model, structure model and translation model. The proposed method utilizes edge-detection-assisted scale-invariant features to avoid lack of edge features in scale-invariant features ( ...
2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008
Abstract In this paper, we present two density estimation methods based on constrained expectatio... more Abstract In this paper, we present two density estimation methods based on constrained expectation-maximization (EM) algorithm. We propose a penalty-based maximum-entropy expectation-maximization (MEEM) algorithm to obtain a smooth estimate of the density function. We further propose an attraction-repulsion expectation-maximization (AREM) algorithm for density estimation in order to determine equilibrium between over-smoothing and over-fitting of the estimated density function. Computer simulation results are used to ...
Abstract. In this paper, we investigate methods for optimal morphological pattern recognition. Th... more Abstract. In this paper, we investigate methods for optimal morphological pattern recognition. The task of optimal pattern recognition is posed as a solution to a hypothesis testing problem. A minimum probability of error decision rule—maximum a posteriori filter—is sought. The classical solution to the minimum probability of error hypothesis testing problem, in the presence of independent and identically distributed noise degradation, is provided by template matching (TM). A modification of this task, seeking a solution to the minimum ...
IEEE transactions on pattern analysis and machine intelligence, 2014
In the past decade, great efforts have been made to extend linear discriminant analysis for highe... more In the past decade, great efforts have been made to extend linear discriminant analysis for higher-order data classification, generally referred to as multilinear discriminant analysis (MDA). Existing examples include general tensor discriminant analysis (GTDA) and discriminant analysis with tensor representation (DATER). Both the two methods attempt to resolve the problem of tensor mode dependency by iterative approximation. GTDA is known to be the first MDA method that converges over iterations. However, its performance relies highly on the tuning of the parameter in the scatter difference criterion. Although DATER usually results in better classification performance, it does not converge, yet the number of iterations executed has a direct impact on DATER's performance. In this paper, we propose a closed-form solution to the scatter difference objective in GTDA, namely, direct GTDA (DGTDA) which also gets rid of parameter tuning. We demonstrate that DGTDA outperforms GTDA in t...
In this paper, we develop a new algorithm to estimate an unknown probability density function giv... more In this paper, we develop a new algorithm to estimate an unknown probability density function given a finite data sample using a tree shaped kernel density estimator. The algorithm formulates an integrated squared error based cost function which minimizes the quadratic divergence between the kernel density and the Parzen density estimate. The cost function reduces to a quadratic programming problem which is minimized within the maximum entropy framework. The maximum entropy principle acts as a regularizer which yields a smooth solution. A smooth density estimate enables better generalization to unseen data and offers distinct advantages in high dimensions and cases where there is limited data. We demonstrate applications of the hierarchical kernel density estimator for function interpolation and texture segmentation problems. When applied to function interpolation, the kernel density estimator improves performance considerably in situations where the posterior conditional density of the dependent variable is multimodal. The kernel density estimator allows flexible non parametric modeling of textures which improves performance in texture segmentation algorithms. We demonstrate performance on a text labeling problem which shows performance of the algorithm in high dimensions. The hierarchical nature of the density estimator enables multiresolution solutions depending on the complexity of the data. The algorithm is fast and has at most quadratic scaling in the number of kernels.
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013
ABSTRACT Game-theoretic methods based on Nash equilibria have been widely used in various fields ... more ABSTRACT Game-theoretic methods based on Nash equilibria have been widely used in various fields including signal processing and communication applications such as cognitive radio systems, sensor networks, defense networks and gene regulatory networks. Solving the Nash equilibria, however, has been proven to be a difficult problem, in general. It is therefore desired to obtain efficient algorithms for solving the Nash equilibria in various special cases. In this paper, we propose a Compressed-Sensing Game Theory (CSGT) framework to solve the Nash equilibria. We demonstrate that the proposed CSGT framework provides a polynomial complexity solution to the Nash Equilibria, thus allowing more general pay-off functions for certain classes of two-player dynamic games. We also provide numerical examples that demonstrate the efficiency of proposed CSGT framework in solving the Nash equilibria for two-player games in comparison to existing algorithms.
2009 16th IEEE International Conference on Image Processing (ICIP), 2009
Abstract In this paper, we develop novel solutions for particle filtering on graphs. An exact sol... more Abstract In this paper, we develop novel solutions for particle filtering on graphs. An exact solution of particle filtering for conditional density propagation on directed cycle-free graphs is performed by a sequential updating scheme in a predetermined order. We also provide an approximate solution for particle filtering on general graphs by splitting the graphs with cycles into multiple directed cycle-free subgraphs. We utilize the proposed solution for distributed multiple object tracking. Experimental results show the improved performance ...
2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008
Abstract This paper presents a novel particle allocation approach to particle filtering for artic... more Abstract This paper presents a novel particle allocation approach to particle filtering for articulated object tracking which minimizes the total tracking distortion given a fixed number of particles over a video sequence. Under the framework of decentralized articulated object tracking, we propose the dynamic proposal variance and optimal particle number allocation algorithm for articulated object tracking to allocate particles among different parts of the articulated object as well as different frames. Experimental results show the superior ...
Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1989
Abstract A general theory for the morphological representation of discrete and binary images is p... more Abstract A general theory for the morphological representation of discrete and binary images is presented. The theory relies on the generation of a set of nonoverlapping segments of an image by repeated erosions and set transformations, which in turn produces a decomposition that guarantees exact reconstruction. The morphological image-representation transform and its properties are examined, with focus on the relationship between the transform and some existing shape-analysis tools, thus introducing the ...
2012 19th IEEE International Conference on Image Processing, 2012
ABSTRACT In this paper, we propose a novel statistical estimation algorithm to stochastic context... more ABSTRACT In this paper, we propose a novel statistical estimation algorithm to stochastic context-sensitive grammars (SCSGs). First, we show that a SCSG model can be solved by decomposing it into several causal stochastic context-free grammars (SCFGs) models and each of these SCFGs models can be solved simultaneously using a fully synchronous distributed computing framework. An alternate updating scheme based approximate solution to multiple SCFGs is also provided under the assumption of a realistic sequential computing framework. A series of statistical algorithms are expected to learn SCFGs subsequently. The SCSGs can be then used to represent multiple-trajectory. Experimental results demonstrate the improved performance of our method compared with existing methods for multiple-trajectory classification.
Proceedings., International Conference on Image Processing, 1995
Abstract The method of projections onto convex sets has been used effectively in the solution man... more Abstract The method of projections onto convex sets has been used effectively in the solution many important signal and image processing applications. This method however fails in circumstances when the desired constraints form non-convex sets. A new approach to image recovery based on a stochastic projections method is presented. At its core the method relies on the iteration of projections onto closed sets (not necessarily convex). A stochastic parameter is used to update the projections among the images in their ...
2012 Visual Communications and Image Processing, 2012
Abstract In this paper, we propose a novel statistical estimation algorithm to stochastic context... more Abstract In this paper, we propose a novel statistical estimation algorithm to stochastic context-sensitive grammars (SCSGs). First, we show that the SCSGs model can be solved by decomposing it into several causal stochastic context-free grammars (SCFGs) models and each of these SCFGs models can be solved simultaneously using a fully synchronous distributed computing framework. An alternate updating scheme based approximate solution to multiple SCFGs is also provided under the assumption of a realistic sequential ...
2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, 2009
Abstract Summary form only given. This talk will explore the use of motion information in video s... more Abstract Summary form only given. This talk will explore the use of motion information in video surveillance. We will focus primarily on retrieval and activity recognition of motion events in video and sensor databases characterized by multiple interactive motion trajectories. We will present a framework based on tensor decomposition for indexing and retrieval of multiple motion trajectories in video databases. An efficient method for extraction and insertion of partial information in video databases used for multiple motion trajectory ...
Uploads
Papers by Dan Schonfeld