The Experts below are selected from a list of 7776 Experts worldwide ranked by ideXlab platform

J. S. Duncan - One of the best experts on this subject based on the ideXlab platform.

  • Point-tracked quantitative analysis of left ventricular surface motion from 3-D image sequences
    IEEE Transactions on Medical Imaging, 2000
    Co-Authors: Albert J. Sinusas, E Ritman, R. Todd Constable, J. S. Duncan
    Abstract:

    Proposes and validates the hypothesis that one can use differential shape properties of the myocardial surfaces to recover dense field motion from standard three-dimensional (3-D) image sequences (MRI and CT). Quantitative measures of left ventricular regional function can be further inferred from the Point Correspondence maps. The noninvasive, algorithm-derived results are validated on two levels. First, the motion trajectories are compared to those of implanted imaging-opaque markers of a canine model in two imaging modalities, where subpixel accuracy is achieved. Second, the validity of using motion parameters (path length and thickness changes) for detecting myocardial injury area is tested by comparing algorithms derived results to postmortem analysis TTC staining of myocardial tissue, where the achieved Pearson product-moment correlation value is 0.968.

  • Point-tracked quantitative analysis of left ventricular surface motion from 3-D image sequence
    IEEE Transactions on Medical Imaging, 2000
    Co-Authors: P. Shi, E Ritman, R. Todd Constable, Albert J. Sinusas, J. S. Duncan
    Abstract:

    We propose and validate the hypothesis that we can use differential shape properties of the myocardial surfaces to recover dense field motion from standard three-dimensional (3-D) image sequences (MRI and CT). Quantitative measures of left ventricular regional function can be further inferred from the Point Correspondence maps. The noninvasive, algorithm-derived results are validated on two levels. First, the motion trajectories are compared to those of implanted imaging-opaque markers of a canine model in two imaging modalities, where subpixel accuracy is achieved. Second, the validity of using motion parameters (path length and thickness changes) for detecting myocardial injury area is tested by comparing algorithms derived results to postmortem analysis TTC staining of myocardial tissue, where the achieved Pearson product-moment correlation value is 0.968.

Mubarak Shah - One of the best experts on this subject based on the ideXlab platform.

  • multiframe many many Point Correspondence for vehicle tracking in high density wide area aerial videos
    International Journal of Computer Vision, 2013
    Co-Authors: Imran Saleemi, Mubarak Shah
    Abstract:

    This paper presents a novel framework for tracking thousands of vehicles in high resolution, low frame rate, multiple camera aerial videos. The proposed algorithm avoids the pitfalls of global minimization of data association costs and instead maintains multiple object-centric associations for each track. Representation of object state in terms of many to many data associations per track is proposed and multiple novel constraints are introduced to make the association problem tractable while allowing sharing of detections among tracks. Weighted hypothetical measurements are introduced to better handle occlusions, mis-detections and split or merged detections. A two-frame differencing method is presented which performs simultaneous moving object detection in both. Two novel contextual constraints of vehicle following model, and discouragement of track intersection and merging are also proposed. Extensive experiments on challenging, ground truthed data sets are performed to show the feasibility and superiority of the proposed approach. Results of quantitative comparison with existing approaches are presented, and the efficacy of newly introduced constraints is experimentally established. The proposed algorithm performs better and faster than global, 1---1 data association methods.

  • a noniterative greedy algorithm for multiframe Point Correspondence
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005
    Co-Authors: Khurram Shafique, Mubarak Shah
    Abstract:

    This work presents a framework for finding Point Correspondences in monocular image sequences over multiple frames. The general problem of multiframe Point Correspondence is NP-hard for three or more frames. A polynomial time algorithm for a restriction of this problem is presented and is used as the basis of the proposed greedy algorithm for the general problem. The greedy nature of the proposed algorithm allows it to be used in real-time systems for tracking and surveillance, etc. In addition, the proposed algorithm deals with the problems of occlusion, missed detections, and false positives by using a single noniterative greedy optimization scheme and, hence, reduces the complexity of the overall algorithm as compared to most existing approaches where multiple heuristics are used for the same purpose. While most greedy algorithms for Point tracking do not allow the entry and exit of the Points from the scene, this is not a limitation for the proposed algorithm. Experiments with real and synthetic data over a wide range of scenarios and system parameters are presented to validate the claims about the performance of the proposed algorithm.

Hong Qiao - One of the best experts on this subject based on the ideXlab platform.

  • Point Correspondence by a new third order graph matching algorithm
    Pattern Recognition, 2017
    Co-Authors: Xu Yang, Hong Qiao
    Abstract:

    The Correspondence between Point sets is a fundamental problem in pattern recognition, which is often formulated and solved by graph matching. In this paper, we propose to solve the Correspondence problem by a new third order graph matching algorithm. Compared with some previous hyper-graph matching algorithms, the proposed one achieves considerable memory reduction and is applicable to both undirected and directed graphs. Specifically, the Correspondence is formulated by the matching between adjacency tensors encoding the third order structural information of each graph, which is then transformed to be a tractable matrix form. Two types of gradient based optimization methods, the graduated nonconvexity and concavity procedure (GNCCP) and graduated assignment (GA) algorithm, are generalized to solve the problem. Comparative experiments with state-of-the-art algorithms on both synthetic and real data witness the effectiveness of the proposed method. HighlightsAn adjacency tensor based third order graph matching algorithm is proposed.It enjoys a much lower storage complexity than affinity tensor based high order algorithms.Instead of spectral decomposition based optimization, it adopts gradient based optimization.Experiments on synthetic and real-world data witness its state-of-the-art performance.

  • Point Correspondence by a new third order graph matching algorithm
    Pattern Recognition, 2017
    Co-Authors: Xu Yang, Hong Qiao, Zhiyong Liu
    Abstract:

    The Correspondence between Point sets is a fundamental problem in pattern recognition, which is often formulated and solved by graph matching. In this paper, we propose to solve the Correspondence problem by a new third order graph matching algorithm. Compared with some previous hyper-graph matching algorithms, the proposed one achieves considerable memory reduction and is applicable to both undirected and directed graphs. Specifically, the Correspondence is formulated by the matching between adjacency tensors encoding the third order structural information of each graph, which is then transformed to be a tractable matrix form. Two types of gradient based optimization methods, the graduated nonconvexity and concavity procedure (GNCCP) and graduated assignment (GA) algorithm, are generalized to solve the problem. Comparative experiments with state-of-the-art algorithms on both synthetic and real data witness the effectiveness of the proposed method.

  • a graph matching based key Point Correspondence method for lunar surface images
    World Congress on Intelligent Control and Automation, 2016
    Co-Authors: Yuren Zhang, Hong Qiao, Xu Yang, Baofeng Wang
    Abstract:

    Key Point Correspondence plays an important role in lunar surface image processing. Since lunar surface images often contain obvious illumination changes, noisy Points and repetitive patterns, traditional appearance based algorithms may fail when local appearance descriptors become less distinctive. In this paper, we introduce a graph matching based algorithm to tackle this problem. First, by incorporating structural information, key Point sets in lunar surface images are represented by graphs. Then key Point Correspondence is formulated as a specific graph matching problem which aims to find a specified number of best assignments, and effectively approximately solved. Finally, an outlier assignment elimination method is proposed based on the affine invariance assumption. Simulations on both benchmark datasets and lunar surface images witness the effectiveness of the proposed method.

  • outlier robust Point Correspondence based on gnccp
    Pattern Recognition Letters, 2015
    Co-Authors: Xu Yang, Hong Qiao
    Abstract:

    Point Correspondence is formulated as the minimization of a quadratic objective.It directly targets at finding a specified number of best vertex assignments.GNCCP, a combinatorial optimization framework, is used to optimize the objective.The method can be applied to both undirected and directed graphs. Graph matching is a fundamental problem in pattern recognition and computer vision. In this paper we introduce a novel graph matching algorithm to find the specified number of best vertex assignments between two labeled weighted graphs. The problem is first explicitly formulated as the minimization of a quadratic objective function and then solved by an optimization algorithm based on the recently proposed graduated nonconvexity and concavity procedure (GNCCP). Simulations on both synthetic data and real world images witness the effectiveness of the proposed method.

Quangtuan Luong - One of the best experts on this subject based on the ideXlab platform.

Xu Yang - One of the best experts on this subject based on the ideXlab platform.

  • Point Correspondence by a new third order graph matching algorithm
    Pattern Recognition, 2017
    Co-Authors: Xu Yang, Hong Qiao
    Abstract:

    The Correspondence between Point sets is a fundamental problem in pattern recognition, which is often formulated and solved by graph matching. In this paper, we propose to solve the Correspondence problem by a new third order graph matching algorithm. Compared with some previous hyper-graph matching algorithms, the proposed one achieves considerable memory reduction and is applicable to both undirected and directed graphs. Specifically, the Correspondence is formulated by the matching between adjacency tensors encoding the third order structural information of each graph, which is then transformed to be a tractable matrix form. Two types of gradient based optimization methods, the graduated nonconvexity and concavity procedure (GNCCP) and graduated assignment (GA) algorithm, are generalized to solve the problem. Comparative experiments with state-of-the-art algorithms on both synthetic and real data witness the effectiveness of the proposed method. HighlightsAn adjacency tensor based third order graph matching algorithm is proposed.It enjoys a much lower storage complexity than affinity tensor based high order algorithms.Instead of spectral decomposition based optimization, it adopts gradient based optimization.Experiments on synthetic and real-world data witness its state-of-the-art performance.

  • Point Correspondence by a new third order graph matching algorithm
    Pattern Recognition, 2017
    Co-Authors: Xu Yang, Hong Qiao, Zhiyong Liu
    Abstract:

    The Correspondence between Point sets is a fundamental problem in pattern recognition, which is often formulated and solved by graph matching. In this paper, we propose to solve the Correspondence problem by a new third order graph matching algorithm. Compared with some previous hyper-graph matching algorithms, the proposed one achieves considerable memory reduction and is applicable to both undirected and directed graphs. Specifically, the Correspondence is formulated by the matching between adjacency tensors encoding the third order structural information of each graph, which is then transformed to be a tractable matrix form. Two types of gradient based optimization methods, the graduated nonconvexity and concavity procedure (GNCCP) and graduated assignment (GA) algorithm, are generalized to solve the problem. Comparative experiments with state-of-the-art algorithms on both synthetic and real data witness the effectiveness of the proposed method.

  • a graph matching based key Point Correspondence method for lunar surface images
    World Congress on Intelligent Control and Automation, 2016
    Co-Authors: Yuren Zhang, Hong Qiao, Xu Yang, Baofeng Wang
    Abstract:

    Key Point Correspondence plays an important role in lunar surface image processing. Since lunar surface images often contain obvious illumination changes, noisy Points and repetitive patterns, traditional appearance based algorithms may fail when local appearance descriptors become less distinctive. In this paper, we introduce a graph matching based algorithm to tackle this problem. First, by incorporating structural information, key Point sets in lunar surface images are represented by graphs. Then key Point Correspondence is formulated as a specific graph matching problem which aims to find a specified number of best assignments, and effectively approximately solved. Finally, an outlier assignment elimination method is proposed based on the affine invariance assumption. Simulations on both benchmark datasets and lunar surface images witness the effectiveness of the proposed method.

  • outlier robust Point Correspondence based on gnccp
    Pattern Recognition Letters, 2015
    Co-Authors: Xu Yang, Hong Qiao
    Abstract:

    Point Correspondence is formulated as the minimization of a quadratic objective.It directly targets at finding a specified number of best vertex assignments.GNCCP, a combinatorial optimization framework, is used to optimize the objective.The method can be applied to both undirected and directed graphs. Graph matching is a fundamental problem in pattern recognition and computer vision. In this paper we introduce a novel graph matching algorithm to find the specified number of best vertex assignments between two labeled weighted graphs. The problem is first explicitly formulated as the minimization of a quadratic objective function and then solved by an optimization algorithm based on the recently proposed graduated nonconvexity and concavity procedure (GNCCP). Simulations on both synthetic data and real world images witness the effectiveness of the proposed method.