The Experts below are selected from a list of 25662 Experts worldwide ranked by ideXlab platform

Laurent Kneip - One of the best experts on this subject based on the ideXlab platform.

  • a certifiably Globally Optimal Solution to generalized essential matrix estimation
    Computer Vision and Pattern Recognition, 2020
    Co-Authors: Ji Zhao, Laurent Kneip
    Abstract:

    We present a convex optimization approach for generalized essential matrix (GEM) estimation. The six-point minimal solver for the GEM has poor numerical stability and applies only for a minimal number of points. Existing non-minimal solvers for GEM estimation rely on either local optimization or relinearization techniques, which impedes high accuracy in common scenarios. Our proposed non-minimal solver minimizes the sum of squared residuals by reformulating the problem as a quadratically constrained quadratic program. The Globally Optimal Solution is thus obtained by a semidefinite relaxation. The algorithm retrieves certifiably Globally Optimal Solutions to the original non-convex problem in polynomial time. We also provide the necessary and sufficient conditions to recover the Optimal GEM from the relaxed problems. The improved performance is demonstrated over experiments on both synthetic and real multi-camera systems.

  • CVPR - A Certifiably Globally Optimal Solution to Generalized Essential Matrix Estimation
    2020 IEEE CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020
    Co-Authors: Ji Zhao, Laurent Kneip
    Abstract:

    We present a convex optimization approach for generalized essential matrix (GEM) estimation. The six-point minimal solver for the GEM has poor numerical stability and applies only for a minimal number of points. Existing non-minimal solvers for GEM estimation rely on either local optimization or relinearization techniques, which impedes high accuracy in common scenarios. Our proposed non-minimal solver minimizes the sum of squared residuals by reformulating the problem as a quadratically constrained quadratic program. The Globally Optimal Solution is thus obtained by a semidefinite relaxation. The algorithm retrieves certifiably Globally Optimal Solutions to the original non-convex problem in polynomial time. We also provide the necessary and sufficient conditions to recover the Optimal GEM from the relaxed problems. The improved performance is demonstrated over experiments on both synthetic and real multi-camera systems.

  • a certifiably Globally Optimal Solution to the non minimal relative pose problem
    Computer Vision and Pattern Recognition, 2018
    Co-Authors: Jesus Briales, Laurent Kneip, Javier Gonzalezjimenez
    Abstract:

    Finding the relative pose between two calibrated views ranks among the most fundamental geometric vision problems. It therefore appears as somewhat a surprise that a Globally Optimal solver that minimizes a properly defined energy over non-minimal correspondence sets and in the original space of relative transformations has yet to be discovered. This, notably, is the contribution of the present paper. We formulate the problem as a Quadratically Constrained Quadratic Program (QCQP), which can be converted into a Semidefinite Program (SDP) using Shor's convex relaxation. While a theoretical proof for the tightness of this relaxation remains open, we prove through exhaustive validation on both simulated and real experiments that our approach always finds and certifies (a-posteriori) the global optimum of the cost function.

  • CVPR - A Certifiably Globally Optimal Solution to the Non-minimal Relative Pose Problem
    2018 IEEE CVF Conference on Computer Vision and Pattern Recognition, 2018
    Co-Authors: Jesus Briales, Laurent Kneip, Javier Gonzalez-jimenez
    Abstract:

    Finding the relative pose between two calibrated views ranks among the most fundamental geometric vision problems. It therefore appears as somewhat a surprise that a Globally Optimal solver that minimizes a properly defined energy over non-minimal correspondence sets and in the original space of relative transformations has yet to be discovered. This, notably, is the contribution of the present paper. We formulate the problem as a Quadratically Constrained Quadratic Program (QCQP), which can be converted into a Semidefinite Program (SDP) using Shor's convex relaxation. While a theoretical proof for the tightness of this relaxation remains open, we prove through exhaustive validation on both simulated and real experiments that our approach always finds and certifies (a-posteriori) the global optimum of the cost function.

Jun Sese - One of the best experts on this subject based on the ideXlab platform.

  • semi supervised local fisher discriminant analysis for dimensionality reduction
    Machine Learning, 2010
    Co-Authors: Masashi Sugiyama, Shinichi Nakajima, Jun Sese
    Abstract:

    When only a small number of labeled samples are available, supervised dimensionality reduction methods tend to perform poorly because of overfitting. In such cases, unlabeled samples could be useful in improving the performance. In this paper, we propose a semi-supervised dimensionality reduction method which preserves the global structure of unlabeled samples in addition to separating labeled samples in different classes from each other. The proposed method, which we call SEmi-supervised Local Fisher discriminant analysis (SELF), has an analytic form of the Globally Optimal Solution and it can be computed based on eigen-decomposition. We show the usefulness of SELF through experiments with benchmark and real-world document classification datasets.

  • Semi-supervised local Fisher discriminant analysis for dimensionality reduction
    Machine Learning, 2009
    Co-Authors: Masashi Sugiyama, Tsuyoshi Idé, Shinichi Nakajima, Jun Sese
    Abstract:

    When only a small number of labeled samples are available, supervised dimensionality reduction methods tend to perform poorly because of overfitting. In such cases, unlabeled samples could be useful in improving the performance. In this paper, we propose a semi-supervised dimensionality reduction method which preserves the global structure of unlabeled samples in addition to separating labeled samples in different classes from each other. The proposed method, which we call SEmi-supervised Local Fisher discriminant analysis (SELF), has an analytic form of the Globally Optimal Solution and it can be computed based on eigen-decomposition. We show the usefulness of SELF through experiments with benchmark and real-world document classification datasets.

  • semi supervised local fisher discriminant analysis for dimensionality reduction
    Knowledge Discovery and Data Mining, 2008
    Co-Authors: Masashi Sugiyama, Shinichi Nakajima, Jun Sese
    Abstract:

    When only a small number of labeled samples are available, supervised dimensionality reduction methods tend to perform poorly due to overfitting. In such cases, unlabeled samples could be useful in improving the performance. In this paper, we propose a semi-supervised dimensionality reduction method which preserves the global structure of unlabeled samples in addition to separating labeled samples in different classes from each other. The proposed method has an analytic form of the Globally Optimal Solution and it can be computed based on eigendecompositions. Therefore, the proposed method is computationally reliable and efficient. We show the effectiveness of the proposed method through extensive simulations with benchmark data sets.

Yifeng Zhou - One of the best experts on this subject based on the ideXlab platform.

  • a Globally Optimal Solution to maximum likelihood bearing only geolocation
    Vehicular Technology Conference, 2017
    Co-Authors: Yifeng Zhou
    Abstract:

    In this paper, an optimization technique is developed for providing the Globally Optimal Solution to the maximum likelihood (ML) method for the bearing-only geolocation problem. The ML formulation does not require the a priori knowledge of the distances between the sensor and the emitter. It is formulated as a non-concave fractional programming problem, and a branch and bound algorithm is developed for solving for the Globally Optimal Solution. The algorithm has the property of global convergence and the advantage of computational efficiency. Computer simulations are used to demonstrate the performance of the proposed techniques and comparisons to other methods and the Cramer-Rao lower bounds (CRLBs) are also provided.

  • VTC-Fall - A Globally Optimal Solution to Maximum Likelihood Bearing-Only Geolocation
    2017 IEEE 86th Vehicular Technology Conference (VTC-Fall), 2017
    Co-Authors: Yifeng Zhou
    Abstract:

    In this paper, an optimization technique is developed for providing the Globally Optimal Solution to the maximum likelihood (ML) method for the bearing-only geolocation problem. The ML formulation does not require the a priori knowledge of the distances between the sensor and the emitter. It is formulated as a non-concave fractional programming problem, and a branch and bound algorithm is developed for solving for the Globally Optimal Solution. The algorithm has the property of global convergence and the advantage of computational efficiency. Computer simulations are used to demonstrate the performance of the proposed techniques and comparisons to other methods and the Cramer-Rao lower bounds (CRLBs) are also provided.

Sorina Dumitrescu - One of the best experts on this subject based on the ideXlab platform.

  • unequal erasure protection technique for scalable multistreams
    IEEE Transactions on Image Processing, 2010
    Co-Authors: Sorina Dumitrescu, G Rivers, S Shirani
    Abstract:

    This paper presents a novel unequal erasure protection (UEP) strategy for the transmission of scalable data, formed by interleaving independently decodable and scalable streams, over packet erasure networks. The technique, termed multistream UEP (M-UEP), differs from the traditional UEP strategy by: 1) placing separate streams in separate packets to establish independence and 2) using permuted systematic Reed-Solomon codes to enhance the distribution of message symbols amongst the packets. M-UEP improves upon UEP by ensuring that all received source symbols are decoded. The R-D Optimal redundancy allocation problem for M-UEP is formulated and its Globally Optimal Solution is shown to have a time complexity of O(2N N(L+1)N+1) , where N is the number of packets and L is the packet length. To address the high complexity of the Globally Optimal Solution, an efficient subOptimal algorithm is proposed which runs in O(N 2 L 2) time. The proposed M-UEP algorithm is applied on SPIHT coded images in conjunction with an appropriate grouping of wavelet coefficients into streams. The experimental results reveal that M-UEP consistently outperforms the traditional UEP reaching peak improvements of 0.6 dB. Moreover, our tests show that M-UEP is more robust than UEP in adverse channel conditions.

  • lagrangian global optimization of two description scalar quantizers
    International Symposium on Information Theory, 2004
    Co-Authors: Sorina Dumitrescu, Xiaolin Wu
    Abstract:

    We develop an efficient Lagrangian-type algorithm for Optimal two-description fixed-rate scalar quantizer design, for a very large class of distortion measures. Our key result is the discovery that the Lagrangian multiplier for the Globally Optimal Solution exists. Although Lagrangian optimization is a method of choice for quantizer design, none of the previous algorithms using this method was shown to guarantee the global Optimality for any instance of the problem

  • ISIT - Lagrangian global optimization of two-description scalar quantizers
    International Symposium onInformation Theory 2004. ISIT 2004. Proceedings., 1
    Co-Authors: Sorina Dumitrescu
    Abstract:

    We develop an efficient Lagrangian-type algorithm for Optimal two-description fixed-rate scalar quantizer design, for a very large class of distortion measures. Our key result is the discovery that the Lagrangian multiplier for the Globally Optimal Solution exists. Although Lagrangian optimization is a method of choice for quantizer design, none of the previous algorithms using this method was shown to guarantee the global Optimality for any instance of the problem

Jorge Batista - One of the best experts on this subject based on the ideXlab platform.

  • Globally Optimal Solution to multi object tracking with merged measurements
    International Conference on Computer Vision, 2011
    Co-Authors: Joao F Henriques, Rui Caseiro, Jorge Batista
    Abstract:

    Multiple object tracking has been formulated recently as a global optimization problem, and solved efficiently with Optimal methods such as the Hungarian Algorithm. A severe limitation is the inability to model multiple objects that are merged into a single measurement, and track them as a group, while retaining Optimality. This work presents a new graph structure that encodes these multiple-match events as standard one-to-one matches, allowing computation of the Solution in polynomial time. Since identities are lost when objects merge, an efficient method to identify groups is also presented, as a flow circulation problem. The problem of tracking individual objects across groups is then posed as a standard Optimal assignment. Experiments show increased performance on the PETS 2006 and 2009 datasets compared to state-of-the-art algorithms.

  • ICCV - Globally Optimal Solution to multi-object tracking with merged measurements
    2011 International Conference on Computer Vision, 2011
    Co-Authors: Joao F Henriques, Rui Caseiro, Jorge Batista
    Abstract:

    Multiple object tracking has been formulated recently as a global optimization problem, and solved efficiently with Optimal methods such as the Hungarian Algorithm. A severe limitation is the inability to model multiple objects that are merged into a single measurement, and track them as a group, while retaining Optimality. This work presents a new graph structure that encodes these multiple-match events as standard one-to-one matches, allowing computation of the Solution in polynomial time. Since identities are lost when objects merge, an efficient method to identify groups is also presented, as a flow circulation problem. The problem of tracking individual objects across groups is then posed as a standard Optimal assignment. Experiments show increased performance on the PETS 2006 and 2009 datasets compared to state-of-the-art algorithms.