The Experts below are selected from a list of 13149 Experts worldwide ranked by ideXlab platform

Tamas Linder - One of the best experts on this subject based on the ideXlab platform.

  • ITW - Lossy transmission of correlated sources over two-way channels
    2017 IEEE Information Theory Workshop (ITW), 2017
    Co-Authors: Jian-jia Weng, Fady Alajaji, Tamas Linder
    Abstract:

    Achievability and converse results for the lossy transmission of correlated sources over Shannon's two-way channels (TWCs) are presented. A joint source-channel coding theorem for independent sources and TWCs for which adaptation cannot enlarge the capacity region is also established. We further investigate the optimality of scalar coding for TWCs with discrete modulo additive noise as well as additive white Gaussian noise. Comparing the Distortion of scalar coding with the derived bounds, we observe that scalar coding achieves the Minimum Distortion over both families of TWCs for independent and uniformly distributed sources and independent Gaussian sources.

  • On the training Distortion of vector quantizers
    IEEE Transactions on Information Theory, 2000
    Co-Authors: Tamas Linder
    Abstract:

    The in-training-set performance of a vector quantizer as a function of its training set size is investigated. For squared error Distortion and independent training data, worst case type upper bounds are derived on the Minimum training Distortion achieved by an empirically optimal quantizer. These bounds show that the training Distortion can underestimate the Minimum Distortion of a truly optimal quantizer by as much as a constant times n/sup -1/2/, where n is the size of the training data. Earlier results provide lower bounds of the same order.

Jian Ren - One of the best experts on this subject based on the ideXlab platform.

  • non uniform information transmission for Minimum Distortion in wireless networks
    Lecture Notes in Computer Science, 2006
    Co-Authors: Huahui Wang, Jian Ren
    Abstract:

    This paper considers average input-output Distortion minimization through joint optimization of source index assignment and modulation design. First, we derive the general optimization criterion, and discuss the possibility of simultaneous minimization of bit-error-rate (BER) and Distortion. Secondly, we propose a novel source-aware information transmission approach by exploiting the non-uniformity in Gray-coded constellations. The proposed approach makes it possible for simultaneous BER and Distortion minimization and outperforms the existing schemes with big margin when the original geometric structure of the quantization codebook can not be maintained in information transmission. The simplicity and power efficiency of the proposed source-aware non-uniform information scheme make it particularly attractive for systems with tight power constraints, such as wireless sensor networks and space communications.

Daniel Cremers - One of the best experts on this subject based on the ideXlab platform.

  • Elastic net constraints for shape matching
    Proceedings of the IEEE International Conference on Computer Vision, 2013
    Co-Authors: Emanuele Rodolà, Andrea Torsello, Yasuo Kuniyoshi, Tatsuya Harada, Daniel Cremers
    Abstract:

    We consider a parametrized relaxation of the widely adopted quadratic \nassignment problem (QAP) formulation for Minimum Distortion correspondence \nbetween deformable shapes. In order to control the accuracy/sparsity trade-off \nwe introduce a weighting parameter on the combination of two existing \nrelaxations, namely spectral and game-theoretic. This leads to the introduction \nof the elastic net penalty function into shape matching problems. In combination \nwith an efficient algorithm to project onto the elastic net ball, we obtain an \napproach for deformable shape matching with controllable sparsity. Experiments \non a standard benchmark confirm the effectiveness of the approach.

  • ICCV - Elastic Net Constraints for Shape Matching
    2013 IEEE International Conference on Computer Vision, 2013
    Co-Authors: Emanuele Rodolà, Andrea Torsello, Yasuo Kuniyoshi, Tatsuya Harada, Daniel Cremers
    Abstract:

    We consider a parametrized relaxation of the widely adopted quadratic assignment problem (QAP) formulation for Minimum Distortion correspondence between deformable shapes. In order to control the accuracy/sparsity trade-off we introduce a weighting parameter on the combination of two existing relaxations, namely spectral and game-theoretic. This leads to the introduction of the elastic net penalty function into shape matching problems. In combination with an efficient algorithm to project onto the elastic net ball, we obtain an approach for deformable shape matching with controllable sparsity. Experiments on a standard benchmark confirm the effectiveness of the approach.

Nikos Paragios - One of the best experts on this subject based on the ideXlab platform.

  • SSVM - Discrete Minimum Distortion correspondence problems for non-rigid shape matching
    Lecture Notes in Computer Science, 2012
    Co-Authors: Chaohui Wang, Alexander M. Bronstein, Michael M. Bronstein, Nikos Paragios
    Abstract:

    Similarity and correspondence are two fundamental archetype problems in shape analysis, encountered in numerous application in computer vision and pattern recognition. Many methods for shape similarity and correspondence boil down to the Minimum-Distortion correspondence problem, in which two shapes are endowed with certain structure, and one attempts to find the matching with smallest structure Distortion between them. Defining structures invariant to some class of shape transformations results in an invariant Minimum-Distortion correspondence or similarity. In this paper, we model shapes using local and global structures, formulate the invariant correspondence problem as binary graph labeling, and show how different choice of structure results in invariance under various classes of deformations.

  • discrete Minimum Distortion correspondence problems for non rigid shape matching
    International Conference on Scale Space and Variational Methods in computer vision, 2011
    Co-Authors: Chaohui Wang, Alexander M. Bronstein, Michael M. Bronstein, Nikos Paragios
    Abstract:

    Similarity and correspondence are two fundamental archetype problems in shape analysis, encountered in numerous application in computer vision and pattern recognition. Many methods for shape similarity and correspondence boil down to the Minimum-Distortion correspondence problem, in which two shapes are endowed with certain structure, and one attempts to find the matching with smallest structure Distortion between them. Defining structures invariant to some class of shape transformations results in an invariant Minimum-Distortion correspondence or similarity. In this paper, we model shapes using local and global structures, formulate the invariant correspondence problem as binary graph labeling, and show how different choice of structure results in invariance under various classes of deformations.

  • Discrete Minimum Distortion correspondence problems for non-rigid shape matching
    2010
    Co-Authors: Chaohui Wang, Michael M. Bronstein, Nikos Paragios
    Abstract:

    Similarity and correspondence are two fundamental archetype problems in shape analysis, encountered in numerous application in computer vision and pattern recognition. Many methods for shape similarity and correspondence boil down to the Minimum-Distortion correspondence problem, in which two shapes are endowed with certain structure, and one attempts to find the matching with smallest structure Distortion between them. Defining structures invariant to some class of shape transformations results in an invariant Minimum-Distortion correspondence or similarity. In this paper, we model shapes using local and global structures and formulate the invariant correspondence problem as binary graph labeling. We perform challenging non-rigid shape matching experiments, and show how different choice of structure results in invariance under various classes of deformations.

Pierre Duhamel - One of the best experts on this subject based on the ideXlab platform.

  • filter bank design for Minimum Distortion in presence of subband quantization
    International Conference on Acoustics Speech and Signal Processing, 1996
    Co-Authors: K. Gosse, Moreau F De Saintmartin, Pierre Duhamel
    Abstract:

    This paper presents source coding schemes, based on both parallel and tree-structured filter banks, in which synthesis filters and quantizers are jointly optimized under bit-rate constraint. The design of reconstruction filter banks thus takes into account the amount of subband quantization noise, and minimizes the output mean square error (MSE). All other constraints on the filters, such as perfect reconstruction or maximum selectivity, are relaxed, and noticeable improvements over perfect reconstruction schemes with optimal bit-rate allocation are shown by means of rate-Distortion curves, for both synthetic and audio signals.

  • Optimizing the synthesis filter bank in audio coding for Minimum Distortion using a frequency weighted psychoacoustic criterion
    Proceedings of 1995 Workshop on Applications of Signal Processing to Audio and Accoustics, 1995
    Co-Authors: K. Gosse, O. Pothier, Pierre Duhamel
    Abstract:

    This paper shows the improvement brought in filter-bank based audio coding schemes by relaxing the constraints of perfect reconstruction or maximum frequency selectivity on the synthesis filter bank: here, the coding scheme is optimized according to a frequency weighted psychoacoustic criterion. This improvement is then evaluated by means of rate/Distortion curves (the weighted criterion being the Distortion measure). This approach does not take frequency masking effects into account but is compatible with their use.