The Experts below are selected from a list of 5226 Experts worldwide ranked by ideXlab platform

Rainer Schuler - One of the best experts on this subject based on the ideXlab platform.

  • Universal distributions and time-bounded Kolmogorov complexity
    Lecture Notes in Computer Science, 1999
    Co-Authors: Rainer Schuler
    Abstract:

    The equivalence of the universal distribution, the a Priori Probability and the negative exponential of Kolmogorov complexity is a well known result. The natural analogs of Kolmogorov complexity and of a Priori Probability in the time-bounded setting are not efficiently computable under reasonable assumptions. In contrast, it is known that for every polynomial p, distributions universal for the class of p-time computable distributions can be computed in polynomial time. We show that in the time-bounded setting the universal distribution gives rise to sensible notions of Kolmogorov complexity and of a Priori Probability.

  • STACS - Universal distributions and time-bounded kolmogorov complexity
    STACS 99, 1999
    Co-Authors: Rainer Schuler
    Abstract:

    The equivalence of the universal distribution, the a Priori Probability and the negative exponential of Kolmogorov complexity is a well known result. The natural analogs of Kolmogorov complexity and of a Priori Probability in the time-bounded setting are not efficiently computable under reasonable assumptions. In contrast, it is known that for every polynomial p, distributions universal for the class of p-time computable distributions can be computed in polynomial time. We show that in the time-bounded setting the universal distribution gives rise to sensible notions of Kolmogorov complexity and of a Priori Probability.

Yuncai Liu - One of the best experts on this subject based on the ideXlab platform.

  • Reduce false positives for object detection by a Priori Probability in videos
    Neurocomputing, 2016
    Co-Authors: Lei Wang, Xu Zhao, Yuncai Liu
    Abstract:

    In this work, we address the problem of reducing the false positives for object detection in videos. We employ the motion cue to build a foreground Probability model. Then the mean expectation of the pixel-level foreground Probability is computed to assign a Priori Probability to the sliding window in detection. The proposed foreground model is evaluated with the detection framework of Deformable Part Models (DPM). We combine the response of DPM detector and the mean Probability expectation to form the features and train a linear classifier. The proposed approach is threshold-free, and reduces the false positives in object detection by the foreground cues. Besides, we describe an integral Probability image for fast computation of the mean Probability expectation. Experimental results show that the proposed method achieve superior performance over the baseline of Deformable Part Models.

  • reduce false positives for human detection by a Priori Probability in videos
    Asian Conference on Pattern Recognition, 2015
    Co-Authors: Lei Wang, Xu Zhao, Yuncai Liu
    Abstract:

    In this work, we address the problem of reducing the false positives for human detection in videos. We employ the motion cue to build a foreground Probability model. Then the mean expectation of the pixel-level foreground Probability is computed to assign a Priori Probability to the sliding window in detection. We combine the response of Deformable Part Models and the mean Probability expectation to form the features and train a linear classifier. The proposed approach is threshold-free, and reduces the false positives in human detection by the foreground cues. As well, we describe an integral Probability image for fast computation of the mean Probability expectation. Experimental results show that the proposed method achieve superior performance over the baseline of Deformable Part Models.

  • ACPR - Reduce false positives for human detection by a Priori Probability in videos
    2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), 2015
    Co-Authors: Lei Wang, Xu Zhao, Yuncai Liu
    Abstract:

    In this work, we address the problem of reducing the false positives for human detection in videos. We employ the motion cue to build a foreground Probability model. Then the mean expectation of the pixel-level foreground Probability is computed to assign a Priori Probability to the sliding window in detection. We combine the response of Deformable Part Models and the mean Probability expectation to form the features and train a linear classifier. The proposed approach is threshold-free, and reduces the false positives in human detection by the foreground cues. As well, we describe an integral Probability image for fast computation of the mean Probability expectation. Experimental results show that the proposed method achieve superior performance over the baseline of Deformable Part Models.

Wynn C. Stirling - One of the best experts on this subject based on the ideXlab platform.

  • ICASSP - A novel approach to time-varying spectral Probability estimation
    ICASSP '87. IEEE International Conference on Acoustics Speech and Signal Processing, 1
    Co-Authors: R. Muir, Wynn C. Stirling
    Abstract:

    A robust method of spectral estimation is examined which uses a decision-directed empirical Bayes receiver to estimate the time-varying a Priori Probability of signal occurrence in a given bin of the FFT of a signal. The a Priori Probability of spectral content for each bin is modeled as a finite-state Markov chain and an exact, recursive, nonlinear least squares estimator is employed to estimate the current state of the Markov process, and consequently the marginal Probability for each of the bins. A generalized Bayes likelihood ratio test (GLRT) is used as the detector which feeds decisions to the state estimator. The estimate of the states is used along with a Markov Probability vector to generate estimates of the a Priori Probability for use in the GLRT. Receiver operating characteristic curves are generated to illustrate algorithm performance.

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

Lei Wang - One of the best experts on this subject based on the ideXlab platform.

  • Reduce false positives for object detection by a Priori Probability in videos
    Neurocomputing, 2016
    Co-Authors: Lei Wang, Xu Zhao, Yuncai Liu
    Abstract:

    In this work, we address the problem of reducing the false positives for object detection in videos. We employ the motion cue to build a foreground Probability model. Then the mean expectation of the pixel-level foreground Probability is computed to assign a Priori Probability to the sliding window in detection. The proposed foreground model is evaluated with the detection framework of Deformable Part Models (DPM). We combine the response of DPM detector and the mean Probability expectation to form the features and train a linear classifier. The proposed approach is threshold-free, and reduces the false positives in object detection by the foreground cues. Besides, we describe an integral Probability image for fast computation of the mean Probability expectation. Experimental results show that the proposed method achieve superior performance over the baseline of Deformable Part Models.

  • reduce false positives for human detection by a Priori Probability in videos
    Asian Conference on Pattern Recognition, 2015
    Co-Authors: Lei Wang, Xu Zhao, Yuncai Liu
    Abstract:

    In this work, we address the problem of reducing the false positives for human detection in videos. We employ the motion cue to build a foreground Probability model. Then the mean expectation of the pixel-level foreground Probability is computed to assign a Priori Probability to the sliding window in detection. We combine the response of Deformable Part Models and the mean Probability expectation to form the features and train a linear classifier. The proposed approach is threshold-free, and reduces the false positives in human detection by the foreground cues. As well, we describe an integral Probability image for fast computation of the mean Probability expectation. Experimental results show that the proposed method achieve superior performance over the baseline of Deformable Part Models.

  • ACPR - Reduce false positives for human detection by a Priori Probability in videos
    2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), 2015
    Co-Authors: Lei Wang, Xu Zhao, Yuncai Liu
    Abstract:

    In this work, we address the problem of reducing the false positives for human detection in videos. We employ the motion cue to build a foreground Probability model. Then the mean expectation of the pixel-level foreground Probability is computed to assign a Priori Probability to the sliding window in detection. We combine the response of Deformable Part Models and the mean Probability expectation to form the features and train a linear classifier. The proposed approach is threshold-free, and reduces the false positives in human detection by the foreground cues. As well, we describe an integral Probability image for fast computation of the mean Probability expectation. Experimental results show that the proposed method achieve superior performance over the baseline of Deformable Part Models.