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Basis Algorithm

The Experts below are selected from a list of 1536 Experts worldwide ranked by ideXlab platform

Kaamran Raahemifar – 1st expert on this subject based on the ideXlab platform

  • ICIP – An Efficient Architecture for Entropy-Based Best-Basis Algorithm
    2006 International Conference on Image Processing, 2006
    Co-Authors: S.m. Aroutchelvame, Kaamran Raahemifar

    Abstract:

    In this work, we propose an architecture for the wavelet-packet based best-Basis Algorithm for images using Shannon entropy as cost function. The Algorithm for the logarithm implementation for integers using Taylor series is also proposed to implement Shannon entropy. The proposed architecture includes the architectures for the best-tree selection. The execution time of the proposed hardware for the best-Basis Algorithm for images is compared to its software implementation. The proposed best-Basis architecture has been described in VHDL at the RTL level, simulated successfully for its functional correctness and implemented in an FPGA.

  • An Architecture Design of Threshold-Based Best-Basis Algorithm
    2006 IEEE International Conference on Multimedia and Expo, 2006
    Co-Authors: S.m. Aroutchelvame, Kaamran Raahemifar

    Abstract:

    The best-Basis Algorithm has gained much importance on textured-based image compression and denoising of signals. In this paper, an architecture for the wavelet-packet based best-Basis Algorithm for images is proposed. The paper also describes the architecture for best-tree selection from 2D wavelet packet decomposition. The precision analysis of the proposed architecture is also discussed and the result shows that increase in the precision of input pixel greatly increases the signal-to-noise ratio (SNR) per pixel whereas increase in the precision of filter coefficient does not greatly help in improving the SNR value. The proposed architecture is described in VHDL at the RTL level, simulated successfully for its functional correctness and implemented in an FPGA

  • ICME – An Architecture Design of Threshold-Based Best-Basis Algorithm
    2006 IEEE International Conference on Multimedia and Expo, 2006
    Co-Authors: S.m. Aroutchelvame, Kaamran Raahemifar

    Abstract:

    The best-Basis Algorithm has gained much importance on textured-based image compression and denoising of signals. In this paper, an architecture for the wavelet-packet based best-Basis Algorithm for images is proposed. The paper also describes the architecture for best-tree selection from 2D wavelet packet decomposition. The precision analysis of the proposed architecture is also discussed and the result shows that increase in the precision of input pixel greatly increases the Signal-to-Noise Ratio (SNR) per pixel whereas increase in the precision of filter coefficient does not greatly help in improving the SNR value. The proposed architecture is described in VHDL at the RTL level, simulated successfully for its functional correctness and implemented in an FPGA.

K. Hazaveh – 2nd expert on this subject based on the ideXlab platform

  • ICIP (1) – Optimized two-dimensional local discriminant Basis Algorithm
    Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), 2003
    Co-Authors: K. Hazaveh, Kaamran Raahemifar

    Abstract:

    Local discriminant Basis Algorithm (LDB) is a supervised scheme for feature extraction and nonstationary signal classification. Due to its fast computational time, O(n log n), and excellent time-frequency localization, it is a promising method for nonstationary signal analysis. An optimized version of local discriminant Basis (OLDB) has been recently proposed that emphasizes certain regions of interest in different classes using initial LDB features. The optimization process is particularly useful when background structures show high correlation with desired features in signal or image space as in mammograms. In this paper the performance of OLDB Algorithm is studied in a texture classification problem. Classification into more than two classes of signals or images is a challenging problem and OLDB is capable of obtaining 85% accuracy classifying grayscale 64/spl times/64 textured images into three classes using only the 280 top LDB features as studied in this paper.

  • Optimized local discriminant Basis Algorithm
    2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698), 2003
    Co-Authors: K. Hazaveh, Kaamran Raahemifar

    Abstract:

    Local discriminant bases method is a powerful Algorithmic framework for feature extraction and classification applications that is based on supervised training. It is considerably faster compared to more theoretically ideal feature extraction methods such as principal component analysis or projection pursuit. In this paper an optimization block is added to original local discriminant bases Algorithm to promote the difference between disjoint signal classes. This is done by optimally weighting the local discriminant Basis using steepest decent Algorithm. The proposed method is particularly useful when background features in the signal space show strong correlation with regions of interest in the signal as in mammograms for instance.

  • ICASSP (6) – Optimized local discriminant Basis Algorithm
    2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698), 2003
    Co-Authors: K. Hazaveh, Kaamran Raahemifar

    Abstract:

    Local discriminant bases method is a powerful Algorithmic framework for feature extraction and classification applications that is based on supervised training. It is considerably faster compared to more theoretically ideal feature extraction methods such as principal component analysis or projection pursuit. In this paper an optimization block is added to the original local discriminant bases Algorithm to promote the difference between disjoint signal classes. This is done by optimally weighting the local discriminant Basis using the steepest decent Algorithm. The proposed method is particularly useful when background features in the signal space show strong correlation with regions of interest in the signal as in mammograms for instance.

A.s. Willsky – 3rd expert on this subject based on the ideXlab platform

  • Best Basis Algorithm for signal enhancement
    1995 International Conference on Acoustics Speech and Signal Processing, 1995
    Co-Authors: H.k.s. Mallat, D. Donoho, A.s. Willsky

    Abstract:

    We propose a best Basis Algorithm for signal enhancement in white Gaussian noise. We base our search of best Basis on a criterion of minimal reconstruction error of the underlying signal. We subsequently compare our simple error criterion to the Stein (1981) unbiased risk estimator, and provide a substantiating example to demonstrate its performance. A review is also given of noise removal by thresholding and of wavepacket orthonormal bases.

  • ICASSP – Best Basis Algorithm for signal enhancement
    1995 International Conference on Acoustics Speech and Signal Processing, 1995
    Co-Authors: H.k.s. Mallat, D. Donoho, A.s. Willsky

    Abstract:

    We propose a best Basis Algorithm for signal enhancement in white Gaussian noise. We base our search of best Basis on a criterion of minimal reconstruction error of the underlying signal. We subsequently compare our simple error criterion to the Stein (1981) unbiased risk estimator, and provide a substantiating example to demonstrate its performance. A review is also given of noise removal by thresholding and of wavepacket orthonormal bases.

  • Robust multiscale representation of processes and optimal signal reconstruction
    Proceedings of IEEE-SP International Symposium on Time- Frequency and Time-Scale Analysis, 1994
    Co-Authors: H. Krim, J.-c. Pesquet, A.s. Willsky

    Abstract:

    We propose a statistical approach to obtain a “best Basis” representation of an observed random process. We derive statistical properties of a criterion first proposed to determine the best wavelet packet Basis, and, proceed to use it in constructing a statistically sound Algorithm. For signal enhancement, this best Basis Algorithm is followed by a nonlinear filter based on the minimum description length (MDL) criterion. We show that it is equivalent to a min-max based Algorithm proposed by Donoho and Johnstone (1992).