Wavelets have become a popular topic, especially in their use for image compression. Many papers have been written, but most are oriented toward mathematicians. This paper will present an application of the Daubechies...
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ISBN:
(纸本)9608052173
Wavelets have become a popular topic, especially in their use for image compression. Many papers have been written, but most are oriented toward mathematicians. This paper will present an application of the Daubechies wavelet for image compression with emphasis on the implementation and not on the mathematical analysis.
We study the problem of representing images within a multimedia Database Management System (DBMS), in order to support fast retrieval operations without compromising storage efficiency. To achieve this goal, we propos...
We study the problem of representing images within a multimedia Database Management System (DBMS), in order to support fast retrieval operations without compromising storage efficiency. To achieve this goal, we propose new image coding techniques which combine a wavelet representation, embedded coding of the wavelet coefficients, and segmentation of image-domain regions in the wavelet domain. A bitstream is generated in which each image region is encoded independently of other regions, without having to explicitly store information describing the regions. Simulation results show that our proposed algorithms achieve coding performance which compares favorably, both perceptually and objectively, to that achieved using state-of-the-art image/video coding techniques while additionally providing region-based support.
The space-time radar problem is well suited to the application of techniques that take advantage of the low-rank property of the space-time covariance matrix. In particular, it was shown that when the space-time covar...
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The space-time radar problem is well suited to the application of techniques that take advantage of the low-rank property of the space-time covariance matrix. In particular, it was shown that when the space-time covariance matrix is estimated from a dataset with limited support, reduced-rank methods outperform full-rank space-time adaptive proc.ssing (STAP). We study the application of several reduced-rank methods to the STAP problem and demonstrate their utility by simulations in terms of the output signal-to-noise ratio and detection probability. It is shown that reduced-rank proc.ssing has two opposite effects on the performance: increased statistical stability which tends to improve performance, and introduction of a bias which lowers the signal-to-noise ratio. Several reduced-rank methods are analyzed and compared for both cases of known and unknown covariance matrix. While the best performance is obtained using transforms based on the eigendecomposition (data dependent), the loss incurred by the application of fixed transforms (such as the discrete cosine transform) is relatively small. The main advantage of fixed transforms is the availability of efficient computational proc.dures for their implementation. These findings suggest that reduced-rank methods could facilitate the development of practical, real-time STAP technology.
This paper proposes a novel method of speaker normalization by means of input space optimization for continuous density hidden Markov models (CDHMM). The parameters of a linear feature transformation function are so d...
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ISBN:
(纸本)078031865X
This paper proposes a novel method of speaker normalization by means of input space optimization for continuous density hidden Markov models (CDHMM). The parameters of a linear feature transformation function are so determined that, together with the previously trained CDHMM parameters, a mis-classification cost function is minimized for the normalizing data set. Preliminary experimental results on the task of sex adaptation for speaker-independent stop consonant discrimination, evaluated from the DARPA TIMIT speech database, demonstrates the effectiveness of the proposed method.< >
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