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- Glen P. Abousleman, Michael W. Marcellin, Bobby R. Hunt
- IEEE Trans. Geoscience and Remote Sensing
- 1995

- James H. Kasner, Michael W. Marcellin, Bobby R. Hunt
- IEEE Trans. Image Processing
- 1999

A new form of trellis coded quantization based on uniform quantization thresholds and "on-the-fly" quantizer training is presented. The universal trellis coded quantization (UTCQ) technique requires neither stored codebooks nor a computationally intense codebook design algorithm. Its performance is comparable with that of fully optimized entropy-constrainedā¦ (More)

- C Miller, B R Hunt, M W Marcellin, M A Neifeld
- Journal of the Optical Society of America. Aā¦
- 2000

The Viterbi algorithm (VA) is known to given an optimal solution to the problem of estimating one-dimensional sequences of discrete-valued pixels corrupted by finite-support blur and memoryless noise. A row-by-row estimation along with decision feedback and vector quantization is used to reduce the computational complexity of the VA and allow the estimationā¦ (More)

- Yingyong Qi, Bobby R. Hunt
- Pattern Recognition
- 1994

-In this work, algorithms for extracting global geometric and local grid features of signature images were developed. These features were combined to build a multi-scale verification function. This multi-scale verification function was evaluated using statistical procedures. Results indicated that the multi-scale verification function yielded a lowerā¦ (More)

- H. Joel Trussell, Bobby R. Hunt
- IEEE Trans. Computers
- 1979

Blur identification is a crucial first step in many image restoration techniques. An approach for identifying image blur using vector quantizer encoder distortion is proposed. The blur in an image is identified by choosing from a finite set of candidate blur functions. The method requires a set of training images produced by each of the blur candidates.ā¦ (More)

- Yingyong Qi, Bobby R. Hunt
- IEEE Trans. Speech and Audio Processing
- 1993

Voiced-unvoiced-silence classhation of speech was made using a multilayer feedforward network. The network was evaluated and compared to a maximum-likelihood classiller. Results indicated that the network performance was not significantly affected by the size of training set and a classification rate as high as 96% was obtained.

- Bobby R. Hunt
- Int. J. Imaging Systems and Technology
- 1995

- Glen P. Abousleman, Michael W. Marcellin, Bobby R. Hunt
- IEEE Trans. Image Processing
- 1997

A training-sequence-based entropy-constrained predictive trellis coded quantization (ECPTCQ) scheme is presented for encoding autoregressive sources. For encoding a first-order Gauss-Markov source, the mean squared error (MSE) performance of an eight-state ECPTCQ system exceeds that of entropy-constrained differential pulse code modulation (ECDPCM) by up toā¦ (More)

- David DeKruger, Bobby R. Hunt
- Pattern Recognition
- 1994