This paper introduces new hierarchical autoregressive (HMAR) models for lossless compression of medical images. The proposed concept involves a multi-layered modeling approach. The 2-D HMAR models can be thought of as modified versions of one-dimensional hierarchical AR (1-D HAR) signal models. The first layer of a 1-D HAR consists of a conventional AR model for the data, whereas each subsequent layer, in turn, attempts to model the AR coefficients of the preceding layer using a new AR model. The main advantage of HMAR is that the transmission of blockwise model coefficients becomes unnecessary. The performances of the proposed technique is compared with two existing alternative techniques; namely, hierarchical interpolation (HINT), and fixed differential pulse code modulation (DPCM). In terms of compression efficiency, HMAR models performs better than the other techniques considered.

This paper studies the characteristic properties of a specific class of near-lossless image compression schemes which consists of a lossless coder followed by a uniform scalar quantizer. Three specific instances of such schemes are investigated; namely, differential pulse code modulation, hierarchical interpolation, and two-dimensional space-varying multiplicative autoregressive coders. The compression gains attainable with such schemes are studied and results of experiments conducted on several medical images are presented.

The problem of edge detection in noisy images is addressed in this paper. It is shown that the performance of an existing edge detection method, known as the stochastic gradient operator, can be significantly improved by incorporating three new features: (i) a robust technique for estimating the noise variance and autocorrelation function of the signal, (ii) a block-by-block adaptation of the gradient mask, and (iii) calculation of a threshold based on Rayleigh distribution. The performance of the proposed technique is compared with that of some existing ones.

Presents new methods for lossless predictive coding of medical images using two dimensional multiplicative autoregressive models. Both single-resolution and multi-resolution schemes are presented. The performances of the proposed schemes are compared with those of four existing techniques. The experimental results clearly indicate that the proposed schemes achieve higher compression compared to the lossless image coding techniques considered.

The authors introduce two new one-dimensional multiplicative autoregressive (MAR) models for adaptive predictive coding of digitized images. The proposed scheme offers a number of advantages. These include easy implementability, a high signal-to-noise ratio at a moderate bit rate, and guaranteed stability of the predictive coder. Results of extensive experimental studies are presented.

A bivariate autoregressive model is introduced for the analysis and classification of closed planar shapes. The boundary coordinate sequence of a digitized binary image is sampled to produce a polygonal approximation to an object's shape. This circular sample sequence is then represented by a vector autoregressive difference equation which models the individual Cartesian coordinate sequences as well as coordinate interdependencies. Several classification features which are functions or transformations of the estimated coefficient matrices and the associated residual error covariance matrices are developed. These features are shown to be invariant to object transformations such as translation, rotation, and scaling. Laboratory experiments involving object sets representative of industrial shapes are presented. Superior classification results are demonstrated.>

This paper formulates one-dimensional (1-D), recursive, multiplicative time series models for digital images and demonstrates their use for adaptive predictive coding of such images. The performance of the scheme presented here is superior compared to that of conventional 1-D modelling techniques, because correlation among all neighboring pixels of interest can be taken into account. Further, the projection-based constrained least squares identification technique proposed here guarantees stability of the underlying predictor, which makes the scheme more robust compared to the ones that use 2-D recursive models where predictor stability cannot be guaranteed.

A new computer aided technique for the single pass optimal design of stable one dimensional infinite impulse response (IIR) filters is proposed. The method is also applied to the single pass design of stable two dimensional (2-D) filters. The problem is formulated as a constrained non-linear programming operation subject to a convex set of constraints. In the one dimensional (1-D) case, the objective function is a convex function of the filter parameters which insures that the final design is a constrained global optimum. Stability is insured by use of a projection algorithm which continually adjusts any estimates which exceed the constraint boundaries.

The purpose of this paper is to demonstrate the advantages of hybrid, model-matching adaptive control schemes over similar discrete schemes for control of robotic manipulators. In the hybrid approach, the controller itself is a continuously adjustable one, but the adaptation mechanism uses discrete algorithms. Thus, it retains the simplicity and efficiency of discrete parameter estimation techniques and requires estimation of fewer parameters. Furthermore, it retains the minimum phase property of the original continuous time plant and thus avoids any potential problem of instability.

This paper deals with the development of a unified theory for identification of parameters of a linear, discrete time system which is described by either causal or noncausal ARMA model. We study in detail the convergence properties of a parameter identification algorithm called least squares with covariance modification (LSCOM) and show how the convergence properties of a number of other related algorithms can be derived as special cases of the LSCOM algorithm.