The problem of adaptive segmentation of images of objects with smooth surfaces is addressed. The images are composed of regions of slowly varying intensities that may be corrupted by additive noise. The underlying field is modeled by Markov random field that consists of both a label process which contains the classification of each pixel in the image and intensity functions which contain the possible grey levels that each pixel may take. The algorithm iteratively repeats two steps: the parameter estimation step, in which the maximum-likelihood (ML) estimates of the associated parameters are obtained; and the restoration step, in which the underlying field is estimated through the maximum-a-posteriori (MAP) method. The concept of allowing the pixel grey values to vary across the image regions is discussed. These values are estimated by using windows on the observed data. As the algorithm progresses, the window size is decreased so that the algorithm adapts to the characteristics of each region.>

Addresses the problem of detecting small regions of interest embedded in larger areas of interest as imaged by a fluorescence imaging system. The application exhibits the variation observed in biological specimens, which are primarily shape and intensity uncertainties. The presence of these variability together with uneven illumination and the lack of a global model, implies that a reliable nonparametric technique should be used to detect the objects of interest. The detection task is formulated as a two step hierarchical approach which integrates both parametric and nonparametric techniques. The image as a whole is considered as a slowly varying multi-modal Gaussian field. The classification of which is obtained through the expectation maximisation algorithm, and a spatially smoother segmentation is accomplished by using a Gibbsian segmenter. Shape deformation constraints retain only the so-called valid objects. A similar approach is employed in the second step, where objects within the already detected objects are identified.>

A robust and efficient technique for detection of region boundaries in a sequence of images is the foundation for a hierarchical framework for visual target tracking. This robust technique is a multi-hypothesis test that searches for a boundary that maximizes a certain performance criterion. The framework also consists of a technique to restore noisy images when the parameters are not known and also filtering to predict positions and orientations of successive images.>

The problem of region recognition in a sequence of images is addressed, and a recognition system that finds and tracks region-of-interest boundaries in those images is presented. These regions are not stationary: parts of the boundary may be missing or completely blurred and outliers are likely to exist. Thus, the emphasis is on robustification and efficiency. The region segmentation problem was formulated as a multihypothesis test that seeks the boundary that maximizes a performance criterion which is general in terms of blur and noise. Efficiency is obtained by restricting outline candidates to an adaptive search area near the optimal boundary from the previous section. The search for the maximum is cast into a fast first-order dynamic programming procedure. Robust statistical techniques are used in the multihypothesis test to reduce the sensitivity to outliers and unexpected noise. The inconsistent parts of the optimal boundary are then detected by using a robust expectation maximization algorithm and are interpolated from higher-quality parts. The boundary obtained by this method is used as the reference boundary for the next image.>

The problem of restoring noisy images when the model parameters are not known is discussed. The underlying field, x, is modeled as a noncausal Markov random field (MRF), namely, either a multilevel logistic (MLL) or a Gaussian MRF, and is corrupted by additive independently identically distributed (i.i.d.) Gaussian noise. The application is a restoration/segmentation of regions of interest in an image obtained from histologies of brain sections, which suggests an MLL modeling since the regions are spatially smooth. The presented algorithm maximizes the joint likelihood of the observations, y, and x given the unknown parameters. The parameters of the noise and the random field are estimated separately through a maximum likelihood technique given the current estimate of x, and the underlying field is estimated through a maximum a posteriori method. In the case of images modeled by MLL MRFs, the result of the restoration is actually a segmentation since the collection of all pixels with the same level defines a region. The results show that the algorithm successfully segments the region of interest even when the signal-to-noise ratio is low.>