Robust detection of region boundaries in a sequence of 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