<title>Fast Adaptive Algorithms For Low-Level Scene Analysis: The Parallel Hierarchical Ripple Filter</title>

Abstract

We report on the development of a new class of parallel computation algorithm for low -level scene analysis.The algorithm is a high resolution, high speed estimator for boundary extraction of simple objects imaged under noisy conditions. We explain the algorithm structure and underlying physical models; we then present demonstrative pictorial examples of application to synthetic test imagery. We next introduce a generalization of the algorithm wherein a hierarchical variable resolution search is employed to gain major improvements in algorithm convergence speed and robustness.We discuss the importance of making the algorithm adaptive to local image statistics and show that the algorithm parallel-window topology is consonant with this goal. We present further experimental results that depict the generalized algorithm applied to real data bases; these results demonstrate that even simple adaptation models can substantially improve algorithm convergence accuracy. AbstractWe report on the development of a new class of parallel computation algorithm for low-level scene analysis. The algorithm is a high resolution, high speed estimator for boundary extraction of simple objects imaged under noisy conditions. We explain the algorithm structure and underlying physical models; we then present demonstrative pictorial examples of application to synthetic test imagery. We next introduce a generalization of the algorithm wherein a hierarchical variable resolution search is employed to gain major improvements in algorithm convergence speed and robustness. We discuss the importance of making the algorithm adaptive to local image statistics and show that the algorithm parallel^J.'Lndow topology is consonant with this goal. We present further experimental results that depict the generalized algorithm applied to real data bases; these results demonstrate that even simple adaptation models can substantially improve algorithm convergence accuracy.