In this paper, we define a new paradigm for eight-connection labeling, which employs a general approach to improve neighborhood exploration and minimizes the number of memory accesses. First, we exploit and extend the decision table formalism introducing OR-decision tables, in which multiple alternative actions are managed. An automatic procedure to synthesize the optimal decision tree from the decision table is used, providing the most effective conditions evaluation order. Second, we propose a new scanning technique that moves on a 2 x 2 pixel grid over the image, which is optimized by the automatically generated decision tree. An extensive comparison with the state of art approaches is proposed, both on synthetic and real datasets. The synthetic dataset is composed of different sizes and densities random images, while the real datasets are an artistic image analysis dataset, a document analysis dataset for text detection and recognition, and finally a standard resolution dataset for picture segmentation tasks. The algorithm provides an impressive speedup over the state of the art algorithms.
In this paper we present a novel algorithm to synthesize an optimal decision tree from OR-decision tables, an extension of standard decision tables, complete with the formal proof of optimality and computational cost analysis. As many problems which require to recognize particular patterns can be modeled with this formalism, we select two common binary image processing algorithms, namely connected components labeling and thinning, to show how these can be represented with decision tables, and the benefits of their implementation as optimal decision trees in terms of reduced memory accesses. Experiments are reported, to show the computational time improvements over state of the art implementations.
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