This paper investigates three different methods of comparing preference structures for thinningalgorithms. The first method involves a series of experiments with human subjects. The second makes use of neural network...
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This paper investigates three different methods of comparing preference structures for thinningalgorithms. The first method involves a series of experiments with human subjects. The second makes use of neural networks and the third is based on dissimilarities and distance measures that is computer generated. Several statistical tests have been performed to analyze the preference structures exhibited by the data. This study highlights human coherence in comparing skeletons and the novelty of using reference skeletons to facilitate the evaluation of thinning algorithms. None of the automatic approaches provides a useful insight although a measure of information content manifests some consistency. The overall study suggests a systematic protocol involving human coherence to evaluate preprocessing algorithms.
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