Análisis comparativo de técnicas de segmentación de estructuras reticulares

Abstract

This article aims to compare our previous work on segmentation of reticular structures with neural networks against an ad hoc algorithm for the same purpose. Nowadays neural networks or artificial intelligence are widely used concepts synonymous with advances and improvements, but in certain cases it is possible to use more classical techniques, outside the paradigm of artificial intelligence to achieve the same type of tasks with similar results. To corroborate last mention, in this article we perform a quantitative and qualitative comparative analysis between an ad hoc algorithm and the best neural network model in our latest work for segmenting reticular structures. Conventional methods such as Random Sample Consensus (RANSAC) and region growing are used to implement the algorithm. Standardised metrics such as precision, recall and f1-score are used for quantitative comparison. The latter will be calculated on a proprietary dataset, consisting of a thousand point clouds automatically generated in previous work. The algorithm in question is designed specifically for such a database