Tuning range image segmentation by genetic algorithm
type: Item
instance of: scholarly article
title: Tuning range image segmentation by genetic algorithm
zbMATH Open document ID: 1084.68935
DOI: 10.1155/S1110865703303087
author: Gianluca Pignalberi
author: Rita Cucchiara
author: L. Cinque
author: Stefano Levialdi
published in: EURASIP Journal on Applied Signal Processing
publication date: 2004-10-28T00:00:00Z
review text: Summary: Several range image segmentation algorithms have been proposed, each one to be tuned by a number of parameters in order to provide accurate results on a given class of images. Segmentation parameters are generally affected by the type of surfaces (e.g., planar versus curved) and the nature of the acquisition system (e.g., laser range finders or structured light scanners). It is impossible to answer the question, which is the best set of parameters given a range image within a class and a range segmentation algorithm? Systems proposing such a parameter optimization are often based either on careful selection or on solution space-partitioning methods. Their main drawback is that they have to limit their search to a subset of the solution space to provide an answer in acceptable time. In order to provide a different automated method to search a larger solution space, and possibly to answer more effectively the above question, we propose a tuning system based on genetic algorithms. A complete set of tests was performed over a range of different images and with different segmentation algorithms. Our system provided a particularly high degree of effectiveness in terms of segmentation quality and search time.
Mathematics Subject Classification ID: 68U10
zbMATH DE Number: 2111338
zbMATH Keywords: space-partitioning methods
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 2111338