Tuning Range Image Segmentation by Genetic Algorithm
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Tuning Range Image Segmentation by Genetic Algorithm Gianluca Pignalberi Dipartimento di Informatica, Universit`a di Roma La Sapienza, Via Salaria, 113 00198 Roma, Italy Email: [email protected]
Rita Cucchiara Dipartimento di Ingegneria dell’Informazione, Universit`a di Modena e Reggio Emilia, Via Vignolese, 905 41100 Modena, Italy Email: [email protected]
Luigi Cinque Dipartimento di Informatica, Universit`a di Roma La Sapienza, Via Salaria, 113 00198 Roma, Italy Email: [email protected]
Stefano Levialdi Dipartimento di Informatica, Universit`a di Roma La Sapienza, Via Salaria, 113 00198 Roma, Italy Email: [email protected] Received 1 July 2002 and in revised form 19 November 2002 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 spacepartitioning 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. Keywords and phrases: range images, segmentation, genetic algorithms.
1. INTRODUCTION Image segmentation problems can be approached with several solution methods. The range image segmentation subfield has been addressed in different ways. But, since an algorithm should work correctly for a large number of images in a class, such a program is normally characterized by a high number of tuning parameters in order to obtain a correct, or at least satisfactory, segmentation. Usually the correct set of parameters is given by the developers of the segmentation algorithm, and it is expected to give satisfactory segmentations for the images in the class used to tune the parameters. But it is possible that, given changing input image class, the results are not satisfactory. To avoid exhaustive test tuning, an expert system to tune parameters should be proposed. In this way, it should be pos-
sible to easily direct the chosen segmentation algorithm to work correctly with a chosen class of images. Several expert systems have been proposed by other teams. We can quote [1] that performs the tuning of a color image segmentation algori
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