tailieunhanh - Báo cáo hóa học: " Tuning Range Image Segmentation by Genetic Algorithm Gianluca Pignalberi"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Tuning Range Image Segmentation by Genetic Algorithm Gianluca Pignalberi | EURASIP Journal on Applied Signal Processing 2003 8 780-790 2003 Hindawi Publishing Corporation Tuning Range Image Segmentation by Genetic Algorithm Gianluca Pignalberi Dipartimento di Informatica Universita di Roma La Sapienza Via Salaria 113 00198 Roma Italy Email pignalbe@ Rita Cucchiara Dipartimento di Ingegneria dell Informazione Universita di Modena e Reggio Emilia Via Vignolese 905 41100 Modena Italy Email Luigi Cinque Dipartimento di Informatica Universita di Roma La Sapienza Via Salaria 113 00198 Roma Italy Email cinque@ Stefano Levialdi Dipartimento di Informatica Universita di Roma La Sapienza Via Salaria 113 00198 Roma Italy Email levialdi@ 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 . planar versus curved and the nature of the acquisition system . 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.

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