A TABU SEARCH METHOD FOR GEOMETRIC PRIMITIVE EXTRACTION

In this paper, we propose a novel method for extracting the geometric primitives from geometric data, which is essentially an optimization p roblem. Specifically, we use tabu search to solve geometric primitive extraction problem. To the best of our knowledge, it is the first atte mpt that tabu search is used in computer vision. Our tabu search (TS) has a number of advantages: (1) TS avoids entrapment in local minima a nd continues the search to give a near-optimal final solution; (2) TS is very general and conceptually much simpler than either simulated an nealing (SA) or genetic algorithm (GA); (3) TS has no special space re quirement and is very easy to implement (the entire procedure only occ upies a few lines of code); (4) our TS-based method can successfully e xtract some geometric primitives which are specially difficult for the traditional methods such as Hough Transform (HT) and Robust Statistic s(RS). TS is a flexible framework of a variety of strategies originati ng from artificial intelligence and is therefore open to further impro vement. (C) 1997 Published by Elsevier Science B.V.