
In this paper we consider the multiple geometrical object detection problem. On the basis of the set A of data points coming from and scattered among a number of geometrical objects not known in advance, we should reconstruct or detect thosegeometrical objects. A new very efficient method for solving this problem based on avery popular RANSAC method using parameters from DBSCAN method is proposed.Thereby, instead of using classical indexes for recognizing the most appropriatepartition, we use parameters from DBSCAN method which define the necessaryconditions proven to be far more efficient.Especially, the method is applied to solving multiple circle detection problem. In this case, we give both the conditions for the existence of the best circle as arepresentative of the data set and the explicit formulas for the parameters of the bestcircle. In the illustrative example we consider the multiple circle detection problem for the datapoint set A coming from 5 intersected circles not known in advance. Using Wolfram Mathematica, the proposed method needed between 0.5 - 1 sec to solve this problem.
Multiple ellipse detection problem, RANSAC, Modified k-means, The most appropriate partition, Multiple line detection problem, Multiple circle detection problem, DBSCAN, RANSAC ; DBSCAN ; Multiple line detection problem ; Multiple circle detection problem ; Multiple ellipse detection problem ; The most appropriate partition ; Modified k-means ; Incremental algorithm, Incremental algorithm
Multiple ellipse detection problem, RANSAC, Modified k-means, The most appropriate partition, Multiple line detection problem, Multiple circle detection problem, DBSCAN, RANSAC ; DBSCAN ; Multiple line detection problem ; Multiple circle detection problem ; Multiple ellipse detection problem ; The most appropriate partition ; Modified k-means ; Incremental algorithm, Incremental algorithm
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