کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
533369 | 870109 | 2012 | 18 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Edge curvature and convexity based ellipse detection method Edge curvature and convexity based ellipse detection method](/preview/png/533369.png)
In this paper, we propose a novel ellipse detection method for real images. The proposed method uses the information of edge curvature and their convexity in relation to other edge contours as clues for identifying edge contours that can be grouped together. A search region is computed for every edge contour that contains other edge contours eligible for grouping with the current edge contour. A two-dimensional Hough transform is performed in an intermediate step, in which we use a new ‘relationship score’ for ranking the edge contours in a group, instead of the conventional histogram count. The score is found to be more selective and thus more efficient. In addition, we use three novel saliency criteria, that are non-heuristic and consider various aspects for quantifying the goodness of the detected elliptic hypotheses and finally selecting good elliptic hypotheses. The thresholds for selection of elliptic hypotheses are determined by the detected hypotheses themselves, such that the selection is free from human intervention. The method requires a few seconds in most cases. So, it is suitable for practical applications. The performance of the proposed ellipse detection method has been tested on a dataset containing 1200 synthetic images and the Caltech 256 dataset containing real images. In both cases, the results show that the proposed ellipse detection method performs far better than existing methods and is close to the ideal results, with precision, recall, and F-measure, all very close to 1. Further, the method is robust to the increase in the complexity of the images (such as overlapping ellipses, occluded ellipses), while the performance of the contemporary methods deteriorates significantly.
► Information of curvature and convexity of edges in relation to each other.
► A novel relationship score, Hough transform and least squares fitting is used.
► Three saliency criteria for quantifying the goodness of fit.
► A non-heuristic selection approach is used for electing salient ellipse.
► Shows good performance for several synthetic and real images.
Journal: Pattern Recognition - Volume 45, Issue 9, September 2012, Pages 3204–3221