کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
536023 | 870436 | 2011 | 18 صفحه PDF | دانلود رایگان |

The gradient image is used to detect edge points, and the gradient histogram is a typical case of a unimodal histogram. It is well-documented that bi-modal thresholding methods (such as the Otsu method) detect edges poorly. Therefore, specific unimodal thresholding methods are used to detect edge points. However, unimodal thresholding methods (such as the Rosin method) sometimes obtain very noisy results. In this paper, we propose a histogram transformation to improve the performance of some thresholding methods. Using the Berkeley Segmentation Dataset, we present quantitative performance results in an edge detection task to show that our transformation improves the performance of the Otsu and Rosin methods. Our histogram transformation can be used by any histogram thresholding method, but the performance of the method, using the transformed histogram, will depend of the criterion used by this method.
Research highlights
► The Otsu thresholding method has a low performance on unimodal histograms.
► The unimodal thresholding methods sometimes obtain very noisy results.
► A method to increase the performance of the Otsu method is proposed.
Journal: Pattern Recognition Letters - Volume 32, Issue 5, 1 April 2011, Pages 676–693