A nonparametric approach for histogram segmentation
In this work, we propose a method to segment a 1-D histogram without a priori assumptions about the underlying density function. Our approach considers a rigorous definition of an admissible segmentation, avoiding over and under segmentation problems. A fast algorithm leading to such a segmentation...
| Publié dans: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1997. - 16(2007), 1 vom: 29. Jan., Seite 253-61 |
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| Auteur principal: | |
| Autres auteurs: | , , |
| Format: | Article |
| Langue: | English |
| Publié: |
2007
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| Accès à la collection: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Sujets: | Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. |
| Résumé: | In this work, we propose a method to segment a 1-D histogram without a priori assumptions about the underlying density function. Our approach considers a rigorous definition of an admissible segmentation, avoiding over and under segmentation problems. A fast algorithm leading to such a segmentation is proposed. The approach is tested both with synthetic and real data. An application to the segmentation of written documents is also presented. We shall see that this application requires the detection of very small histogram modes, which can be accurately detected with the proposed method |
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| Description: | Date Completed 28.02.2007 Date Revised 26.10.2019 published: Print Citation Status MEDLINE |
| ISSN: | 1057-7149 |