Fractal dimension estimation using modified differential box-counting and its application to MSTAR target classification
A new approach to estimate the fractal dimension of an image is proposed in this paper. A modified differential box-counting (MDBC) is an extended version of the differential box-counting (DBC) by incorporating order statistics. We compare the fractal dimensions achieved by the MDBC and the traditio...
محفوظ في:
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| مؤلفون آخرون: | |
| التنسيق: | Conference or Workshop Item |
| اللغة: | English |
| منشور في: |
2014
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| الوصول للمادة أونلاين: | http://www.scopus.com/inward/record.url?eid=2-s2.0-0036976693&partnerID=40&md5=9b67125013307e57007cbf6d96faa01c http://cmuir.cmu.ac.th/handle/6653943832/1343 |
| الوسوم: |
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| المؤسسة: | Chiang Mai University |
| اللغة: | English |
| الملخص: | A new approach to estimate the fractal dimension of an image is proposed in this paper. A modified differential box-counting (MDBC) is an extended version of the differential box-counting (DBC) by incorporating order statistics. We compare the fractal dimensions achieved by the MDBC and the traditional DBC on both synthetic and real texture images. The MDBC yields similar fractal dimension estimates to the DBC and some other methods. We also apply the new textural feature to artificial neural networks in a target classification problem of synthetic aperture radar (SAR) images from the MSTAR public release data set collected by the DARPA and Wright Laboratory. The results suggest that the feature based on the modified DBC yields a good classification performance and provides better discrimination than the traditional DBC. |
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