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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Dawei Qi Li Li Jingwei Song Hongbo Mu |
| Copyright Year | 2007 |
| Description | Author affiliation: Northeast Forestry Univ., Harbin (Dawei Qi; Li Li; Jingwei Song; Hongbo Mu) |
| Abstract | Defects connotative feature information in the X-ray image data of log with rotten knot was studied. Adopting box counting method detected log with rotten knot image edges effectively. Describe the irregular degree of image in quantity by using the value of fractal dimension to confirm the defects position. There were differences in fractal dimension between the normal regions and the edges. The values of normal regions in log image were about 2.0783, while that of the edges were between 1.4000 and 1.9000. The results show that fractal dimension was useful to numerically detect the defects of log with rotten knot image. This study provides a new method for edge detection of X-ray log image. |
| Starting Page | 2868 |
| Ending Page | 2873 |
| File Size | 1298848 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424408177 |
| DOI | 10.1109/ICCA.2007.4376886 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-05-30 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Fractals Image edge detection X-ray imaging Nondestructive testing Forestry Layout Upper bound Automatic control Automation Educational institutions box counting rotten knot x-ray image edge detection fractal dimension |
| Content Type | Text |
| Resource Type | Article |
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