Euclidean Distance transformation is a fundamental technique for the application fields of image understanding and computer vision. Some important characteristics in image analysis such as shape factor, skeleton and medial axis are based upon the distance transformation computation.
The lookup table algorithm is based upon the recursive computation structure of the 4N method. Therefore, this algorithm is very fast and is close to the 4N method, which performs as the fastest one among all the comparing algorithms in our experiments. The success of the lookup table algorithm is based upon a checking strategy by error geometry. The error candidates are arranged in order according to their distances to the reference point. In addition, a Local_Array is used to store the y coordinates of the closest foreground pixels above the processing line. Therefore we can find the correct feature point by checking the ordered candidates with the information provided from the Local_Array instead of comparisons among the candidates. In contrast, all the comparing eror-free Euclidean algorithms select their feature points from candidates by time consuming distance comparison.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0912107-112813 |
| Date | 12 September 2007 |
| Creators | Yu, Yan-Liang |
| Contributors | Chin-Hsing Chen, Ben-shung Chow, Tsang-Yi Wang, Tsung Lee |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0912107-112813 |
| Rights | not_available, Copyright information available at source archive |
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