Name
Abstract | ||
|---|---|---|
A novel descriptor, referred to as the whitening central projection predictor (WCPD), is developed for affine-invariant shape description. The proposed descriptor is based on central projection transform (CPT) and whitening transform (WT). Dislike contour-based or region-based approaches, an object is first converted to a closed curve by CPT, which is called the general curve (GC). The derived GC not only keeps the affine transform information, but also is very robust to noise. Then WT is performed to the GC with the purpose that the affine transformation is simplified to a rotation only. Finally, Fourier descriptors are employed to remove the rotation, and WCPD is obtained. One advantage of using WCPD for affine-invariant description lies in that it is applicable to objects consisting of several components. Furthermore, the approach used on the GC is contour-based, and is of small computational complexity. Several experiments have been conducted to evaluate the performance of the proposed method. Experimental results show that the proposed method has a powerful discrimination ability, and is more robust to noise. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1049/iet-ipr.2012.0094 | IET Image Processing |
Keywords | Field | DocType |
cpt,fourier transforms,fourier descriptors,shape recognition,region-based approaches,whitening transform,central projection transform,general curve,gc,wt,affine-invariant shape description,whitening central projection descriptor,computational complexity,powerful discrimination ability,object detection,contour-based approaches,wcpd,affine transforms,affine transform information,closed curve,affine transform | Affine transformation,Pattern recognition,Fourier transform,Affine invariant,Artificial intelligence,Mathematics,Computational complexity theory | Journal |
Volume | Issue | ISSN |
7 | 1 | 1751-9659 |
Citations | PageRank | References |
5 | 0.40 | 27 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rushi Lan | 1 | 100 | 15.72 |
| Jianwei Yang | 2 | 58 | 12.73 |
| Yong Jiang | 3 | 28 | 1.73 |
| Colin Fyfe | 4 | 5 | 0.40 |
| Zhan Song | 5 | 5 | 0.40 |