Name
Abstract | ||
|---|---|---|
Describing local patches to register image keypoints is an important task for building a huge database from video frames. When searching for an efficient descriptor, task is twofold: features must describe the featuring patches at a high efficiency, while the dimensionality should be kept at a manageable low value. The main assumption in finding local descriptors is the defect of continuity in the discrete neighborhood or the imperfectness of local shape formats. Curve fitting methods for noisy shapes are called: active contours are generated around keypoints. Local contours are characterized by a small number of Fourier descriptors, resulting a new feature set of low dimensionality. Similarity among different images are searched through these descriptors. The method was tested on 22 real-life video frames made by an outdoor surveillance camera of a city police central. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICIP.2009.5413448 | ICIP |
Keywords | Field | DocType |
scale-invariant keypoints,important task,local shape format,local contour,low dimensionality,local contour descriptors,real-life video frame,image keypoints,local patch,manageable low value,fourier descriptors,local descriptors,feature extraction,shape,curve fitting,active contour,scale invariance,sift,edge detection,computer vision,fourier analysis | Active contour model,Scale-invariant feature transform,Computer vision,Scale invariance,Fourier analysis,Pattern recognition,Curve fitting,Edge detection,Computer science,Curse of dimensionality,Feature extraction,Artificial intelligence | Conference |
ISSN | ISBN | Citations |
1522-4880 E-ISBN : 978-1-4244-5655-0 | 978-1-4244-5655-0 | 3 |
PageRank | References | Authors |
0.40 | 10 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrea Kovács | 1 | 37 | 3.56 |
| Sziranyi, T. | 2 | 395 | 44.76 |