Name
Abstract | ||
|---|---|---|
We propose a general approach for the design of 2D feature detectors from a class of steerable functions based on the optimization of a Canny-like criterion. In contrast with previous computational designs, our approach is truly 2D and provides filters that have closed-form expressions. It also yields operators that have a better orientation selectivity than the classical gradient or Hessian-based detectors. We illustrate the method with the design of operators for edge and ridge detection. We present some experimental results that demonstrate the performance improvement of these new feature detectors. We propose computationally efficient local optimization algorithms for the estimation of feature orientation. We also introduce the notion of shape-adaptable feature detection and use it for the detection of image corners. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/TPAMI.2004.44 | IEEE Trans. Pattern Anal. Mach. Intell. |
Keywords | Field | DocType |
feature detector,new feature detector,shape-adaptable feature detection,general approach,orientation selectivity,computationally efficient local optimization,steerable filter,canny-like criterion,ridge detection,feature orientation,previous computational design,edge detection,ridge,matched filters,edge,lines,boundary,feature,feature detection,feature extraction | Computer vision,Feature detection (computer vision),Pattern recognition,Edge detection,Feature (computer vision),Ridge detection,Computer science,Hessian matrix,Feature extraction,Artificial intelligence,Local search (optimization),Steerable filter | Journal |
Volume | Issue | ISSN |
26 | 8 | 0162-8828 |
Citations | PageRank | References |
162 | 7.24 | 23 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mathews Jacob | 1 | 790 | 59.62 |
| Unser, M. | 2 | 3438 | 442.40 |