Name
Abstract | ||
|---|---|---|
We present a novel scale-invariant image feature detection algorithm (D-SIFER) using a newly proposed scale-space optimal 10th-order Gaussian derivative (GDO-10) filter, which reaches the jointly optimal Heisenberg's uncertainty of its impulse response in scale and space simultaneously (i.e., we minimize the maximum of the two moments). The D-SIFER algorithm using this filter leads to an outstanding quality of image feature detection, with a factor of three quality improvement over state-of-the-art scale-invariant feature transform (SIFT) and speeded up robust features (SURF) methods that use the second-order Gaussian derivative filters. To reach low computational complexity, we also present a technique approximating the GDO-10 filters with a fixed-length implementation, which is independent of the scale. The final approximation error remains far below the noise margin, providing constant time, low cost, but nevertheless high-quality feature detection and registration capabilities. D-SIFER is validated on a real-life hyperspectral image registration application, precisely aligning up to hundreds of successive narrowband color images, despite their strong artifacts (blurring, low-light noise) typically occurring in such delicate optical system setups. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TIP.2014.2315959 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
Gaussian processes,Heisenberg model,computational complexity,feature extraction,hyperspectral imaging,image recognition,image registration,transforms,D-SIFER,Heisenberg's uncertainty,approximation error,computational complexity,derivative-based scale invariant image feature detector,error resilience,hyperspectral image registration,optical system,optimal 10th-order Gaussian derivative filter,quality of image feature detection,scale-invariant feature transform,second-order Gaussian derivative filters,speeded up robust features methods,Gaussian derivatives,keypoint,registration,scale space,scale-invariant features | Computer vision,Scale-invariant feature transform,Feature detection (computer vision),Pattern recognition,Feature (computer vision),Edge detection,Gaussian blur,Feature extraction,Artificial intelligence,Kanade–Lucas–Tomasi feature tracker,Mathematics,Image registration | Journal |
Volume | Issue | ISSN |
23 | 5 | 1057-7149 |
Citations | PageRank | References |
8 | 0.51 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mainali, P. | 1 | 8 | 0.51 |
| G. Lafruit | 2 | 655 | 56.91 |
| Tack, K. | 3 | 8 | 0.51 |
| Luc Van Gool | 4 | 27566 | 1819.51 |