Derivative-Based Scale Invariant Image Feature Detector With Error Resilience

作者：Mainali Pradip^*; Lafruit Gauthier; Tack Klaas; Van Gool Luc; Lauwereins Rudy

来源：IEEE Transactions on Image Processing, 2014, 23(5): 2380-2391.

DOI：10.1109/TIP.2014.2315959

• Gaussian derivatives
• scale space
• scale-invariant features
• registration
• keypoint

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.

• 出版日期2014-5