An effective approach to corner point detection through multiresolution analysis

Feature points are low-level image features representing meaningful image regions and ideal candidates for feature-based image representation, and feature point detection is an essential pre-processing step for high-level computer vision tasks. Existing feature detection algorithms are either computationally intensive (multi-scale detectors) or sensitive to scale variations (single-scale detectors). In this paper, we propose a computationally efficient multi-scale corner detector based on Discrete Wavelet Transform (DWT). We use non-redundant DWT coefficients to build a corner strength map at each scale in a data-compact way and upsample these maps by the Gaussian kernel interpolation to the original image size. By taking the summation of these maps, a corner strength measure is formed. We propose a new scale selection method that utilizes a Gaussian kernel convolution to measure the corner distribution in the vicinity of every corner point. In addition, the so-called “Polarized Gaussian” kernels are introduced to achieve rotational invariance. The high efficiency of the proposed corner detector is shown through both computational complexity analysis and accuracy analysis.