Multiscale corner detection by using wavelet transform

Abstract

A multiscale corner detection algorithm based on the wavelet transform of contour orientation is proposed. It can utilize both the information of the local extrema and modulus of transform results to detect corners and arcs effectively. The ramp-width of contour orientation profile, which can be computed using the transformed modulus of two scales, reveals the difference between corner and arc and is utilized in the determination of corner points. The experimental results have shown that the detector is more effective than both the single- and multiple-scale detectors. They also demonstrate that the detector is insensitive to boundary noise. In addition, the proposed method is more efficient than the other multiscale corner detector because it operates on fewer number of scales, which can be implemented by a fast transform algorithm.

Corner Detection Based on Multi-directional Gabor Filters with Multi-scales

“…Because of its simplicity and high efficiency, the Harris detector has become one of the most successful methods and has been widely used. However, the detection system based on a single scale leads to the loss of certain salient points and acceptance of false points [29]. The distinction between edges and corners cannot be effectively represented by two-directional derivatives [30].…”

<title>Corner detection method based on wavelet transform</title>

“…Firstly they represent 2D planar curves in the form of a defined orientation angle. Then involve a fast wavelet transform given by Mallat [8] and computing the derivative of the ratios of the wavelet transform modulus maxima (WTMM) [9]. To circumvent the computation complexity A.…”