The Comparison and Application of Corner Detection Algorithms

Abstract

Corners in images represent a lot of important information. Extracting corners accurately is significant to image processing, which can reduce much of the calculations. In this paper, two widely used corner detection algorithms, SUSAN and Harris<em> </em>corner detection algorithms which are both based on intensity, were compared in stability, noise immunity and complexity quantificationally via stability factor η, anti-noise factor ρ and the runtime of each algorithm. It concluded that Harris corner detection algorithm was superior to SUSAN corner detection algorithm on the whole. Moreover, SUSAN and Harris detection algorithms were improved by selecting an adaptive gray difference threshold and by changing directional differentials, respectively, and compared using these three criterions. In addition, SUSAN and Harris corner detectors were applied to an image matching experiment. It was verified that the quantitative evaluations of the corner detection algorithms were valid through calculating match efficiency, defined as correct matching corner pairs dividing by matching time, which can reflect the performances of a corner detection algorithm comprehensively. Furthermore, the better corner detector was used into image mosaic experiment, and the result was satisfied. The work of this paper can provide a direction to the improvement and the utilization of these two corner detection algorithms

Experimental evaluation of four feature detection methods for close range and distant airborne targets for Unmanned Aircraft Systems applications

“…Chen et al [11] performed a comparison of Harris-Stephens-Plessey and SUSAN based on metrics such as stability, noise immunity, complexity and run-time. They concluded that Harris-Stephens-Plessey was superior to SUSAN on the whole.…”

“…In this paper we perform an experimental evaluation of four feature detection methods widely used in the implementation of many target detection approaches reported in the literature [8] [9] [10] [11]: Features from Accelerated Segment Test (FAST) [12], Harris-Stephens-Plessey feature detection [13], Shi and Tomasi -Minimum Eigenvalue feature detection [14], Smallest Univalue Segment Assimilating Nucleus (SUSAN) feature detection [15]. The four methods are evaluated based on their ability to detect features corresponding to close range and distant targets in frames from three video streams recorded in October 2010 using two aircrafts and a tri-focal video recording system [16].…”

A Leukocyte Detection System Using Scale Invariant Feature Transform Method

“…If the absolute gradient values in two directions are both great, then judge the pixel as a corner. Harris corner detector is defined as follows [11]:…”

Human recognition based on retinal images and using new similarity function

“…Harris corner detector is one of the most well-known corner detectors and is based on measuring the corner strength. Comparison between Harris corner detector and other corner detectors like SUSAN shows the superiority of Harris corner detector in the case of stability and complexity (running time) [25]. Therefore, Harris corner detector is used for feature extraction.…”