A Performance Review of Recent Corner Detectors

Abstract

Contour-based corner detectors directly or indirectly estimate a significance measure (eg, curvature) on the points of a planar curve and select the curvature extrema points as corners. A number of promising contour-based corner detectors have recently been proposed. They mainly differ in how the curvature is estimated on each point of the given curve. As the curvature on a digital curve can only be approximated, it is important to estimate a curvature that remains stable against significant noises, for example, geometric transformations and compression, on the curve. Moreover, in many applications, for instance, in content-based image retrieval, a fast corner detector is a prerequisite. So, it is also a primary characteristic that how much time a corner detector takes for corner detection in a given image. In addition, different authors evaluated their detectors on different platforms using different evaluation systems. Evaluation systems that depend on human judgements and visual identification of corners are manual and too subjective. Application of a manual system on a large test database will be expensive. Therefore, it is important to evaluate the detectors on a common platform using an automatic evaluation system. This paper first reviews six most recent and highly performed corner detectors and analyse their theoretical running time. Then it uses an automatic evaluation system to analyse their performance. Both the robustness to noise and efficiency are estimated to rank the detectors.

Chord Angle Deviation using Tangent (CADT), an efficient and robust contour-based corner detector

“…CTAR refinement process is also simpler in comparison to that of CPDA. This comparative study of corner detectors in [6] finds CTAR to be more effective and faster than CPDA in the field of average repeatability. However, CPDA has a lower localization error compared to that of CTAR.…”

“…If the value of R L (i) is lower than the value of the threshold T h = 0.9896, then P i gets considered as a candidate corner [1]. In comparison, CTAR is faster than CPDA in terms of computation [6] and can find corners in a more reliable fashion under a range of image transformations [1]. Now we move on to the description of CTAA.…”