Robust fast corner detector based on filled circle and outer ring mask

A novel mask with a filled circle and outer ring is proposed to detect corners from images, based on the adaptive threshold of a local region. First, the inner filled circle is used in a response function to filter four non-corner regions: image noise, object edges, corner neighbourhoods, and flat regions. Second, corner candidates are detected using a complex response function, by considering the margin of inner circle and the outer ring together. Finally, related algorithms are developed to determine and remove the false corners lying on thin-band, noisy, and salient pixels. The authors’ approach has been tested on artificial, noisy, fuzzy, and real images, and its performance is evaluated, analysed, and compared with the existing grey-level-based corner detection methods of Harris, SUSAN, FAST, and Lan and Zhang. The presented corner detector has better detection accuracy, less sensitivity to noisy and fuzzy images, high computational efficiency, and good repeatability.