  Reliable corner detection is an important task in determining the shape of
different regions within an image. Real-life image data are always imprecise
due to inherent uncertainties that may arise from the imaging process such as
defocusing, illumination changes, noise, etc. Therefore, the localization and
detection of corners has become a difficult task to accomplish under such
imperfect situations. On the other hand, Fuzzy systems are well known for their
efficient handling of impreciseness and incompleteness, which make them
inherently suitable for modelling corner properties by means of a rule-based
fuzzy system. The paper presents a corner detection algorithm which employs
such fuzzy reasoning. The robustness of the proposed algorithm is compared to
well-known conventional corner detectors and its performance is also tested
over a number of benchmark images to illustrate the efficiency of the algorithm
under uncertainty.
