Illumination-robust feature detection based on adaptive threshold function

Feature detection is the basis of many computer vision applications. However, the existing feature detectors have poor illumination robustness for various reasons. FAST is a very effective detection method, and is currently widely used for real-time feature detection. The threshold function in the traditional FAST method is a linear function and is unable to deal with the issue of illumination robustness. This paper proposes an illumination-robust feature detection method, the core is an adaptive threshold FAST. The proposed method constructs a threshold function based on neighborhood standard deviation, which successfully solves the problem that the traditional FAST has poor illumination robustness. In addition, a new image preprocessing method consisting of homomorphic filtering and histogram equalization is introduced to the front-end of the proposed method in order to improve the quality of the input image. Compared with state-of-the-art methods, the repeatibility rate of proposed method has been increased several times in the underexposure matching experiment, and the number of repeated features has been increased by dozens of times. Meanwhile, the number of repeated features increased by more than a third on average in the overexposed experiment. The experimental results strongly prove that the proposed method has significant advantages in terms of repeatibility rate, number of repeated features and detection stability evaluation indices.