Center surround feature detection of volumetric data

摘要

The process of feature point detection is the first stage of many computer vision applications including object detection, recognition and reconstruction. Aimed at the drawbacks (e.g. inefficiency and poor rotation invariance) of some classical detectors for volumetric data, we present a new scale-invariant center surround feature detector. Combining a 3D center surround feature point detector and the use of an integral image will accelerate the building of the scale space. Meantime, we also use Harris criterion to perform line suppression to enhance the stability of feature point detection. The experimental results indicate that our new detectors make the balance between computational efficiency and repetition rate.