Feature Detectors and Descriptors Generations With Numerous Images and Video Applications

Abstract

Feature detectors have a critical role in numerous applications such as camera calibrations, object recognition, biometrics, medical applications and image/video retrieval. One of its main tasks is to extract point correspondences “Interest points” between two similar scenes, objects, images or video shots. Extensive research has been done concerning the progress of visual feature detectors and descriptors to be robust against image deformations and achieve reduced computational speed in real-time applications. The current chapter introduced an overview of feature detectors such as Moravec, Hessian, Harris and FAST (Features from Accelerated Segment Test). It addressed the feature detectors' generation over time, the principle concept of each type, and their use in image/video applications. Furthermore, some recent feature detectors are addressed. A comparison based on these points is performed to illustrate their respective strengths and weaknesses to be a base for selecting an appropriate detector according to the application under concern.