摘要
In feature based image matching,distinctive features in images are detected and represented by feature descriptors.Matching is then carried out by assessing the similarity of the descriptors of potentially conjugate points.In this paper,we first shortly discuss the general frame-work.Then,we review feature detection as well as the determination of affine shape and orientation of local features,before analyzing feature description in more detail.In the feature description review,the general framework of local feature description is presented first.Then,the review discusses the evolution from hand-crafted feature descriptors,e.g.SIFT(Scale Invariant Feature Transform),to machine learning and deep learning based descriptors.The machine learning models,the training loss and the respective training data of learning-based algorithms are looked at in more detail;subsequently the various advantages and challenges of the different approaches are discussed.Finally,we present and assess some current research directions before concluding the paper.
基金
The authors would like to thank NVIDIA Corp.for donating the GPU used in this research through its GPU grant program.The first author Lin Chen would also like to thank the China Scholarship Council(CSC)for financially supporting his PhD study.
