Paper Info
Title
From handcrafted to deep local invariant features.
Abstract
This paper presents an overview of the evolution of local features from handcrafted to deep-learning-based methods, followed by a discussion of several benchmarks and papers evaluating such local features. Our investigations are motivated by 3D reconstruction problems, where the precise location of the features is important. As we describe these methods, we highlight and explain the challenges of feature extraction and potential ways to overcome them. We first present handcrafted methods, followed by methods based on classical machine learning and finally we discuss methods based on deep-learning. This largely chronologically-ordered presentation will help the reader to fully understand the topic of image and region description in order to make best use of it in modern computer vision applications. In particular, understanding handcrafted methods and their motivation can help to understand modern approaches and how machine learning is used to improve the results. We also provide references to most of the relevant literature and code.
Year
Venue
Field
2018
arXiv: Computer Vision and Pattern Recognition
Computer science,Feature extraction,Invariant (mathematics),Artificial intelligence,Machine learning,3D reconstruction
DocType
Volume
Citations 
Journal
abs/1807.10254
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Gabriela Csurka197285.08
Martin Humenberger221715.74