Image registration is an important technique in many computer vision applications such as image fusion, image retrieval, object tracking, face recognition, change detection and so on. Local feature descriptors, i.e., how to detect features and how to describe them, play a fundamental and important role in image registration process, which directly influence the accuracy and robustness of image registration. This paper mainly focuses on the variety of local feature descriptors including some theoretical research, mathematical models, and methods or algorithms along with their applications in the context of image registration. The existing local feature descriptors are roughly classified into six categories to demonstrate and analyze comprehensively their own advantages. The current and future challenges of local feature descriptors are discussed. The major goal of the paper is to present a unique survey of the state-of-the-art image matching methods based on feature descriptor, from which future research may benefit. INDEX TERMS Local feature descriptor, image matching, point pattern matching, pattern recognition.
With rapid advancements in remote sensing image registration algorithms, comprehensive imaging applications are no longer limited to single-modal remote sensing images. Instead, multi-modal remote sensing (MMRS) image registration has become a research focus in recent years. However, considering multi-source, multi-temporal, and multi-spectrum input introduces significant nonlinear radiation differences in MMRS images for which researchers need to develop novel solutions. At present, comprehensive reviews and analyses of MMRS image registration methods are inadequate in related fields. Thus, this paper introduces three theoretical frameworks: namely, area-based, feature-based and deep learning-based methods. We present a brief review of traditional methods and focus on more advanced methods for MMRS image registration proposed in recent years. Our review or comprehensive analysis is intended to provide researchers in related fields with advanced understanding to achieve further breakthroughs and innovations.
Optical molecular imaging is an important technique of studies at molecular level and provides promising tools to non-invasively delineate in vivo physiological and pathological activities at cellular and molecular levels, and it has been widely used for diagnosing, managing diseases, metastasis detection and drug development. From a mathematical perspective, this paper mainly focuses on the forward problem and inverse problem in biological tissues based on the radiative transfer equation (RTE). The forward problem is accustomed to describing photon propagation in biological tissues and the inverse problem is used to reconstruct internal source distribution from the signal detected on the external surface. We also introduce the detailed derivation of the RTE and Robin boundary condition and discretization of the forward problem, along with the reconstruction methods and iterative solution algorithms summarized for the inverse problem. Finally, the current and future challenges of optical molecular imaging are discussed. This survey aims to construct a mathematical method, a state-of-the-art framework for optical molecular imaging, from which future research may benefit.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.