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When Deep Learning Meets Data Alignment: A Review on Deep Registration Networks (DRNs)
Applied Sciences ( IF 2.838 ) Pub Date : 2020-10-26 , DOI: 10.3390/app10217524
Victor Villena-Martinez , Sergiu Oprea , Marcelo Saval-Calvo , Jorge Azorin-Lopez , Andres Fuster-Guillo , Robert B. Fisher

Registration is the process that computes the transformation that aligns sets of data. Commonly, a registration process can be divided into four main steps: target selection, feature extraction, feature matching, and transform computation for the alignment. The accuracy of the result depends on multiple factors, the most significant are the quantity of input data, the presence of noise, outliers and occlusions, the quality of the extracted features, real-time requirements and the type of transformation, especially those ones defined by multiple parameters, like non-rigid deformations. Recent advancements in machine learning could be a turning point in these issues, particularly with the development of deep learning (DL) techniques, which are helping to improve multiple computer vision problems through an abstract understanding of the input data. In this paper, a review of deep learning-based registration methods is presented. We classify the different papers proposing a framework extracted from the traditional registration pipeline to analyse the new learning-based proposal strengths. Deep Registration Networks (DRNs) try to solve the alignment task either replacing part of the traditional pipeline with a network or fully solving the registration problem. The main conclusions extracted are, on the one hand, 1) learning-based registration techniques cannot always be clearly classified in the traditional pipeline. 2) These approaches allow more complex inputs like conceptual models as well as the traditional 3D datasets. 3) In spite of the generality of learning, the current proposals are still ad hoc solutions. Finally, 4) this is a young topic that still requires a large effort to reach general solutions able to cope with the problems that affect traditional approaches.

中文翻译:

当深度学习遇到数据对齐:深度注册网络 (DRN) 综述

配准是计算对齐数据集的转换的过程。通常,配准过程可以分为四个主要步骤:目标选择、特征提取、特征匹配和对齐的变换计算。结果的准确性取决于多种因素,最重要的是输入数据的数量、噪声、异常值和遮挡的存在、提取特征的质量、实时性要求和变换类型,尤其是那些定义的通过多个参数,如非刚性变形。机器学习的最新进展可能是这些问题的转折点,特别是随着深度学习 (DL) 技术的发展,这些技术通过对输入数据的抽象理解来帮助改善多个计算机视觉问题。在本文中,回顾了基于深度学习的配准方法。我们对提出从传统注册管道中提取的框架的不同论文进行分类,以分析新的基于学习的提案优势。深度注册网络 (DRN) 试图解决对齐任务,要么用网络替换部分传统管道,要么完全解决注册问题。提取的主要结论是,一方面,1)基于学习的注册技术不能总是在传统管道中明确分类。2)这些方法允许更复杂的输入,如概念模型以及传统的 3D 数据集。3)尽管学习具有普遍性,但目前的建议仍然是临时解决方案。最后,
更新日期:2020-10-26
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