• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International journal of image and data fusion >An overview of deep learning methods for image registration with focus on feature-based approaches
【24h】

An overview of deep learning methods for image registration with focus on feature-based approaches

机译：专注于基于特征的方法的图像登记深层学习方法概述

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Image registration is an essential pre-processing step for several computer vision problems like image reconstruction and image fusion. In this paper, we present a review on image registration approaches using deep learning. The focus of the survey presented is on how conventional image registration methods such as area-based and feature-based methods are addressed using deep net architectures. Registration approach adopted depends on type of images and type of transformation used to describe the deformation between the images in an application. We then present a comparative performance analysis of convolutional neural networks that have shown good performance across feature extraction, matching and transformation estimation in featured-based registration. Experimentation is done on each of these approaches using a dataset of aerial images generated by inducing deformations such as scale.
机译：图像注册是用于图像重建和图像融合等几个计算机视觉问题的基本预处理步骤。在本文中，我们对使用深度学习进行了综述了图像登记方法。所提出的调查的重点是如何使用深网络架构解决常规图像登记方法，例如基于区域和基于特征的方法。采用的登记方法取决于用于描述应用中图像之间的变形的图像类型和变换类型。然后，我们对卷积神经网络的比较绩效分析，在特色的注册中具有特征提取，匹配和转换估算的良好性能。使用通过诱导诸如规模的变形而产生的空中图像的数据集来完成这些方法的每个方法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号