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Distorted Building Image Matching with Automatic Viewpoint Rectification and Fusion.

Linwei Yue1, Hongjie Li2, Xianwei Zheng2

  • 1School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China.

Sensors (Basel, Switzerland)
|December 1, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for matching distorted urban building images. The approach significantly enhances both the number and accuracy of feature correspondences in urban image matching applications.

Keywords:
building image matchinggrid-based motion statisticsrepetitive structuretransform invariant low-rank textures (TILT)viewpoint fusion

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Area of Science:

  • Computer Vision
  • Photogrammetry
  • Urban Planning

Background:

  • Accurate building image matching is crucial for urban applications.
  • Challenges exist in finding reliable feature correspondences between images with widely separated viewpoints.
  • Existing methods struggle with distortions and significant viewpoint variations.

Purpose of the Study:

  • To propose a robust distorted image matching method for urban buildings.
  • To improve the reliability and sufficiency of feature correspondences in challenging urban scenes.
  • To enhance the precision and number of matching pairs for distorted building images.

Main Methods:

  • Viewpoint rectification using the transform invariant low-rank textures (TILT) algorithm.
  • Extraction of local symmetry feature graphs and multi-level clustering for low-rank texture detection.
  • Image matching using Oriented FAST and Rotated BRIEF (ORB) features post-rectification.
  • Outlier removal with grid-based motion statistics (GMS) and RANSAC.
  • Fusion of matches from rectified and original viewpoints.

Main Results:

  • The proposed method significantly improves the number of matching pairs for distorted building images.
  • Matching precision is substantially enhanced compared to traditional methods.
  • Effective handling of viewpoint variations and image distortions was demonstrated.

Conclusions:

  • The combined approach of viewpoint rectification and feature fusion offers a robust solution for urban building image matching.
  • The method effectively addresses the challenges of feature correspondence in widely separated views.
  • This technique has strong potential for improving various urban applications reliant on accurate image analysis.