LAT: Local area transform for cross modal correspondence matching

Seungchul Ryu, Seungryong Kim, Kwanghoon Sohn

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Establishing correspondences is a fundamental task in many image processing and computer vision applications. In particular, finding the correspondences between a non-linearly deformed image pair induced by different modality conditions is a challenging problem. This paper describes a simple but powerful image transform called local area transform (LAT) for modality-robust correspondence estimation. Specifically, LAT transforms an image from the intensity domain to the local area domain, which is invariant under nonlinear intensity deformations, especially radiometric, photometric, and spectral deformations. Experimental results show that LATransformed images provide a consistency for nonlinearly deformed images, even under random intensity deformations. LAT reduces the mean absolute difference by approximately 0.20 and the different pixel ratio by approximately 58% on average, as compared to conventional methods. Furthermore, the reformulation of descriptors with LAT shows superiority to conventional methods, which is a promising result for the tasks of cross-spectral and modality correspondence matching. LAT gains an approximately 23% improvement in the correct detection ratio and a 10% improvement in the recognition rate for the tasks of RGB-NIR cross-spectral template matching and cross-spectral feature matching, respectively. LAT reduces the bad pixel percentage by approximately 15% and the root mean squared errors by 13.5 in the task of cross-radiation stereo matching. LAT also improves the cross-modal dense flow estimation task in terms of warping error, providing 50% error reduction.

Original languageEnglish
Pages (from-to)218-228
Number of pages11
JournalPattern Recognition
Volume63
DOIs
Publication statusPublished - 2017 Mar 1

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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