  Establishing correspondences is a fundamental task in variety of 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 efficient
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. In addition, robust feature descriptors are
reformulated with LAT for several practical applications. Furthermore,
LAT-convolution layer and Aception block are proposed and, with these novel
components, deep neural network called LAT-Net is proposed especially for scene
recognition task. Experimental results show that LATransformed images provide a
consistency for nonlinearly deformed images, even under random intensity
deformations. LAT reduces the mean absolute difference 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. the local area
can be considered as an alternative domain to the intensity domain to achieve
robust correspondence matching, image recognition, and a lot of applications:
such as feature matching, stereo matching, dense correspondence matching, image
recognition, and image retrieval.
