MATLAB PROJECT
Online Subspace
Learning from Gradient Orientations for Robust Image Alignment
Abstract:
Robust and efficient image
alignment remains a challenging task, due to the massiveness of images, great
illumination variations between images, partial occlusion, and corruption. To
address these challenges, we propose an online image alignment method via
subspace learning from image gradient orientations (IGOs). The proposed method
integrates the subspace learning, transformed the IGO reconstruction and image
alignment into a unified online framework, which is robust for aligning images
with severe intensity distortions. Our method is motivated by a principal
component analysis (PCA) from gradient orientations that provides more reliable
low-dimensional subspace than that from pixel intensities. Instead of
processing in the intensity-domain-like conventional methods, we seek alignment
in the IGO domain, such that the aligned IGO of the newly arrived image can be
decomposed as the sum of a sparse error and a linear composition of the IGO-PCA
basis learned from previously well-aligned ones. The optimization problem is
tackled by an iterative linearization that minimizes the ℓ 1 -norm
of the sparse error. Furthermore, the IGO-PCA basis is adaptively updated based
on incremental thin singular value decomposition, which takes the shift of IGO
mean into consideration. The efficacy of the proposed method is validated on
the extensive challenging datasets through image alignment, medical atlas
construction, and face recognition. The experimental results demonstrate that
our algorithm provides more illumination- and occlusion-robust image alignment
than the state-of-the-art methods.
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