Multi-modal and Multi-spectral Registration for Natural Images

Xiaoyong Shen¹, Li Xu², Qi Zhang¹, and Jiaya Jia¹

¹The Chinese University of Hong Kong, China

²Image & Visual Computing Lab, Lenovo R&T, Project Website, Hong Kong, China
http://www.cse.cuhk.edu.hk/leojia/projects/multimodal

Abstract. Images now come in different forms – color, near-infrared, depth, etc. – due to the development of special and powerful cameras in computer vision and computational photography. Their cross-modal correspondence establishment is however left behind. We address this challenging dense matching problem considering structure variation possibly existing in these image sets and introduce new model and solution. Our main contribution includes designing the descriptor named robust selective normalized cross correlation (RSNCC) to establish dense pixel correspondence in input images and proposing its mathematical parameterization to make optimization tractable. A computationally robust framework including global and local matching phases is also established. We build a multi-modal dataset including natural images with labeled sparse correspondence. Our method will benefit image and vision applications that require accurate image alignment.

Keywords: multi-modal, multi-spectral, dense matching, variational model

LNCS 8692, p. 309 ff.

Full article in PDF | BibTeX

lncs@springer.com
© Springer International Publishing Switzerland 2014