This Blog is maintained by the Robot Perception and Learning lab at CSIE, NTU, Taiwan.
Our scientific interests are driven by the desire to build intelligent robots and computers, which are capable of servicing people more efficiently than equivalent manned systems in a wide variety of dynamic and unstructured environments.
Wednesday, October 01, 2014
Lab Meeting, October 2, 2014(Yun-Jun Shen): Multi-modal and Multi-spectral Registration for Natural Images
Title: Multi-modal and Multi-spectral Registration for Natural Images
Authors: Xiaoyong Shen , Li Xu, Qi Zhang, and Jiaya Jia
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.