Robust Click-Point Linking: Matching Visually Dissimilar Local Regions

This paper presents robust click-point linking: a novel localized registration framework that allows users to interactively prescribe where the accuracy has to be high. By emphasizing locality and interactivity, our solution is faithful to how the registration results are used in practice. Given a user-specified point, the click-point linking provides a single point-wise correspondence between a data pair. In order to link visually dissimilar local regions, a correspondence is sought by using only geometrical context without comparing the local appearances. Our solution is formulated as a maximum likelihood estimation (MLE) without estimating a domain transformation explicitly. A spatial likelihood of Gaussian mixture form is designed to capture geometrical configurations between the point-ofinterest and a hierarchy of global-to-local 3D landmarks that are detected using machine learning and entropy based feature detectors. A closed-form formula is derived to specify each Gaussian com...

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Kazunori Okada, Xiaolei Huang
• Keywords: Computer Vision, CVPR 2007, Dissimilar Local Regions, Domain Transformation, Gaussian Mixture Form, Local Mle Problem, Maximum Likelihood Estimation