A coarse-to-fine scheme for groupwise registration of multisensor images. (14th November 2016)

• Record Type: Journal Article
• Authors: Li, Yinghao; He, Zhongshi; Zhu, Hao; Zou, Dongsheng; Zhang, Weiwei
• Is Part Of: International journal of advanced robotic systems. Volume 13:Number 6(2016)
• Publication Date: 2016-11-14
• Subjects: Image registration -- multi-image -- coarse-to-fine -- image segmentation -- mixture model; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881416673302
• Languages: English
• ISSNs: 1729-8806

Abstract

Ensemble registration is concerned with a group of images that need to be registered simultaneously. It is challenging but important for many image analysis tasks such as vehicle detection and medical image fusion. To solve this problem effectively, a novel coarse-to-fine scheme for groupwise image registration is proposed. First, in the coarse registration step, unregistered images are divided into reference image set and float image set. The images of the two sets are registered based on segmented region matching. The coarse registration results are used as an initial solution for the next step. Then, in the fine registration step, a Gaussian mixture model with a local template is used to model the joint intensity of coarse-registered images. Meanwhile, a minimum message length criterion-based method is employed to determine the unknown number of mixing components. Based on this mixture model, a maximum likelihood framework is used to register a group of images. To evaluate the performance of the proposed approach, some representative groupwise registration approaches are compared on different image data sets. The experimental results show that the proposed approach has improved performance compared to conventional approaches.