Non-rigid point set registration via global and local constraints

Non-rigid point set registration is often encountered in meical image processing, pattern recognition, and computer vision. This paper presents a new method for non-rigid point set registration that can be used to recover the underlying coherent spatial mapping (CSM). Firstly, putative correspondences between two point sets are established by using feature descriptors. Secondly, each point is expressed as a weighted sum of several nearest neighbors and the same relation holds after the transformation. Then, this local geometrical constraint is combined with the global model, and the transformation problem is solved by minimizing an error function. These two steps of recovering point correspondences and transformation are performed iteratively to obtained a promising result. Extensive experiments on various synthetic and real data demonstrate that the proposed approach is robust and outperforms the state-of-the-art methods.

[1]  Lisa M. Brown,et al.  A survey of image registration techniques , 1992, CSUR.

[2]  Zhuowen Tu,et al.  Robust $L_{2}E$ Estimation of Transformation for Non-Rigid Registration , 2015, IEEE Transactions on Signal Processing.

[3]  Zhuowen Tu,et al.  Regularized vector field learning with sparse approximation for mismatch removal , 2013, Pattern Recognit..

[4]  Baba C. Vemuri,et al.  Robust Point Set Registration Using Gaussian Mixture Models , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Junjun Jiang,et al.  Non-Rigid Point Set Registration with Robust Transformation Estimation under Manifold Regularization , 2017, AAAI.

[6]  Alan L. Yuille,et al.  Non-Rigid Point Set Registration by Preserving Global and Local Structures , 2016, IEEE Transactions on Image Processing.

[7]  Andrew W. Fitzgibbon.  Robust registration of 2D and 3D point sets , 2003, Image Vis. Comput..

[8]  Li Ma,et al.  Non-rigid point set registration via coherent spatial mapping , 2015, Signal Process..

[9]  Anand Rangarajan,et al.  A new point matching algorithm for non-rigid registration , 2003, Comput. Vis. Image Underst..

[10]  Lei Zhang,et al.  Robust Point Matching Revisited: A Concave Optimization Approach , 2012, ECCV.

[11]  Yongdong Zhang,et al.  A Highly Parallel Framework for HEVC Coding Unit Partitioning Tree Decision on Many-core Processors , 2014, IEEE Signal Processing Letters.

[12]  Jihua Zhu,et al.  Robust non-rigid point set registration via building tree dynamically , 2016, Multimedia Tools and Applications.

[13]  Zhuowen Tu,et al.  Robust Estimation of Nonrigid Transformation for Point Set Registration , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Jiayi Ma,et al.  A Mixture Model for Robust Point Matching under Multi-Layer Motion , 2014, PloS one.

[15]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[16]  Andriy Myronenko,et al.  Point Set Registration: Coherent Point Drift , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Yongdong Zhang,et al.  Efficient Parallel Framework for HEVC Motion Estimation on Many-Core Processors , 2014, IEEE Transactions on Circuits and Systems for Video Technology.

[18]  David S. Doermann,et al.  Robust point matching for nonrigid shapes by preserving local neighborhood structures , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Paolo Toth,et al.  Algorithm 548: Solution of the Assignment Problem [H] , 1980, TOMS.

[20]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[21]  Jing Tian,et al.  Retinal image registration using topological vascular tree segmentation and bifurcation structures , 2015, Biomed. Signal Process. Control..

[22]  Zhuowen Tu,et al.  Robust Point Matching via Vector Field Consensus , 2014, IEEE Transactions on Image Processing.

[23]  Haibin Ling,et al.  Shape Classification Using the Inner-Distance , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Junjun Jiang,et al.  Robust Feature Matching for Remote Sensing Image Registration via Locally Linear Transforming , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Junjun Jiang,et al.  Guided Locality Preserving Feature Matching for Remote Sensing Image Registration , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[26]  Yongdong Zhang,et al.  Effective Uyghur Language Text Detection in Complex Background Images for Traffic Prompt Identification , 2018, IEEE Transactions on Intelligent Transportation Systems.

[27]  Grace Wahba,et al.  Spline Models for Observational Data , 1990 .

[28]  Edward H. Adelson,et al.  The Design and Use of Steerable Filters , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[30]  Yansheng Li,et al.  Feature guided Gaussian mixture model with semi-supervised EM and local geometric constraint for retinal image registration , 2017, Inf. Sci..

[31]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[32]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[33]  Haibin Ling,et al.  An Efficient Earth Mover's Distance Algorithm for Robust Histogram Comparison , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  Yongdong Zhang,et al.  Supervised Hash Coding With Deep Neural Network for Environment Perception of Intelligent Vehicles , 2018, IEEE Transactions on Intelligent Transportation Systems.