Contour-based image registration with local deformations

This research is focused on the study of local-type image registration techniques, which concern pixel mapping between two correlated images with both global and local deformations. Our algorithm is object-contour-based, meaning that control points that conduct the transformation are adaptively selected from the matched contour points. It is novel in two important respects. First, we propose to match object contours in the source and target images by using locally-maximal-curvature points and curve projection. Second, the selection of control points is capable of adapting to both global and local deformations, normally being denser in contour segments with substantial local deformations. The proposed scheme is also hybrid in the sense that local contour matching and global surface-spline fitting of control points are combined. Simulations and some comparisons are made by using infrared thermographs and ordinary gray images. Experiments show better registration accuracy of object contours than with traditional methods such as elastic or curvature scale space matching.

[1]  Raimondo Schettini,et al.  Image registration by recognition of corresponding structures , 1990 .

[2]  Gerald Q. Maguire,et al.  Evaluation of polynomial image deformation using anatomical landmarks for matching of 3D-abdominal MR-images and for atlas construction , 1999 .

[3]  David J. Burr,et al.  Elastic Matching of Line Drawings , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Sadegh Abbasi,et al.  Shape similarity retrieval under affine transforms , 2002, Pattern Recognit..

[5]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[6]  Siamak Khorram,et al.  The effects of image misregistration on the accuracy of remotely sensed change detection , 1998, IEEE Trans. Geosci. Remote. Sens..

[7]  S. Khorram,et al.  A hierarchical methodology framework for multisource data fusion in vegetation classification , 1998 .

[8]  A. Ardeshir Goshtasby,et al.  Piecewise cubic mapping functions for image registration , 1987, Pattern Recognit..

[9]  Josef Kittler,et al.  Enhancing CSS-based shape retrieval for objects with shallow concavities , 2000, Image Vis. Comput..

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

[11]  Azriel Rosenfeld,et al.  Point pattern matching by relaxation , 1980, Pattern Recognit..

[12]  A. Ardeshir Goshtasby,et al.  Registration of images with geometric distortions , 1988 .

[13]  Siamak Khorram,et al.  A feature-based image registration algorithm using improved chain-code representation combined with invariant moments , 1999, IEEE Trans. Geosci. Remote. Sens..

[14]  Kuo-Chin Fan,et al.  Image Registration Using a New Edge-Based Approach , 1997, Comput. Vis. Image Underst..

[15]  D. Burr.  A dynamic model for image registration , 1981 .

[16]  Jerry L. Prince,et al.  Image registration based on boundary mapping , 1996, IEEE Trans. Medical Imaging.

[17]  Jerry L. Prince,et al.  An active contour model for mapping the cortex , 1995, IEEE Trans. Medical Imaging.

[18]  Shinji Umeyama.  Parameterized Point Pattern Matching and Its Application to Recognition of Object Families , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Jan Flusser,et al.  A moment-based approach to registration of images with affine geometric distortion , 1994, IEEE Trans. Geosci. Remote. Sens..

[20]  Wieslaw Lucjan Nowinski,et al.  Talairach-Tournoux brain atlas registration using a metalforming principle-based finite element method , 2001, Medical Image Anal..

[21]  Mahua Bhattacharya,et al.  Registration of CT and MR images of Alzheimer's patient: a shape theoretic approach , 2000, Pattern Recognit. Lett..

[22]  B. S. Manjunath,et al.  A contour-based approach to multisensor image registration , 1995, IEEE Trans. Image Process..

[23]  Hui Henry Li,et al.  Automatic visual/IR image registration , 1996 .

[24]  J. L. Moigne.  Parallel registration of multisensor remotely sensed imagery using wavelet coefficients , 1994 .

[25]  F. S. Cohen,et al.  Registering coronal histological 2-D sections of a rat brain with coronal sections of a 3-D brain atlas using geometric curve invariants and B-spline representation , 1998, IEEE Transactions on Medical Imaging.

[26]  W. Lie,et al.  Fast and accurate snake model for object contour detection , 2001 .

[27]  Michael Werman,et al.  Affine point matching , 1994, Pattern Recognit. Lett..

[28]  Jan Flusser,et al.  An adaptive method for image registration , 1992, Pattern Recognit..

[29]  Farzin Mokhtarian,et al.  Silhouette-Based Isolated Object Recognition through Curvature Scale Space , 1995, IEEE Trans. Pattern Anal. Mach. Intell..