Multispectral Image Matching Technique

In this paper, we present a method for matching corner points in un-calibrated wide-baseline images. The images used in our study are both in the visible and Near Infra Red (NIR) spectrums. The algorithm starts by detecting corner points and for each point a vector based on first order derivatives is constructed. We show that adding the NIR images can improve the matching process. The main contribution of this paper is that we present an invariant vector to match wide-baseline multispectral images.

[1]  H. C. Longuet-Higgins,et al.  A computer algorithm for reconstructing a scene from two projections , 1981, Nature.

[2]  Allen R. Hanson,et al.  Obstacle Detection Based on Qualitative and Quantitative 3D Reconstruction , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[4]  Stefan Carlsson.  Recognizing Walking People , 2003, Int. J. Robotics Res..

[5]  Hassan Hajjdiab,et al.  Wide baseline obstacle detection and localization , 2003, Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings..

[6]  Narendra Ahuja,et al.  Two-view Matching , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[7]  Semyon Vaynman,et al.  Flux development for lead-free solders containing zinc , 2000 .

[8]  Cordelia Schmid,et al.  Evaluation of Interest Point Detectors , 2000, International Journal of Computer Vision.

[9]  Zhengyou Zhang,et al.  Flexible camera calibration by viewing a plane from unknown orientations , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[10]  Rachid Deriche,et al.  Matching color uncalibrated images using differential invariants , 2000, Image Vis. Comput..

[11]  O. Faugeras.  Three-dimensional computer vision: a geometric viewpoint , 1993 .

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

[13]  Kenichi Kanatani,et al.  Calibration of a Moving Camera Using a Planar Pattern: Optimal Computation, Relibility Evaluation, and Stabilization by Model Selection , 1999, ECCV.

[14]  Zhengyou Zhang,et al.  Determining the Epipolar Geometry and its Uncertainty: A Review , 1998, International Journal of Computer Vision.

[15]  D. Q. Yu,et al.  Investigation of interfacial microstructure and wetting property of newly developed Sn–Zn–Cu solders with Cu substrate , 2004 .

[16]  Kwang-Lung Lin,et al.  Wetting interaction between Sn-Zn-Ag solders and Cu , 2003 .

[17]  Hans P. Moravec.  Obstacle avoidance and navigation in the real world by a seeing robot rover , 1980 .

[18]  C. L. Wu,et al.  The wettability and microstructure of Sn-Zn-RE alloys , 2003 .

[19]  Charles E. Thorpe,et al.  A specialized multibaseline stereo technique for obstacle detection , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[20]  Marie-Odile Berger,et al.  Iterative Multi-Planar Camera Calibration: Improving Stability using Model Selection , 2003, VVG.

[21]  Emanuele Trucco,et al.  Introductory techniques for 3-D computer vision , 1998 .

[22]  Bill Triggs,et al.  Autocalibration from Planar Scenes , 1998, ECCV.

[23]  Pär Fornland.  Direct Obstacle Detection and Motion from Spatio-Temporal Derivatives , 1995, CAIP.