Applying SIFT Descriptors to Stellar Image Matching

Stellar image matching allows to verify if a given pair of images belongs to the same stellar object/area, or knowing that they correspond to the same sky area, to verify if there are some changes between them due to an stellar event (supernova event, changes in the object position, etc). Some applications are stellar photometry, telescope tracking and pointing, robot telescopes, and sky monitoring. However, the matching of stellar images is a hard problem because normally the images are taken using different telescopes, image sensors and settings, as well as from different places, which produces variability in the image's resolution, orientation, and field of view. In this context, the aim of this paper is to propose a robust SIFT-based wide baseline matching technique for stellar images. The proposed technique was evaluated in a new database composed by 100 pairs of galaxies, nebulas and star clusters images, achieving a true positive rate of 87% with a false positive rate of 1.7%.

[1]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[2]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[3]  Hoon Kang,et al.  Segmentation Tracking and Recognition Based on Foreground-Background Absolute Features, Simplified SIFT, and Particle Filters , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[4]  Cordelia Schmid,et al.  Local Grayvalue Invariants for Image Retrieval , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Luc Van Gool,et al.  Simultaneous Object Recognition and Segmentation by Image Exploration , 2004, ECCV.

[6]  Javier Ruiz-del-Solar,et al.  Improving SIFT-Based Object Recognition for Robot Applications , 2005, ICIAP.

[7]  Fabio Roli,et al.  Image Analysis and Processing - ICIAP 2005, 13th International Conference, Cagliari, Italy, September 6-8, 2005, Proceedings , 2005, ICIAP.

[8]  Mubarak Shah,et al.  A 3-dimensional sift descriptor and its application to action recognition , 2007, ACM Multimedia.

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

[10]  Javier Ruiz-del-Solar,et al.  A New Approach for Fingerprint Verification Based on Wide Baseline Matching Using Local Interest Points and Descriptors , 2007, PSIVT.

[11]  Javier Ruiz-del-Solar,et al.  A Fast Probabilistic Model for Hypothesis Rejection in SIFT-Based Object Recognition , 2006, CIARP.

[12]  Luis Rueda,et al.  Advances in Image and Video Technology, Second Pacific Rim Symposium, PSIVT 2007, Santiago, Chile, December 17-19, 2007, Proceedings , 2007, PSIVT.

[13]  David G. Lowe,et al.  Local feature view clustering for 3D object recognition , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[14]  Javier Ruiz-del-Solar,et al.  Gaze Direction Determination of Opponents and Teammates in Robot Soccer , 2005, RoboCup.

[15]  José Francisco Martínez-Trinidad,et al.  Progress in Pattern Recognition, Image Analysis and Applications, 12th Iberoamericann Congress on Pattern Recognition, CIARP 2007, Valparaiso, Chile, November 13-16, 2007, Proceedings , 2008, CIARP.

[16]  Jiří Matas,et al.  Computer Vision - ECCV 2004 , 2004, Lecture Notes in Computer Science.

[17]  James J. Little,et al.  Vision-based mobile robot localization and mapping using scale-invariant features , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[18]  Keith Baker,et al.  5th Alvey vision Conference , 1990, Image Vis. Comput..

[19]  Cordelia Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.