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Comprehensive interest point based imaging mosaic

Tian, Gui Yun, Gledhill, Duke and Taylor, David (2003) Comprehensive interest point based imaging mosaic. Pattern Recognition Letters, 24 (9). pp. 1171-1179. ISSN 0167-8655

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Abstract

This paper begins by reviewing the current state-of-the-art for imaging mosaic and applications. Then a new approach that uses a four-step automatic imaging mosaic, based on interest points, is proposed. The four steps are identification of interest points, finding corresponding points in the stitching images, deriving the spatial and spectral transform matrix then image mosaic and smoothing. The advantage of this approach is that it is robust to image capture conditions such as lighting, rotation, viewpoint and capture device.

Item Type: Article
Additional Information: © 2002 Elsevier Science B.V.
Uncontrolled Keywords: Interest points; Image mosaic; Homography; Image features; Image understanding
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Schools: School of Computing and Engineering
School of Computing and Engineering > High-Performance Intelligent Computing > Visualisation, Interaction and Vision
School of Computing and Engineering > Serious Games Research Group
References: Apple Computer Inc., 1995. An overview of apples QuickTime VR technology. Available from: <http://quicktime.apple. com/qtvr/qtvrtech5_25.html>. Bao, P., Xu, D., 1999. Complex wavelet-based image mpsaics using edge-preserving visual perception modeling. Comput. Graphics 23, 309–321. Can, A., Stewart, C., Roysam, B., 1999. Robust hierarchical algorithm for constructing a mosaic from images of the curved human retina, in: Proceedings of CVPR, pp. 286– 292. Can, A., Stewart, C., Roysam, B., Tanenbaum, H.L., 2000. A Feature-based technique for joint, linear estimation of highorder image-to-mosaic transformations: Application to Mosaicing the curved human retina, in: Proceedings of CVPR, pp. 523–530. Caspi, Y., Irani, M., 2001. Alignment of non-overlapping sequences, in: Proceedings of ICCV 2001, vol. II, pp. 76–83. Finlayson, G.D., Tian, G.Y., 1999. Color normalization for color object recognition. Int. J. Pattern Recognit. Artif. Intell. 13 (8), 1271–1285. Finlayson, G.D., Tian, G.Y., 1999. Color indexing across illumination, in: Proceedings of CIR 99, Newcastle, Session 2and Electronic Journal, pp. 1–7. Funt, B.V., Lewis, B.C., 2000. Diagonal versus affine transformations for color correction. J. Opt. Soc. Am. A 17 (11). Harris, C., Stephens, M., 1988. A combined corner and edge detector. Fourth Alvey Vision Conference, pp. 147–151. Healey, G., Slater, D., 1995. Global color constancy: recognition of objects by use of illumination invariant properties of color distributions. J. Opt. Soc. Am. A 11 (11), 3003–3010. Lowe, D.G., 1999. Object recognition from local scale-invariant features, in: Proceedings of the International Conference on Computer Vision, Corfu, September. Luong, Q.T., Faugeras, O.D., 1997. Self-calibration of a moving camera from point correspondences and fundamental matrices. Int. J. Comput. Vision 22 (3), 261–289. Ojala, T., 1997. Nonparameteric texture analysis using spatial operators, with applications in visual inspection, PhD thesis, University of Oulu. Faugeras, O., 1993. Three Dimensional Computer Vision: A Geometric Viewpoint. MIT Press. Szeliski, R., Shum, H.-Y., 1998. Creating full view panoramic image mosaics and environment maps. Schmid, C., Mohr, R., 1997. Local gray-value invariants for image retrieval. IEEE Trans. Pattern Anal. Machine Intell. 19 (5), 530–535. Schmid, C., Mohr, R., Bauckhage, C., 1998. Comparing and evaluating interest points, in: Proceedings of the International Conference on Computer Vision, Bombay, January. Seitz, S., 2001. The Space of all stereo images, in: Proceedings of ICCV 2001, Vol. I, pp. 26–33. Shum, H.Y., Szeliski, R., 1998. Construction and refinement of panoramic mosaics with global and local alignment, in: Proceedings of IEEE International Conference On Computer Vision, pp. 953–958. Szeliski, R., 1994. Image mosaicing for tele-reality applications, in: WACV94, pp. 44–53. Szeliski, R., 1996. Video mosaics for virtual environments. IEEE Comput. Graphics Appl., 22–30. Tuytelaars, T., van Gool, L., 1999. Content-based image retrieval based on local affinity invariant regions, in: Proceedings of Visual 99: Information and Information Systems, pp. 493–500. Zhang, Z., Deriche, R., Faugeras, O., Luong, Q., 1995. A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Artif. Intell. 78 (1–2), 87–119. Zhao, L., Yang, Y.H., 1999. Mosaic image method: A local and global method. Pattern Recognition 32(8), 1421–1433. Zoghlami, I., Faugeras, O., Deriche, R., 1997. Using geometric corners to build a 2D mosaic from a set of images, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Juan, PR, June, pp. 420–425.
Depositing User: Sara Taylor
Date Deposited: 22 Jun 2007
Last Modified: 29 Mar 2018 14:01
URI: http://eprints.hud.ac.uk/id/eprint/241

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