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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: 22 Aug 2015 19:59
URI: http://eprints.hud.ac.uk/id/eprint/241

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