Panoramic Image Stitching using SURF
1
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
This paper proposes a new method to process panoramic image stitching using SURF(Speeded Up Robust Features). Panoramic image stitching is considered a problem of the correspondence matching. In computer vision, it is difficult to find corresponding points in variable environment where a scale, rotation, view point and illumination are changed. However, SURF algorithm have been widely used to solve the problem of the correspondence matching because it is faster than SIFT(Scale Invariant Feature Transform). In this work, we also describe an efficient approach to decreasing computation time through the homography estimation using RANSAC(random sample consensus). RANSAC is a robust estimation procedure that uses a minimal set of randomly sampled correspondences to estimate image transformation parameters. Experimental results show that our method is robust to rotation, zoom, Gaussian noise and illumination change of the input images and computation time is greatly reduced.
Abstract
This paper proposes a new method to process panoramic image stitching using SURF(Speeded Up Robust Features). Panoramic image stitching is considered a problem of the correspondence matching. In computer vision, it is difficult to find corresponding points in variable environment where a scale, rotation, view point and illumination are changed. However, SURF algorithm have been widely used to solve the problem of the correspondence matching because it is faster than SIFT(Scale Invariant Feature Transform). In this work, we also describe an efficient approach to decreasing computation time through the homography estimation using RANSAC(random sample consensus). RANSAC is a robust estimation procedure that uses a minimal set of randomly sampled correspondences to estimate image transformation parameters. Experimental results show that our method is robust to rotation, zoom, Gaussian noise and illumination change of the input images and computation time is greatly reduced.
참고문헌 (13)
- Image alignment and stitching: A turorial, "R.Szeliski", Microsoft Research, Technicla Report, ~, pp.MSR-TR-2004-92 (2004)
- D.Lowe, "Distinctive image features from scale- invariant key-points", International Journal of Computer Vision, 60(2), pp.91~110 (2004)
- S.Chen, "Quick Time VR-An image-based approach to virtual environment navigation", In SIGGRAPH'95, 29, pp.29~38 (1995)
- H.Shum, "Construction of panoramic mosaics with global and local alignment", International Journal of Computer Vision, 36(2), pp.101~130 (2000)
- P.McLauchlan, "Image mosaicing using sequential bundle adjustment", Image and Vision Computing, 20(9), pp.751~759 (2002)
- C.Harris, "Geometry from visual motion", pp.263~284 (1992), MIT Press
- H.Bay, "Surf:Speeded up robust features", European Conference on Computer Vision, 3951, pp.404~417 (2006)
- T.Lindeberg, "Feature detection with automatic scale selection", International Journal of Computer Vision, 30(3), pp.79~116 (1998)
- M.Fischler, "Random sample consensus: A paradigm for model fitting with application to image analysis and autometed cartography", Communications of the ACM, 24, pp.381~395 (1981)
- R.Szeliski, "Image mosaicing for tele-reality applications", IEEE Workshop on Applications of Computer Vision, pp.44~53 (1994)
- R.Szeliski, "Creating full view panoramic image mosaics and texture-mapped models", Computer Graphics, pp.251~258 (1997)
- M.Brown, "Multi-image matching using multi-scale oriented patches", IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR'2005), pp.510~517 (2005)
- V.Rankov, "An algorithm for image stitching and blending", Proceedings of SPIE, 5701, pp.190~199 (2005)