Segmentation of GIS Object for Mobile Mapping System
Abstract
Segmentation and tracking of objects in a 2D image
sequence is an important and challenging field of wide usages
including change detection, object-based video post production, content-based indexing and retrieval, surveillance. This paper proposes a novel hybrid method to segment the GIS object in DMI sequences using pyramid decomposition. The proposed method involves image enhancement strategy to hypothesis for foreground background identification and applying watershed transformation to achieve foreground background segmentation more effectively. The proposed method also uses image enhancement and denoising strategy to enhance the frameset to properly track the Object of interest in the given spatiotemporal data set using Gaussian filter. Pyramid decomposition based strategy is used to track the GIS object in sequential frame which involves DMI. Pixels having the highest gradient magnitude intensities (GMIs) correspond to watershed lines which represent the region boundaries are tracked in subsequent
frames. The object template is updated with the frame set to increase the tracking performance.
Keywords
Full Text:
PDFReferences
Peng Li, Cheng Wang, Hanyun Wang, Shengyong Hao, “
Spatiotemporal egmentation of GIS Object for Mobile Mapping System”
IEEE , Conference 2011.
J. G. Allen, R. Y. D. Xu, and J. S. Jin, “Object Tracking Using CamShift
Algorithm and Multiple Quantized Feature Spaces,” Pan-Sydney Area
Workshop on Visual Information Processin, 2003.
N. Apostoloff, and A. Fitzgibbon, “Automatic video segmentation using
spatiotemporal T-junctions,” BMVC, 2006.
S. W. Babacan, and T. N. Pappas, “Spatiotemporal algorithm for joint
video segmentation and foreground detection,” EUSIPCO, 2006.
E. Borenstein, and J. Malik, “Shape Guided Object Segmentation,”
CVPR, 2006.
G. Bradski, and A. Kaehler, Learning OpenCV. O’Reilly Media Inc.,
Sebastopol, pp.194-221, 2008.
W. Brendel, and S. Todorovic, “Video Object Segmentation by
TrackingRegions” ICCV, 2009..
P. L. Correia, , and F. Pereira, “Classification of Video Segmentation
Application Scenarios,” IEEE Trans. on Circuits and Systems for Video
Technology, 14(5), pp. 735-741, 2004.
R. Ahmed, G. C. Karmakar, and L. S. Dooley, “Probabilistic Spatio-
Temporal Video Object Segmentation Incorporating
Shape Information,” ICASSP, 2005.
S. beucher, “The watershed transformation applied to image
segmentation,” 10th Pfefferkorn Conf. on Signal and Image Processing
in Microscopy and Microanalysis, 16-19 sept. 1991, Cambridge, UK,
Scanning Microscopy International, suppl. 6. pp. 299-314, 1992.
J. Huang, S. Ravikumar, M. Mitra, W.J. Zhu, and R. Zabih, “spatial
Color Indexing and Applications,” International Journal of Computer
Vision, 35(3), pp. 245–268, 1999.
Y. C. Huang, Q.S. Liu, and, D. Metaxas, “Video Object Segmentation
by Hypergraph Cut,” CVPR, 2009
D. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,”
International Journal of Computer Vision, 60(2), pp. 91–110, 2004.
S. J. Sun, D. R. Haynor, and Y. M. Kim, “Semiautomatic Video Object
Segmentation Using VSnakes,” IEEE Trans. on Circuits and Systems for
Video Technology. 13(1), pp. 75-82, 2003.
N. Vaswani, Y. Rathi, A. Yezzi, and A. Tannenbaum, “Deform PF-MT:
Particle Filter with Mode Tracker for Tracking Non-Affine Contour
Deformations,” IEEE Trans. Image Processing, 19(4), pp. 841-
,2009.
C. Wang, T. Hassan, N. El-Sheimy, and M. Lavigne, “Automatic Road
Vector Extraction for Mobile Mapping Systems,” XXI Congress,
ISPRS,2008.
T. T. Zin, and H. Hama, “A Method Using Morphology and Histogram
for Object-based Retrieval in Image and Video Databases,” International
Journal of Computer Science and Network Security, 7(9), pp. 123-
,2007.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.