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Globally Optimal Geodesic Active Contours
Appleton, Ben and Talbot, Hugues (2005-07-01) Globally Optimal Geodesic Active Contours. Journal of Mathematical Imaging and Vision, 23 1: 67-86.
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| Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials) |
| Name |
Description |
MIMEType |
Size |
Downloads |
GOGAC.pdf
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GOGAC.pdf |
application/pdf |
658.77KB |
425 |
| Author(s) |
Appleton, Ben
Talbot, Hugues
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| Title |
Globally Optimal Geodesic Active Contours
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| Journal name |
Journal of Mathematical Imaging and Vision
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| Publication date |
2005-07-01
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| Volume number |
23
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| Issue number |
1
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| ISSN |
0162-8828
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| Start page |
67
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| End page |
86
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| Total pages |
20
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| Place of publication |
Dordrecht
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| Publisher |
Springer
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| Language |
eng
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| Subject |
280208 Computer Vision
080104 Computer Vision
C1
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| Abstract |
An approach to optimal object segmentation in the geodesic active contour framework is presented with application to automated image segmentation. The new segmentation scheme seeks the geodesic active contour of globally minimal energy under the sole restriction that it contains a specified internal point p_int. This internal point selects the object of interest and may be used as the only input parameter to yield a highly automated segmentation scheme. The image to be segmented is represented as a Riemannian space S with an associated metric induced by the image. The metric is an isotropic and decreasing function of the local image gradient at each point in the image, encoding the local homogeneity of image features. Optimal segmentations are then the closed geodesics which partition the object from the background with minimal similarity across the partitioning. An efficient algorithm is presented for the computation of globally optimal segmentations and applied to cell microscopy, x-ray, magnetic resonance and cDNA microarray images.
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| Keyword(s) |
geodesic active contour
circular shortest path
optimal segmentation
Computer Science, Artificial Intelligence
Computer Science, Software Engineering
Mathematics, Applied
Gradient Vector Flow
Level Set Method
Models
Segmentation
Balloons
Fronts
Images
Snakes
Paths
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