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Active contours are widely used in image segmentation. To cope with missing or misleading features in image frames taken in contexts such as spatial and surveillance, researchers have commence various ways to model the preceding of shapes and use the prior to constrict active contours. However, the shape prior is frequently learnt from a large set of annotated data, which is not constantly accessible in practice. In addition, it is often doubted that the existing shapes in the training set will be sufficient to model the new instance in the testing image. In this paper we propose to use the diminutive sequence of image frames to learn the missing contour of the input images. The central median minimization is a simple and effective way to impose the proposed constraint on existing active contour models. Moreover, we extend a fast algorithm to solve the projected model by using the hastened proximal method. The Experiments done using image frames acquired from surveillance, which demonstrated that the proposed method can consistently improve the performance of active contour models and increase the robustness against image defects such as missing boundaries.
D. Baswaraj, Dr. A. Govardhan, Dr. P. Premchand. 2013. "Projecting Active Contours with Diminutive Sequence Optimality". Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 13 (GJCST Volume 13 Issue F8).
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
v1.2
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Total Score: 153
Country: India
Subject: Global Journal of Computer Science and Technology
Authors: D. Baswaraj, Dr. A. Govardhan, Dr. P. Premchand (PhD/Dr. count: 2)
View Count (all-time): 408
Total Views (Real + Logic): 2878
Total Downloads (simulated): 235
Publish Date: 2013 01, Tue
Monthly Totals (Real + Logic):
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