Synthetic Images


Fig.3 shows the resutls of CACE on a synthetic image, against other models. While the geodesic snake fails to detect the objects under cross-boundary initialisation, the GVF geodesic snake is less constrained, but nonetheless, still unable to reach some of the boundaries when it gets trapped by divergent vectors in homogeneous areas. CACE improves on these limitations and succeeds in detecting both objects in. Note that multi-scale settings are used for CPM in order to capture as much edges as possible. Further, Delaunay triangulation of Voronoi diagram is used for curve reconstruction from the scattered particles.
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Figure 4: Contour propagation for boundary detection. Top row: iterations of the CPM, 2nd row: geodesic snake, 3rd row: GVF geodesic snake, final row: CACE
Initial Model
CPM Image example_blurr_cpm_1010 Image example_blurr_cpm_2005 Image example_blurr_cpm_3015 Image example_blurr_cpm_3300 Image example_blurr_cpm_final
Geodesic snake Image example_blurr_geo_100 Image example_blurr_geo_300 Image example_blurr_geo_400 Image example_blurr_geo_600 Image example_blurr_geo_final
GVF geodesic snake Image example_blurr_gvf_020 Image example_blurr_gvf_040 Image example_blurr_gvf_060 Image example_blurr_gvf_080 Image example_blurr_gvf_final
CACE Image example_blurr_ccm_030 Image example_blurr_ccm_100 Image example_blurr_ccm_140 Image example_blurr_ccm_190 Image example_blurr_ccm_final

The improved robustness to initiliazation of CACE over other contour models can be further illustrated in the following figure. With an initial snake relatively interior to the objects, CACE still succeeds in detecting both objects, while the geodesic snake fails again due to the cross-boundary initial placement, and the GVF geodesic snake also getts trapped by the divergent vectors.
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Initial model Image example_blurr_init
Geodesic snake
GVF geodesic snake
CACE

 


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