Title :
NBGVF: Normally Biased Gradient Vector Flow External Force for Active Contours
Author :
Lu, Shaopei ; Wang, Yuanquan
Author_Institution :
Tianjin Key Lab. of Intell. Comput. & Novel Software Technol., Tianjin Univ. of Technol., Tianjin, China
Abstract :
Gradient vector flow (GVF) has been one effective external force for active contours, but it is based on isotropic diffusion. The recently proposed NGVF external force just took into account the diffusion along normal direction of the level line, so it is sensitive to noise and could smear the weak boundaries. In this article, we present a novel one called normally biased gradient vector flow (NBGVF) which keeps the diffusion along the tangential direction of the level line and biases that along the normal direction. The biasing weight of the diffusion along the normal direction approaches zero at boundaries and is 1, even larger, in homogeneous regions. Consequently, the NBGVF can preserve weak edges and smooth out noise while maintaining other desirable properties of GVF and NGVF such as enlarged capture range, insensitivity to initialization and convergence to u-shape concavity. These properties are evaluated on synthetic and real images.
Keywords :
computer vision; gradient methods; image segmentation; NGVF external force; active contours; image segmentation; isotropic diffusion; normally biased gradient vector flow external force; synthetic images; u-shape concavity; Active contours; Calculus; Computer science; Convergence; Diffusion processes; Image edge detection; Image segmentation; Interpolation; Noise level; Solid modeling;
Conference_Titel :
Information Engineering and Computer Science, 2009. ICIECS 2009. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-4994-1
DOI :
10.1109/ICIECS.2009.5365081