Object segmentation using graph cuts based active contours

By Ning Xu, Ravi Bansal, Narendra Ahuja. 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., 2003

307 citations recorded by the source corpus.

In this paper we present a graph cuts based active contours (GCBAC) approach to object segmentation problems. Our method is a combination of active contours and the optimization tool of graph cuts and differs fundamentally from traditional active contours in that it uses graph cuts to iteratively deform the contour. Consequently, it has the following advantages. (1) It has the ability to jump over local minima and provide a more global result. (2) Graph cuts guarantee continuity and lead to smooth contours free of self-crossing and uneven spacing problems. Therefore, the internal force, which is commonly used in traditional energy functions to control the smoothness, is no longer needed, and hence the number of parameters is greatly reduced. (3). Our approach easily extends to the segmentation of three and higher dimensional objects. In addition, the algorithm is suitable for interactive correction and is shown to always converge. Experimental results and analyses are provided.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/5bda2034891476473545ef1cc56efd0c8005a4b6

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Citations in the corpus

Graph Cuts and Efficient N-D Image Segmentation (2006, methodology): “... a number of interesting directions: geometric cues (Boykov and Kolmogorov, 2003; Kolmogorov and Boykov, 2005) (geo-cuts), regional cues based on Gaussian mixture models (Blake et al., 2004) for improved interactivity (Rother et al., 2004) (grab-cuts), using superpixels (Li et al., 2004) (lazy sn”
Active contours with selective local or global segmentation: A new formulation and level set method (2010, methodology): “…lists available at ScienceDirect Image and Vision Computing journal homepage: www.elsevier .com/locate / imavis Active contours with selective local or global segmentation: A new formulation and level set method Kaihua Zhang a, Lei Zhang a,*, Huihui Song b, Wengang Zhou b a Dept. of Computing, The…”
Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review (2016, background): “though graph cut-based active contour models [188], [217] are presented, the two major implementations of deformable models for nucleus or cell segmentation are geodesic and parametric snakes, which are with implicit and explicit contour representations, respectively.”
QIN “Radiomics: The Process and the Challenges” (2012, methodology): “Many popular segmentation algorithms have been applied in medical imaging studies within the last 20 years; the most popular ones include region-growing methods [31,32], level set methods [33–38], graph cut methods [39–44], active contours (snake) algorithms [45–49] and semiautomatic segmentations s”