Localizing Region-Based Active Contours

By Shawn Lankton, Allen R. Tannenbaum. IEEE Transactions on Image Processing, 2008

1,216 citations recorded by the source corpus.

In this paper, we propose a natural framework that allows any region-based segmentation energy to be re-formulated in a local way. We consider local rather than global image statistics and evolve a contour based on local information. Localized contours are capable of segmenting objects with heterogeneous feature profiles that would be difficult to capture correctly using a standard global method. The presented technique is versatile enough to be used with any global region-based active contour energy and instill in it the benefits of localization. We describe this framework and demonstrate the localization of three well-known energies in order to illustrate how our framework can be applied to any energy. We then compare each localized energy to its global counterpart to show the improvements that can be achieved. Next, an in-depth study of the behaviors of these energies in response to the degree of localization is given. Finally, we show results on challenging images to illustrate the robust and accurate segmentations that are possible with this new class of active contour models.

Subfields: Biophysics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/f8b3142bf319f9d2c54fd5dca2cad3bda3a1cc32

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

In Computer Vision and Pattern Recognition

Citations in the corpus

Histology image analysis for carcinoma detection and grading (2012, methodology): “A detailed comparison of our LDF model and other local region-based models [91,93] can be seen in [70].”
Optic Disk and Cup Segmentation From Monocular Color Retinal Images for Glaucoma Assessment (2011, positive): “We propose a region-based active contour model which uses local image information at a support domain around each point of interest (POI) inspired by localized C-V models [43], [44] by using a richer form of local image information gathered over a multi-dimensional feature space.”
TouchCut: Fast image and video segmentation using single-touch interaction (2014, background): “This approach to color consistency, combined with a novel per-pixel adaptive weighting scheme, mitigates the tendency for contour expansion to skip the real boundary when color models of the foreground and background are indistinct.”
A Level Set Approach to Image Segmentation With Intensity Inhomogeneity (2016, neutral): “In this section, we compare our method with the CV model [1], the GCV model [21],1 the LRB model [9],2 the LIC model [11], and the LBF model [10],3 which”
An efficient local Chan-Vese model for image segmentation (2010, background): “In [26], a novel localization framework was proposed which allows the region-based energy to be localized in a fully variational way so that objects with heterogeneous statistics can be successfully segmented with the localized energies.”
A visual analytics approach to understanding cycling behaviour (2012, neutral): “Arcoumanis et al. [3], were one of the first who studied experimentally the breakup of Diesel droplets using the continuous air jet arrangement.”