Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation

By Nikos Paragios, Rachid Deriche. International Journal of Computer Vision, 2002

925 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/5a9ac67c92371e0b507f85a5a02306021c963049

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In Computational Mechanics

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Citations in the corpus

Minimization of Region-Scalable Fitting Energy for Image Segmentation (2008, methodology): “Existing active contour models can be categorized into two major classes: edge-based models [3], [10]–[12], [18], [19], [28], [30], and region-based models [4], [23], [25]–[27], [29].”
Edge and line oriented contour detection: State of the art (2011, background): “Moreover, region-based snakes can be more naturally integrated with texture models [213].”
An efficient local Chan-Vese model for image segmentation (2010, methodology): “Recently, Gabor filters have been efficiently incorporated into level set methods and CV model for the texture image segmentation [12] [28] [29].”
Object tracking: A survey (2006, methodology): “Particle .ltering [MacCormick and Blake 2000] M F v Gradient mag.”
A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI (2011, background): “A typical example is piecewise constant (PC) models proposed in [4], [16]–[18].”
Active contours driven by local Gaussian distribution fitting energy (2009, methodology): “Given all partitions are a priori equally possible [13], i.”
Dynamical statistical shape priors for level set-based tracking (2006, methodology): “The first applications of level set methods to image segmentation were pioneered in the early 1990s by Caselles et al. [4], [33], Malladi et al. [17], Kichenassamy et al. [13], and Paragios and Deriche [23].”
Advanced Level-Set-Based Cell Tracking in Time-Lapse Fluorescence Microscopy (2010, background): “where is the background, , , are the regions such that , is the boundary between regions, is the conditional probability that voxel with intensity belongs to the region [ 44 ].”
Active unsupervised texture segmentation on a diffusion based feature space (2003, methodology): “Following [18], the image segmentation can be found by maximizing the a posteriori partitioning probability p(P(Ω)|I) where P(Ω) = {Ω1, Ω2} is a partitioning of the image domain Ω.”
A nonparametric statistical method for image segmentation using information theory and curve evolution (2005, neutral): “[31].”
Active contours with selective local or global segmentation: A new formulation and level set method (2010, background): “According to the nature of constraints, the existing ACMs can be categorized into two types: edge-based models [1,3,4,6,10,12,18,20] and region-based models [5,7,8,11,14–17].”
Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation (2006, background): “…al., 1995; Caselles et al., 1995; Kichenassamy et al., 1995), intensity homogeneity (Chan and Vese, 2001; Tsai et al., 2001), texture information (Paragios and Deriche, 2002; Rousson et al., 2003; Heiler and Schnörr, 2003; Brox and Weickert , 2004) and motion information (Cremers and Soatto,…”
Contour-based object tracking with occlusion handling in video acquired using mobile cameras (2004, background): “In addition, the proposed image energy is very general, and the functionals used by Mansouri [14], Caselles et al. [6], and Paragios and Deriche [11] are special cases of it.”
Active contours driven by local image fitting energy (2010, neutral): “So we obtain the Euler–Lagrange equation 1⁄2I m1HeðfÞ m2ð1 HeðfÞÞ ðm1 m2ÞdeðfÞ 1⁄4 0 By the steepest gradient descent method [19], we get the following gradient descent flow: @f @t 1⁄4 ðI m1HeðfÞ m2ð1 HeðfÞÞÞðm1 m2ÞdeðfÞ 1⁄4 ðI IÞðm1 m2ÞdeðfÞ”
Implicit Active Contours Driven by Local Binary Fitting Energy (2007, methodology): “The existing active contour models can be categorized into two classes: edge-based models [1,4–6] and region-based models [2,9,10].”
Segmentation by transduction (2008, methodology): “It includes completely automated methods [8, 10, 22, 25], semi-supervised methods [4, 12, 11, 9, 13, 17, 23, 1], and model-driven approaches [18, 20, 26], among many others.”