Graph cuts based interactive segmentation has become very popular over the last decade. In standard graph cuts, the extraction of foreground object in a complex background often leads to many segmentation errors and the parameter λ in the energy function is hard to select. In this paper, we propose an iterated graph cuts algorithm, which starts from the sub-graph that comprises the user labeled...

In computer vision, segmentation is the process of partitioning digital image into multiple regions (sets of pixels), according to some homogeneity criterion. The problem of segmentation is a well-studied one in literature and there are a wide variety of approaches that are used. Graph cuts has emerged as a preferred method to solve a class of energy minimization problems such as Image Segmenta...

Abstract In this work we study statistical properties of graph-based clustering algorithms that rely on the optimization balanced graph cuts, main example being Cheeger cuts. We consider proximity graphs built from data sampled an underlying distribution supported a generic smooth compact manifold $${\mathcal {M}}$$ M </...

In this paper, present a graph cuts based geodesic active contours (GAC) approach to object segmentation problems. Our method is a combination of geodesic 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 abil...

Many computer vision problems are naturally formulated as random fields, specifically MRFs or CRFs. The introduction of graph cuts has enabled efficient and optimal inference in associative random fields, greatly advancing applications such as segmentation, stereo reconstruction and many others. However, while fast inference is now widespread, parameter learning in random fields has remained an...

We investigate the estimation of the perimeter of a set by a graph cut of a random geometric graph. For Ω ⊂ D = (0, 1), with d ≥ 2, we are given n random i.i.d. points on D whose membership in Ω is known. We consider the sample as a random geometric graph with connection distance ε > 0. We estimate the perimeter of Ω (relative to D) by the, appropriately rescaled, graph cut between the vertices...

The stereo matching aims to find corresponding entities between two (or more) images, i.e. entities that are projections of the same 3D object in the scene. Constraints used in stereo matching can be classified into two categories: local constraints, which rely only on a pixel and on some pixels in its surrounding, and global constraints, which must be verified by the whole pixels of a line or ...

Removal of non-brain tissues, particularly dura, is an important step in enabling accurate measurement of brain structures. Many popular methods rely on iterative surface deformation to fit the brain boundary and tend to leave residual dura. Similar to other approaches, the method proposed here uses intensity thresholding followed by removal of narrow connections to obtain a brain mask. However...