It has been recently shown that a large class of balanced graph cuts allows for an exact relaxation into a nonlinear eigenproblem. We review briefly some of these results and propose a family of algorithms to compute nonlinear eigenvectors which encompasses previous work as special cases. We provide a detailed analysis of the properties and the convergence behavior of these algorithms and then ...

A cactus is a simple data structure that represents all minimum cuts of a weighted, undi-rected graph in linear space. We describe the rst algorithm that can build a cactus from the asymptotically fastest deterministic algorithm that nds all minimum cuts in a weighted graph | the Hao-Orlin minimum cut algorithm. This improves the time to construct the cactus in graphs with n vertices and m edge...

Given an edge-capacitated undirected graph G = (V ,E,C) with edge capacity c :E → R+, n = |V |, an s − t edge cut C of G is a minimal subset of edges whose removal from G will separate s from t in the resulting graph, and the capacity sum of the edges in C is the cut value of C. A minimum s − t edge cut is an s − t edge cut with the minimum cut value among all s − t edge cuts. A theorem given b...

The Compute Unified Device Architecture (CUDA) has enabled graphics processors to be explicitly programmed as general-purpose shared-memory multi-core processors with a high level of parallelism. In this paper, we present our preliminary results of implementing the Graph Cuts algorithm on CUDA. Our primary focus is on implementing Graph Cuts on grid graphs, which are extensively used in imaging...

1D gridded design is one of the most promising solutions that can enable the scaling to 10nm technology node and beyond. Line-end cuts are needed to fabricate 1D layouts, where two techniques are available to resolve the conflicts between cuts: cut redistribution and cut mask assignment. In this paper, we consider incorporating the two techniques to enable the manufacturing of cut patterns in 1...

Learning short binary codes is challenged by the inherent discrete nature of the problem. The graph cuts algorithm is a well-studied discrete label assignment solution in computer vision, but has not yet been applied to solve the binary coding problems. This is partially because it was unclear how to use it to learn the encoding (hashing) functions for out-of-sample generalization. In this pape...

This is a short guide to explain how to quickly minimize a large class of segmentation models called active contours. We believe that the proposed theory and algorithm, introduced in a series of papers [6, 4, 10, 11], produce so far one of the most efficient minimization methods for the active contour segmentation problem. For example, the well-know cameraman picture, which size is 256× 256, is...

Recent stereo algorithms have achieved impressive results by modelling the disparity image as a Markov Random Field (MRF). An important component of an MRF-based approach is the inference algorithm used to find the most likely setting of each node in the MRF. Algorithms have been proposed which use Graph Cuts or Belief Propagation for inference. These stereo algorithms differ in both the infere...

In this paper we present a graph cuts based active contours (GCBAC) approach to object segmentation. GCBAC approach is a combination of the iterative deformation idea of active contours and the optimization tool of graph cuts. It differs from traditional active contours in that it uses graph cuts to iteratively deform the contour and its cost function is defined as the summation of edge weights...

Recently, it is shown that graph cuts algorithms can be used to solve some variational image restoration problems, especially connected with noise removal and segmentation. For very large size images, the usage for memory and computation increases dramatically. We propose a domain decomposition method with graph cuts algorithms. We show that the new approach costs effective both for memory and ...