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34
Interactive Graph Cuts for Optimal Boundary & Region Segmentation of Objects in ND Images
, 2001
"... In this paper we describe a new technique for general purpose interactive segmentation of Ndimensional images. The user marks certain pixels as “object” or “background” to provide hard constraints for segmentation. Additional soft constraints incorporate both boundary and region information. Graph ..."
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Cited by 1013 (20 self)
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In this paper we describe a new technique for general purpose interactive segmentation of Ndimensional images. The user marks certain pixels as “object” or “background” to provide hard constraints for segmentation. Additional soft constraints incorporate both boundary and region information. Graph cuts are used to find the globally optimal segmentation of the Ndimensional image. The obtained solution gives the best balance of boundary and region properties among all segmentations satisfying the constraints. The topology of our segmentation is unrestricted and both “object” and “background” segments may consist of several isolatedparts. Some experimental results are presented in the context ofphotohideo editing and medical image segmentation. We also demonstrate an interesting Gestalt example. A fast implementation of our segmentation method is possible via a new mar$ow algorithm in [2].
Graph Cuts and Efficient ND Image Segmentation
, 2006
"... Combinatorial graph cut algorithms have been successfully applied to a wide range of problems in vision and graphics. This paper focusses on possibly the simplest application of graphcuts: segmentation of objects in image data. Despite its simplicity, this application epitomizes the best features ..."
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Cited by 304 (7 self)
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Combinatorial graph cut algorithms have been successfully applied to a wide range of problems in vision and graphics. This paper focusses on possibly the simplest application of graphcuts: segmentation of objects in image data. Despite its simplicity, this application epitomizes the best features of combinatorial graph cuts methods in vision: global optima, practical efficiency, numerical robustness, ability to fuse a wide range of visual cues and constraints, unrestricted topological properties of segments, and applicability to ND problems. Graph cuts based approaches to object extraction have also been shown to have interesting connections with earlier segmentation methods such as snakes, geodesic active contours, and levelsets. The segmentation energies optimized by graph cuts combine boundary regularization with regionbased properties in the same fashion as MumfordShah style functionals. We present motivation and detailed technical description of the basic combinatorial optimization framework for image segmentation via s/t graph cuts. After the general concept of using binary graph cut algorithms for object segmentation was first proposed and tested in Boykov and Jolly (2001), this idea was widely studied in computer vision and graphics communities. We provide links to a large number of known extensions based on iterative parameter reestimation and learning, multiscale or hierarchical approaches, narrow bands, and other techniques for demanding photo, video, and medical applications.
Learning a classification model for segmentation
 In Proc. 9th Int. Conf. Computer Vision
, 2003
"... We propose a twoclass classification model for grouping. Human segmented natural images are used as positive examples. Negative examples of grouping are constructed by randomly matching human segmentations and images. In a preprocessing stage an image is oversegmented into superpixels. We define a ..."
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Cited by 279 (2 self)
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We propose a twoclass classification model for grouping. Human segmented natural images are used as positive examples. Negative examples of grouping are constructed by randomly matching human segmentations and images. In a preprocessing stage an image is oversegmented into superpixels. We define a variety of features derived from the classical Gestalt cues, including contour, texture, brightness and good continuation. Informationtheoretic analysis is applied to evaluate the power of these grouping cues. We train a linear classifier to combine these features. To demonstrate the power of the classification model, a simple algorithm is used to randomly search for good segmentations. Results are shown on a wide range of images. 1.
Modelling and interpretation of architecture from several images
"... The modelling of 3dimensional (3D) environments has become a requirement for many applications in engineering design, virtual reality, visualisation and entertainment. However the scale and complexity demanded from such models has risen to the point where the acquisition of 3D models can require a ..."
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Cited by 116 (6 self)
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The modelling of 3dimensional (3D) environments has become a requirement for many applications in engineering design, virtual reality, visualisation and entertainment. However the scale and complexity demanded from such models has risen to the point where the acquisition of 3D models can require a vast amount of specialist time and equipment. Because of this much research has been undertaken in the computer vision community into automating all or part of the process of acquiring a 3D model from a sequence of images. This thesis focuses specifically on the automatic acquisition of architectural models from short image sequences. An architectural model is defined as a set of planes corresponding to walls which contain a variety of labelled primitives such as doors and windows. As well as a label defining its type, each primitive contains parameters defining its shape and texture. The key advantage of this representation is that the model defines not only geometry and texture, but also an interpretation of the scene. This is crucial as it enables reasoning about the scene; for instance, structure and texture can be inferred in areas of the model which are unseen in any
Interactive Graph Cut Based Segmentation With Shape Priors
 IN CVPR, PAGES I: 755–762
, 2005
"... ... alternative to pure automatic segmentation in many applications. While automatic segmentation can be very challenging, a small amount of user input can often resolve ambiguous decisions on the part of the algorithm. In this work, we devise a graph cut algorithm for interactive segmentation which ..."
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Cited by 116 (0 self)
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... alternative to pure automatic segmentation in many applications. While automatic segmentation can be very challenging, a small amount of user input can often resolve ambiguous decisions on the part of the algorithm. In this work, we devise a graph cut algorithm for interactive segmentation which incorporates shape priors. While traditional graph cut approaches to interactive segmentation are often quite successful, they may fail in cases where there are diffuse edges, or multiple similar objects in close proximity to one another. Incorporation of shape priors within this framework mitigates these problems. Positive results on both medical and natural images are demonstrated.
Globally optimal regions and boundaries as minimum ratio weight cycles
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2001
"... Abstract. We describe a new form of energy functional for the modelling and identification of regions in images. The energy is defined on the space of boundaries in the image domain, and can incorporate very general combinations of modelling information both from the boundary (intensity gradients,.. ..."
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Cited by 84 (2 self)
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Abstract. We describe a new form of energy functional for the modelling and identification of regions in images. The energy is defined on the space of boundaries in the image domain, and can incorporate very general combinations of modelling information both from the boundary (intensity gradients,...), and from the interior of the region (texture, homogeneity,. We describe two polynomialtime digraph algorithms for finding the global minima of this energy. One of the algorithms is completely general, minimizing the functional for any choice of modelling information. It runs in a few seconds on a 256 × 256 image. The other algorithm applies to a subclass of functionals, but has the advantage of being extremely parallelizable. Neither algorithm requires initialization. 1.
Interactive organ segmentation using graph cuts
 In Medical Image Computing and ComputerAssisted Intervention
, 2000
"... Abstract. An Ndimensional image is divided into “object ” and “background” segments using a graph cut approach. A graph is formed by connecting all pairs of neighboring image pixels (voxels) by weighted edges. Certain pixels (voxels) have to be a priori identified as object or background seeds prov ..."
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Cited by 82 (1 self)
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Abstract. An Ndimensional image is divided into “object ” and “background” segments using a graph cut approach. A graph is formed by connecting all pairs of neighboring image pixels (voxels) by weighted edges. Certain pixels (voxels) have to be a priori identified as object or background seeds providing necessary clues about the image content. Our objective is to find the cheapest way to cut the edges in the graph so that the object seeds are completely separated from the background seeds. If the edge cost is a decreasing function of the local intensity gradient then the minimum cost cut should produce an object/background segmentation with compact boundaries along the high intensity gradient values in the image. An efficient, globally optimal solution is possible via standard mincut/maxflow algorithms for graphs with two terminals. We applied this technique to interactively segment organs in various 2D and 3D medical images. 1
Beamlets and Multiscale Image Analysis
 in Multiscale and Multiresolution Methods
, 2001
"... We describe a framework for multiscale image analysis in which line segments play a role analogous to the role played by points in wavelet analysis. ..."
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Cited by 66 (16 self)
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We describe a framework for multiscale image analysis in which line segments play a role analogous to the role played by points in wavelet analysis.
Grouping with Bias
 In Advances in Neural Information Processing Systems
, 2001
"... With the optimization of pattern discrimination as a goal, graph partitioning approaches often lack the capability to integrate prior knowledge to guide grouping. In this paper, we consider priors from unitary generative models, partially labeled data and spatial attention. These priors are modelled ..."
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Cited by 60 (5 self)
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With the optimization of pattern discrimination as a goal, graph partitioning approaches often lack the capability to integrate prior knowledge to guide grouping. In this paper, we consider priors from unitary generative models, partially labeled data and spatial attention. These priors are modelled as constraints in the solution space. By imposing uniformity condition on the constraints, we restrict the feasible space to one of smooth solutions. A subspace projection method is developed to solve this constrained eigenproblem.
Automatic 3d modelling of architecture
 British Machine Vision Conference
, 2000
"... This paper describes a system which automatically derives 3D models of architectural scenes from multiple images. This system differs from previous structure from motion algorithms in that it explicitly makes use of strong geometric constraints such as perpendicularity and verticality which are like ..."
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Cited by 30 (3 self)
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This paper describes a system which automatically derives 3D models of architectural scenes from multiple images. This system differs from previous structure from motion algorithms in that it explicitly makes use of strong geometric constraints such as perpendicularity and verticality which are likely to be found in architecture. Structure is compactly represented as a piecewise planar model which is initialised automatically by segmenting a featurebased reconstruction. An efficient technique for evaluation of model likelihood is also presented, which allows a rapid search through a large number of 3D models. 1