Narrow Results

Graph cuts is an image segmentation method by which the region and boundary information of objects can be revolved comprehensively. Because of the complex spatial characteristics of high-dimensional images, time complexity and segmentation accuracy of graph cuts methods for high-dimensional images need to be improved. This paper proposes a new three-dimensional multilevel banded graph cuts model to increase its accuracy and reduce its complexity. Firstly, three-dimensional image is viewed as a high-dimensional space to construct three-dimensional network graphs. A pyramid image sequence is created by Gaussian pyramid downsampling procedure. Then, a new energy function is built according to the spatial characteristics of the three-dimensional image, in which the adjacent points are expressed by using a 26-connected system. At last, the banded graph is constructed on a narrow band around the object/background. The graph cuts method is performed on the banded graph layer by layer to obtain the object region sequentially. In order to verify the proposed method, we have performed an experiment on a set of three-dimensional colon CT images, and compared the results with local region active contour and Chan–Vese model. The experimental results demonstrate that the proposed method can segment colon tissues from three-dimensional abdominal CT images accurately. The segmentation accuracy can be increased to 95.1% and the time complexity is reduced by about 30% of the other two methods.

Graph cuts (GC) have become one of the most important methodologies in image segmentation. Recently, some researches tend to study GC and kernel mapping of the image pixels. However, most of the methods of existing kernel GC (KGC) image segmentation not only suffers from different types of noise, but also suffers from the settings of the regularizing parameter, which is used to balance the edge and region terms in the existing KGC. This paper is to investigate the image segmentation via principal component analysis and KGC ensemble. The principal components are selected by the estimated strength of introduced noise, which is calculated by the maximum statistics of variation coefficient. Besides, the regularization term of KGC is difficult to choose the appropriate scaling factor value for the regularization term, and it is always obtained by experience. Here, we aim to present KGC ensemble strategy to combine the segmenting results under different settings of scaling parameter. Furthermore, to achieve higher segmenting accuracy, the KGC is implemented in the projected lower dimensional subspace by selected principal components. Experiments on synthetic image, nature image, medical image and real SAR images demonstrate the advantages of the proposed algorithm over the existing KGC, two variants of fuzzy c-means not only in region consistence but also in the boundary localization.

Depth images, in particular depth maps estimated from stereo vision, may have a substantial amount of outliers and result in inaccurate 3D modelling and reconstruction. To address this challenging issue, in this paper, a graph-cut based multiple depth maps integration approach is proposed to obtain smooth and watertight surfaces. First, confidence maps for the depth images are estimated to suppress noise, based on which reliable patches covering the object surface are determined. These patches are then exploited to estimate the path weight for 3D geodesic distance computation, where an adaptive regional term is introduced to deal with the "shorter-cuts" problem caused by the effect of the minimal surface bias. Finally, the adaptive regional term and the boundary term constructed using patches are combined in the graph-cut framework for more accurate and smoother 3D modelling. We demonstrate the superior performance of our algorithm on the well-known Middlebury multi-view database and additionally on real-world multiple depth images captured by Kinect. The experimental results have shown that our method is able to preserve the object protrusions and details while maintaining surface smoothness.