Region merging methods consist of improving an initial segmentation by merging some pairs of neighboring regions. In a graph, merging two regions, separated by a set of vertices, is not straightforward. The perfect fusion graphs defined in [J. Cousty et al, “Fusion Graphs: Merging Properties and Watersheds”, JMIV 2008] verify all the basic properties required by region merging algorithms as use...

Region merging methods consist of improving an initial segmentation by merging some pairs of neighboring regions. In a graph, merging two regions is not straightforward. The perfect fusion graphs defined in [J. Cousty et al, “Fusion Graphs: Merging Properties and Watersheds”, JMIV 2008] verify all the basic properties required by region merging algorithms as used in image segmentation. Unfortun...

Label fusion is a multi-atlas segmentation approach that explicitly maintains and exploits the entire training dataset, rather than a parametric summary of it. Recent empirical evidence suggests that label fusion can achieve significantly better segmentation accuracy over classical parametric atlas methods that utilize a single coordinate frame. However, this performance gain typically comes at...

As the information carried in a high spatial resolution image is not represented by single pixels but by meaningful image objects, which include the association of multiple pixels and their mutual relations, the object based method has become one of the most commonly used strategies for the processing of high resolution imagery. This processing comprises two fundamental and critical steps towar...

The image segmentation method based on two-dimensional histogram segments the image according to the thresholds of the intensity of the target pixel and the average intensity of its neighborhood. This method is essentially a hard-decision method. Due to the uncertainties when labeling the pixels around the threshold, the hard-decision method can easily get the wrong segmentation result. Therefo...

Multi-atlas segmentation infers the target image segmentation by combining prior anatomical knowledge encoded in multiple atlases. It has been quite successfully applied to medical image segmentation in the recent years, resulting in highly accurate and robust segmentation for many anatomical structures. However, to guide the label fusion process, most existing multi-atlas segmentation methods ...

Since the dark channel prior (DCP)-based dehazing method is ineffective in sky area and will cause problem of too color distortion image, we propose a novel based on segmentation image fusion. We first segment according to characteristics non-sky then estimate atmospheric light transmission map DCP correct them, fuse original after contrast adaptive histogram equalization improve details inform...

This paper presents a strategy for combining the results of image classification and image segmentation. The visual features used for classification and segmentation may be different in general. Fusion is performed in a Maximum Likelihood framework using the Expectation Maximization algorithm. Preliminary results show that segmentation may effectively contribute to increase the quality of class...

normal 0 false false false en-us x-none fa background:  regarding the importance of right diagnosis in medical applications, various methods have been exploited for processing medical images solar. the method of segmentation is used to analyze anal to miscall structures in medical imaging. objective:  this study describes a new method for brain magnetic resonance image (mri) segmentation via a ...

In this work we propose a Bayesian framework for fully automated image fusion and their joint segmentation. More specifically, we consider the case where we have observed images of the same object through different image processes or through different spectral bands. The objective of this work is then to propose a coherent approach to combine these data sets and obtain a segmented image which c...