Yongchao Xu

Meaningful disjoint level lines selection

By Yongchao Xu, Edwin Carlinet, Thierry Géraud, Laurent Najman

2014-05-26

In Proceedings of the 21st international conference on image processing (ICIP)

Abstract

Many methods based on the morphological notion of shapes (i.e., connected components of level sets) have been proved to be very efficient in shape recognition and shape analysis. The inclusion relationship of the level lines (boundaries of level sets) forms the tree of shapes, a tree-based image representation with a high potential. Numerous applications using this tree representation have been proposed. In this article, we propose an efficient algorithm that extracts a set of disjoint level lines in the image. These selected level lines yields a simplified image with clean contours, which also provides an intuitive idea about the main structure of the tree of shapes. Besides, we obtain a saliency map without transition problems around the contours by weighting level lines with their significance. Experimental results demonstrate the efficiency and usefulness of our method.

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Tree-based shape spaces: Definition and applications in image processing and computer vision

By Yongchao Xu

2013-12-12

In

Abstract

The classical framework of connected filters relies on the removal of some connected components of a graph. To apply those filters, it is often useful to transform an image into a component tree, and to prune the tree to simplify the original image. Those trees have some remarkable properties for computer vision. A first illustration of their usefulness is the proposition of a local feature detector, truly invariant to change of contrast. which allows us to obtain the state-of-the-art results in image registration and in multi-view 3D reconstruction. Going further in the use of those trees, we propose to expand the classical framework of connected filters. For this, we introduce the notion of tree-based shape spaces: instead of filtering the connected components of the graph corresponding to the image, we propose to filter the connected components of the graph given by the component tree of the image. This general framework, which we call shape-based morphology can be used for object detection and segmentation, hierarchical segmentation, and image filtering. Many applications and illustrations show the usefulness of the proposed framework.

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Salient level lines selection using the Mumford-Shah functional

By Yongchao Xu, Thierry Géraud, Laurent Najman

2013-05-27

In Proceedings of the 20th international conference on image processing (ICIP)

Abstract

Many methods relying on the morphological notion of shapes, (i.e., connected components of level sets) have been proved to be very useful for pattern analysis and recognition. Selecting meaningful level lines (boundaries of level sets) yields to simplify images while preserving salient structures. Many image simplification and/or segmentation methods are driven by the optimization of an energy functional, for instance the Mumford-Shah functional. In this article, we propose an efficient shape-based morphological filtering that very quickly compute to a locally (subordinated to the tree of shapes) optimal solution of the piecewise-constant Mumford-Shah functional. Experimental results demonstrate the efficiency, usefulness, and robustness of our method, when applied to image simplification, pre-segmentation, and detection of affine regions with viewpoint changes.

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Two applications of shape-based morphology: Blood vessels segmentation and a generalization of constrained connectivity

By Yongchao Xu, Thierry Géraud, Laurent Najman

2013-03-14

In Mathematical morphology and its application to signal and image processing – proceedings of the 11th international symposium on mathematical morphology (ISMM)

Abstract

Connected filtering is a popular strategy that relies on tree-based image representations: for example, one can compute an attribute on each node of the tree and keep only the nodes for which the attribute is sufficiently strong. This operation can be seen as a thresholding of the tree, seen as a graph whose nodes are weighted by the attribute. Rather than being satisfied with a mere thresholding, we propose to expand on this idea, and to apply connected filters on this latest graph. Consequently, the filtering is done not in the space of the image, but on the space of shapes built from the image. Such a processing, that we called shape-based morphology, is a generalization of the existing tree-based connected operators. In this paper, two different applications are studied: in the first one, we apply our framework to blood vessels segmentation in retinal images. In the second one, we propose an extension of constrained connectivity. In both cases, quantitative evaluations demonstrate that shape-based filtering, a mere filtering step that we compare to more evolved processings, achieves state-of-the-art results.

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Morphological filtering in shape spaces: Applications using tree-based image representations

By Yongchao Xu, Thierry Géraud, Laurent Najman

2012-06-16

In Proceedings of the 21st international conference on pattern recognition (ICPR)

Abstract

Connected operators are filtering tools that act by merging elementary regions of an image. A popular strategy is based on tree-based image representations: for example, one can compute a shape-based attribute on each node of the tree and keep only the nodes for which the attribute is sufficiently strong. This operation can be seen as a thresholding of the tree, seen as a graph whose nodes are weighted by the attribute. Rather than being satisfied with a mere thresholding, we propose to expand on this idea, and to apply connected filters on this latest graph. Consequently, the filtering is done not in the space of the image, but on the space of shapes build from the image. Such a processing is a generalization of the existing tree-based connected operators. Indeed, the framework includes classical existing connected operators by attributes. It also allows us to propose a class of novel connected operators from the leveling family, based on shape attributes. Finally, we also propose a novel class of self-dual connected operators that we call morphological shapings.

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Context-based energy estimator: Application to object segmentation on the tree of shapes

By Yongchao Xu, Thierry Géraud, Laurent Najman

2012-04-17

In Proceedings of the 19th international conference on image processing (ICIP)

Abstract

Image segmentation can be defined as the detection of closed contours surrounding objects of interest. Given a family of closed curves obtained by some means, a difficulty is to extract the relevant ones. A classical approach is to define an energy minimization framework, where interesting contours correspond to local minima of this energy. Active contours, graph cuts or minimum ratio cuts are instances of such approaches. In this article, we propose a novel, efficient ratio-cut estimator, which is both context-based and can be interpreted as an active contour. As a first example of the effectiveness of our formulation, we consider the tree of shapes, which provides a family of level lines organized in a tree hierarchy through an inclusion relationship. Thanks to the tree structure, the estimator can be computed incrementally in an efficient fashion. Experimental results on synthetic and real images demonstrate the robustness and usefulness of our method.

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