Abstract. The watershed transform is the basic morphological tool for image segmentation. Watershed lines, also called divide lines, are a topographical concept: a drop of water falling on a topographical surface follows a steepest descent line until it stops when reaching a regional minimum. Falling on a divide line, the same drop of water may glide towards one or the other of both adjacent catchment basins. For segmenting an image, one takes as topographic surface the modulus of its gradient: the associated watershed lines will follow the contour lines in the initial image. The trajectory of a drop of water is disturbed if the relief is not smooth: it is undefined for instance on plateaus. On the other hand, each regional minimum of the gradient image is the attraction point of a catchment basin. As gradient images generally present many minima, the result is a strong oversegmentation. For these reasons a more robust scheme is used for the construction of the watershed based on flooding: a set of sources are defined, pouring water in such a way that the altitude of the water increases with constant speed. As the flooding proceeds, the boundaries of the lakes propagate in the direction of the steepest descent line of the gradient. The set of points where lakes created by two distinct sources meet are the contours. As the sources are far less numerous than the minima, there is no more oversegmentation. And on the plateaus the flooding also is well defined and propagates from the boundary towards the inside of the plateau. Used in conjunction with markers, the watershed is a powerful, fast and robust segmentation method. Powerful: it has been used with success in a variety of applications. Robust: it is insensitive to the precise placement or shape of the markers. Fast: efficient algorithms are able to mimic the progression of the flood. In some cases however the resulting segmentation will be poor: the contours always belong to the watershed lines of the gradient and these lines are poorly defined when the initial image is blurred or extremely noisy. In such cases, an additional regularization has to take place. Denoising and filtering the image before constructing the gradient is a widely used method. It is however not always sufficient. In some cases, one desires smoothing the contour, despite the chaotic fluctuations of the watershed lines. For this two options are possible. The first consists in using a viscous fluid for the flooding: a viscous fluid will not be able to follow all irregularities of the relief and produce lakes with smooth boundaries. Simulating a viscous fluid is however computationally intensive. For this reason we propose an alternative solution, in which the topographic surface is modified in such a way that flooding it with a non viscous fluid will produce the same lakes as flooding the original relief with a viscous fluid. On this new relief, the standard watershed algorithm can be used, which has been optimized for various architectures. Two types of viscous fluids will be presented, y...
Viscous morphological operators have shown very good performance in regularizing various image analysis tasks such as detection of intensity-varying boundaries and segmentation. This paper presents a novel formulation of viscous morphological operators as solutions of nonlinear partial differential equations (PDEs) of the hyperbolic type with level-varying speed. Efficient numerical algorithms are also developed to solve these PDEs and generate the viscous operations. It also generalizes the viscous operators by studying the class of intensity level-varying operators, of which special cases are intensity adaptive connected operators such as volume openings and viscous reconstruction filters. We present both theoretical aspects and applications of the above ideas.
In this paper we briefly overview emerging trends in 'Adaptive Morphology', i.e. work related to the theory and/or applications of image analysis filters, systems, or algorithms based on mathematical morphology, that are adaptive w.r.t. to space or intensity or use any other adaptive scheme. We present a new classification of work in this area structured along several major theoretical perspectives. We then sample specific approaches that develop spatially-variant structuring elements or intensity level-adaptive operators, modeled and implemented either via conventional nonlinear digital filtering or via geometric PDEs. Finally, we discuss some applications.
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