Abstract.A general framework for image segmentation is presented in this paper, based on the paradigm of water flow. The major water flow attributes like water pressure, surface tension and capillary force are defined in the context of force field generation and make the model adaptable to topological and geometrical changes. A flow-stopping image functional combining edge-and region-based forces is introduced to produce capability for both range and accuracy. The method is assessed qualitatively and quantitatively on synthetic and natural images. It is shown that the new approach can segment objects with complex shapes or weak-contrasted boundaries, and has good immunity to noise. The operator is also extended to 3-D, and is successfully applied to medical volume segmentation.

There is a rich literature of approaches to image feature extraction in computer vision. Many sophisticated approaches exist for low-and for high-level feature extraction but can be complex to implement with parameter choice guided by experimentation, but with performance analysis and optimization impeded by speed of computation. We have developed new feature extraction techniques on notional use of physical paradigms, with parametrization aimed to be more familiar to a scientifically trained user, aiming to make best use of computational resource. This paper is the first unified description of these new approaches, outlining the basis and results that can be achieved. We describe how gravitational force can be used for low-level analysis, while analogies of water flow and heat can be deployed to achieve high-level smooth shape detection, by determining features and shapes in a selection of images, comparing results with those by stock approaches from the literature. We also aim to show that the implementation is consistent with the original motivations for these techniques and so contend that the exploration of physical paradigms offers a promising new avenue for new approaches to feature extraction in computer vision. FEATURE EXTRACTION AND IMAGE PROCESSINGThis paper presents the first unified description of some new approaches to feature extraction in image processing and computer vision, derived by using physical analogies. The approaches aim to achieve feature extraction with insight for parameter selection, fast implementation by virtue of simplicity or by use of established techniques to improve computational speed, and with performance that is at minimum comparable with that achieved by state-or-art techniques. For this, we first explore what we understand to be meant by the term feature extraction. Feature and shape extractionThere is now a rich literature of techniques that can detect lowlevel features, such as edges and corners, and high-level shapes [1]. Low-level operators are generally those which operate on an image as a whole; high-level operators are those which process images so as to determine shapes that lie therein. Both processes are used within computer vision, to render explicit information that is implicit within the original image-as such providing image understanding. The state-of-art operators for low-level feature extraction include anisotropic diffusion for image smoothing, to preserve features and to reduce the effects of noise; the scale invariant feature transform (SIFT) aims to find corner features that persist over image scales, by a sophisticated operation based on the difference of Gaussian operator. By way of example, we show the result of anisotropic diffusion [2] applied to an image of an eye, in comparison with the result by Gaussian filtering, a standard operator. The anisotropic diffusion process in Fig. 1c achieves a more pleasing result than that of Fig. 1b, preserving features better while smoothing noise to greater effect. The parameter choice for the Gaussian oper...

There is a rich literature of approaches to image feature extraction in computer vision. Many sophisticated approaches exist for low-and high-level feature extraction but can be complex to implement with parameter choice guided by experimentation, but impeded by speed of computation. We have developed new ways to extract features based on notional use of physical paradigms, with parameterisation that is more familiar to a scientifically-trained user, aiming to make best use of computational resource. We describe how analogies based on gravitational force can be used for low-level analysis, whilst analogies of water flow and heat can be deployed to achieve high-level smooth shape detection. These new approaches to arbitrary shape extraction are compared with standard state-of-art approaches by curve evolution. There is no comparator operator to our use of gravitational force. We also aim to show that the implementation is consistent with the original motivations for these techniques and so contend that the exploration of physical paradigms offers a promising new avenue for new approaches to feature extraction in computer vision.

Previous vessel segmentation methods mainly concentrate on the general structure, and often ignore the accuracy, smoothness and continuity of vessel boundaries. A water flow based method is proposed to solve the problem. It embodies the fluidity of water and hence can handle the complex topological changes of vessels. A snake-like force functional combining edge-based and region-based forces produces capability for both accuracy and range. Properties analogous to surface tension and adhesion are also applied so that the smoothness of the evolving contour and the ability to flow into narrow branches can be controlled. The technique has been assessed on synthetic and real images, and shows excellent detection performance and ability to handle noise.

Abstract.A new general framework for shape extraction is presented, based on the paradigm of water flow. The mechanism embodies the fluidity of water and hence can detect complex shapes. A new snake-like force functional combining edge-based and region-based forces produces capability for both range and accuracy. Properties analogous to surface tension and adhesion are also applied so that the smoothness of the evolving contour and the ability to flow into narrow branches can be controlled. The method has been assessed on synthetic and natural images, and shows encouraging detection performance and ability to handle noise, consistent with properties included in its formulation.