Magnetostatic Field for the Active Contour Model: A Study in Convergence

Xie, Xianghua; Mirmehdi, Majid

doi:10.5244/c.20.14

Cited by 16 publications

(13 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [6,7], magnetostatic theory was combined with an active contour model. For a moving charge inside a magnetic field, there are magnetic forces introduced by the field acting on it.…”

Section: Using Force Analogies In Image Processingmentioning

confidence: 99%

On Using Physical Analogies for Feature and Shape Extraction in Computer Vision

Nixon¹,

Liu²,

Direkoğlu³

et al. 2009

The Computer Journal

View full text Add to dashboard Cite

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...

show abstract

“…In [6,7], magnetostatic theory was combined with an active contour model. For a moving charge inside a magnetic field, there are magnetic forces introduced by the field acting on it.…”

Section: Using Force Analogies In Image Processingmentioning

confidence: 99%

On Using Physical Analogies for Feature and Shape Extraction in Computer Vision

Nixon¹,

Liu²,

Direkoğlu³

et al. 2009

The Computer Journal

View full text Add to dashboard Cite

show abstract

“…Final segmentation was obtained by comparing these independent segmentations followed by contour shortening based on curvature flow. Xiang et al's simplified long range force between the object boundaries and the active contour takes a similar form to our magnetic force in [31]. However, the MAC model is more intuitive, and uses different strategies to perform initialization and to handle image noise.…”

Section: Initialisation and Convergencementioning

confidence: 99%

“…In [31], we proposed a snake external force field based on magnetostatics. We hypothesized magnetic interactions between the active contours and the object boundaries, resulting in a magnetic field that can push the contours to object boundaries.…”

Section: Initialisation and Convergencementioning

confidence: 99%

MAC: Magnetostatic Active Contour Model

Xie

Mirmehdi

2008

IEEE Trans. Pattern Anal. Mach. Intell.

142

125

View full text Add to dashboard Cite

Abstract-We propose an active contour model using an external force field that is based on magnetostatics and hypothesized magnetic interactions between the active contour and object boundaries. The major contribution of the method is that the interaction of its forces can greatly improve the active contour in capturing complex geometries and dealing with difficult initializations, weak edges and broken boundaries. The proposed method is shown to achieve significant improvements when compared against six well-known and state-of-the-art shape recovery methods, including the geodesic snake, the generalized version of GVF snake, the combined geodesic and GVF snake, and the charged particle model.

show abstract

“…In the original method the energies acting upon the active contour are defined as in equation 11, consisting of an internal energy, the image energy and external constraint energy, which the active contour then tries to minimise. The internal energy acts to maintain the active contour's shape, image energy can be defined via the image intensity (Kass et al (1988)), image gradient (Lam and Yan (1994)), or via more complex methods (Xie and Mirmehdi (2006)), and the external energy defines constraints that the active contour needs to observe that are not directly defined by the Fig. 27 The three vectors that define the direction the control point will move.…”

Section: Crisping the Vague-fieldmentioning

confidence: 99%

Interpreting spatial language in image captions

2010

View full text Add to dashboard Cite

The map as a tool for accessing data has become very popular in recent years, but a lot of data does not have the necessary spatial meta-data to allow for that. Some data such as photographs however have spatial information in their captions and if this could be extracted, then they could be made available via map-based interfaces. Towards this goal we introduce a model and spatio-linguistic reasoner for interpreting the spatial information in image captions that is based upon quantitative data about spatial language use acquired directly from people. Spatial language is inherently vague and both the model and reasoner have been designed to incorporate this vagueness at the quantitative level and not only qualitatively.

show abstract

Magnetostatic Field for the Active Contour Model: A Study in Convergence

Cited by 16 publications

References 14 publications

On Using Physical Analogies for Feature and Shape Extraction in Computer Vision

On Using Physical Analogies for Feature and Shape Extraction in Computer Vision

MAC: Magnetostatic Active Contour Model

Interpreting spatial language in image captions

Contact Info

Product

Resources

About