Anti-geometric diffusion for adaptive thresholding and fast segmentation

Manay, Siddharth; Yezzi, Anthony

doi:10.1109/tip.2003.818039

Cited by 34 publications

(25 citation statements)

References 37 publications

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“…Anisotropic diffusion, which was introduced to computer vision by Perona and Malik (1990), is a state-of-art image enhancement technique. In (Manay and Yezzi, 2003), the anti-geometric heat flow model was introduced for the segmentation of regions, where the flow was represented as diffusion through the normal direction of edges. A smooth shape extraction algorithm was also presented in (Direkoglu and Nixon, 2007) by solving the particular heat conduction problems in the image domain.…”

Section: Using Heat Flow In Image Analysismentioning

confidence: 99%

Moving-edge detection via heat flow analogy

Direkoğlu

Nixon

2011

Pattern Recognition Letters

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a b s t r a c tIn this paper, a new and automatic moving-edge detection algorithm is proposed, based on using the heat flow analogy. This algorithm starts with anisotropic heat diffusion in the spatial domain, to remove noise and sharpen region boundaries for the purpose of obtaining high quality edge data. Then, isotropic and linear heat diffusion is applied in the temporal domain to calculate the total amount of heat flow. The moving-edges are represented as the total amount of heat flow out from the reference frame. The overall process is completed by non-maxima suppression and hysteresis thresholding to obtain binary movingedges. Evaluation, on a variety of data, indicates that this approach can handle noise in the temporal domain because of the averaging inherent of isotropic heat flow. Results also show that this technique can detect moving-edges in image sequences, without background image subtraction.

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Section: Using Heat Flow In Image Analysismentioning

confidence: 99%

Moving-edge detection via heat flow analogy

Direkoğlu

Nixon

2011

Pattern Recognition Letters

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“…Anisotropic diffusion, which was introduced to computer vision by Perona and Malik [29], is the state-of-art image enhancement technique. In [30], the anti-geometric heat flow model was introduced for the segmentation of regions. Here, anti-geometric heat flow is represented as diffusion through the normal direction of edges.…”

Section: Heat Flow In Image Processing and Computer Visionmentioning

confidence: 99%

On using an analogy to heat flow for shape extraction

Direkoğlu

Nixon

2011

Pattern Anal Applic

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We introduce a novel evolution-based segmentation algorithm which uses the heat flow analogy to gain practical advantage. The proposed algorithm consists of two parts. In the first part, we represent a particular heat conduction problem in the image domain to roughly segment the region of interest. Then we use geometric heat flow to complete the segmentation, by smoothing extracted boundaries and removing noise inside the prior segmented region. The proposed algorithm is compared with active contour models and is tested on synthetic and medical images. Experimental results indicate that our approach works well in noisy conditions without pre-processing. It can detect multiple objects simultaneously. It is also computationally more efficient and easier to control and implement in comparison with active contour models.Keywords Feature extraction Á Shape extraction Á Segmentation Á Heat flow Originality and contributionIn this paper, we introduce a novel evolution-based image segmentation algorithm based on the heat flow analogy. Our model is new and different because of the evolution technique and organization of segmentation. The evolution is achieved with the heat conduction equations to gain practical advantages. Our model is also organized effectively to improve the segmentation. The proposed algorithm has two parts. In the first part, we represent a particular heat conduction problem in the image domain to roughly segment the region of interest. Then, in the second part, geometric heat flow is used to complete the segmentation, by smoothing extracted boundaries and removing noise inside the prior segmented region. Our algorithm is compared with popular active contour models and is tested on synthetic and medical images. Experimental results indicate that our approach works well in noisy conditions. It is also computationally more efficient and easier to control and implement in comparison with active contour models. IntroductionThere are two main types of shape extraction method that evolve to the target solution: active contours and region growing techniques. We first review these techniques with special consideration of their advantages and practical limitations. We then describe techniques which are based on the use of the heat flow analogy, including the proposed model and its advantages as a segmentation technique. Related works Active contoursActive contours (snakes) are curves that evolve to recover object shapes. Active contours can be classified as Parametric Active Contours (PAC) and Geometric Active Contours (GAC) according to their representation. These models mainly differ in their ability to handle multiple object detection, computational efficiency and complexity of implementation.

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“…The pyramid is constructed using the scale-space representation of the anisotropic diffusion. In [12], the anti-geometric heat-flow model was introduced for the segmentation of regions. Here, anti-geometric heat flow is represented as diffusion 4 M.S.…”

Section: Deployment Of Water and Heat In Image Analysismentioning

confidence: 99%

“…Following this, isotropic or linear heat equation is applied in the temporal domain to calculate the total amount of heat flow from an image sequence derived by application of the Sobel edge operator to the filtered images. The discrete form of the isotropic heat equation is given as an iterative process as follows: (12) where E n−1 t , E n−1 t−1 and E n−1 t+1 are images at iteration n − 1 resulting from the application of the Sobel operator to the images resulting from anisotropic diffusion, as shown in Fig. 13d-f.…”

Section: Low-level Featuresmentioning

confidence: 99%

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

Nixon¹,

Liu²,

Direkoğlu³

et al. 2009

The Computer Journal

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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...

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Anti-geometric diffusion for adaptive thresholding and fast segmentation

Cited by 34 publications

References 37 publications

Moving-edge detection via heat flow analogy

Moving-edge detection via heat flow analogy

On using an analogy to heat flow for shape extraction

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

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