Live-wire-based segmentation using similarities between corresponding image structures

Färber, Matthias; Ehrhardt, Jan; Handels, Heinz

doi:10.1016/j.compmedimag.2007.06.005

Cited by 17 publications

(8 citation statements)

References 24 publications

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“…It was firstly proposed by Barrett and Mortensen in 1992 [6], and can be divided into two parts: defining the cost function and searching for the optimal path. The cost function which measures the boudaries of objects, is defined based on the 8 neighboring pixels as shown in formula 1 [7]:…”

Section: Image Segmentation Methodsmentioning

confidence: 99%

A Region-Based Image Fusion Algorithm for Detecting Trees in Forests

Yu¹,

Yan²,

Han³

2014

TOCSJ

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Multi-sensor image fusion has been widely used in many areas. In this paper, a region-based method using NSCT and fuzzy logic is proposed to enhance trees' contour and enrich the information in images. Firstly, the required source images are segmented, which are then decomposed using the NSCT transform. In low frequency domain, the fuzzy fusion method is used. And in high frequency domain, a region-based rule is proposed to highlight the objects' detail within images. Finally, the fused images are obtained by an inverse NSCT transform. Nine pieces of objective criteria and computation time are adopted to evaluate the fusion result. Moreover 30 groups of collected images are utilized to prove the robustness of the proposed method. The results show that the algorithm could provide fused images which have a higher visual effect, combine more information and consume less time. Furthermore, this process could better capture the contour and texture of the trees.

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Section: Image Segmentation Methodsmentioning

confidence: 99%

A Region-Based Image Fusion Algorithm for Detecting Trees in Forests

Yu¹,

Yan²,

Han³

2014

TOCSJ

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“…This technique helps the user to define contours by computing image gradient and computing minimal path from user defined points. It was frequently used in medical application for shapes measures as in [7,8]. Other more recent techniques exploit this idea (lazy snaping [9], enhanced lane [10] or grabcut [11]).…”

Section: Introductionmentioning

confidence: 99%

“…Another recent work from Färber et al proposed a Live-wire based segmentation approach to associate corresponding image structures. In that case, the curvature was only used as a parameter for the contour association [7].…”

Section: Introductionmentioning

confidence: 99%

Discrete Contour Extraction from Reference Curvature Function

Nam

Kerautret

Desbarats

et al. 2008

Advances in Visual Computing

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Abstract. A robust discrete curvature estimator was recently proposed by Kerautret et al. [1]. In this paper, we exploit the precision and stability of this estimator in order to define a contour extraction method for analysing geometric features. We propose to use a reference curvature function for extracting the frontier of a shape in a gray level image. The frontier extraction is done by using both geometric information represented by the reference curvature and gradient information contained in the source image. The application of this work is done in a medical application.

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“…Since then, many studies have applied the small eigenvalues to detect corners directly [15], [16], [18], [21]. In addition, some others studies were also inspired by Tsai et al's small eigenvalues approach [6], [7], [13], [14], [17], [19], [22], [23]. Although Sossa Azuela et al [20] commented that Tsai et al's method was the best one among the methods they had tested, Guru et al [8] later discovered that Tsai et al's method may also detect unwanted spurious corners.…”

Section: Introductionmentioning

confidence: 99%

“…Others use morphological operators to extract corners [5]. Boundary-based corner detectors, segmenting objects from an image first and then locating the discontinuities on the object boundaries [6]- [13], have been widely applied to spline curve fitting [14], [15], automated visual inspection [16]- [19], image segmentation [20]- [22], object recognition [23], etc.…”

Section: Introductionmentioning

confidence: 99%

Revision of Using Eigenvalues of Covariance Matrices in Boundary-Based Corner Detection

Horng

Chen

2009

IEICE Trans. Inf. & Syst.

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SUMMARYIn this paper, we present a revision of using eigenvalues of covariance matrices proposed by Tsai et al. as a measure of significance (i.e., curvature) for boundary-based corner detection. We first show the pitfall of Tsai et al.'s approach. We then further investigate the properties of eigenvalues of covariance matrices of three different types of curves and point out a mistake made by Tsai et al.'s method. Finally, we propose a modification of using eigenvalues as a measure of significance for corner detection to remedy their defect. The experiment results show that under the same conditions of the test patterns, in addition to correctly detecting all true corners, the spurious corners detected by Tsai et al.'s method disappear in our modified measure of significance.

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Live-wire-based segmentation using similarities between corresponding image structures

Cited by 17 publications

References 24 publications

A Region-Based Image Fusion Algorithm for Detecting Trees in Forests

A Region-Based Image Fusion Algorithm for Detecting Trees in Forests

Discrete Contour Extraction from Reference Curvature Function

Revision of Using Eigenvalues of Covariance Matrices in Boundary-Based Corner Detection

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