Optimal blurred segments decomposition of noisy shapes in linear time

Debled-Rennesson, Isabelle; Feschet, Fabien; Rouyer-Degli, Jocelyne

doi:10.1016/j.cag.2005.10.007

Cited by 74 publications

(88 citation statements)

References 17 publications

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“…So, C is a polygon that approximates a circle or an arc of circle if and only if M pC = {M i } n−1 i=0 is a set of collinear points. Blurred Segment of width ν [10]. It is a set of points that is included in a band of width ν.…”

Section: Linear Methods For Arc Recognition and Segmentationmentioning

confidence: 99%

Unsupervised, Fast and Precise Recognition of Digital Arcs in Noisy Images

Nguyễn

Kerautret

Debled-Rennesson

et al. 2010

Computer Vision and Graphics

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Abstract. In image processing and pattern recognition, the accuracy of most algorithms is dependent on a good parameterization, generally a computation scale or an estimation of the amount of noise, which may be global or variable within the input image. Recently, a simple and linear time algorithm for arc detection in images was proposed [1]. Its accuracy is dependent on the correct evaluation of the amount of noise, which was set by the user in this former version. In the present work we integrate a promising unsupervised noise detection method [2] in this arc recognition method, in order to process images with or without noise, uniformly distributed or variable within the picture. We evaluate the performance of this algorithm and we compare it with standard arc and circle detection methods based on extensions of the Hough transform.

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Section: Linear Methods For Arc Recognition and Segmentationmentioning

confidence: 99%

Unsupervised, Fast and Precise Recognition of Digital Arcs in Noisy Images

Nguyễn

Kerautret

Debled-Rennesson

et al. 2010

Computer Vision and Graphics

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“…In their review of curvature based corner detectors, Kerautret et al [25] emphasize blurred line segments [26] as common to a number of curvature estimators. Where the "blurred segment is a piece of thick digital straight line, whose width is the vertical diameter of its convex hull".…”

Section: Curvaturementioning

confidence: 99%

“…Using blurred line segments [26] to find the line segments to use in the curvature estimator will likely provide more discrimination in the curvature estimator and may provide some further insight.…”

Section: Future Workmentioning

confidence: 99%

Automated measurement of sintering degree in optical microscopy through image analysis of particle joins

Nellros

Thurley

Jönsson

et al. 2015

Pattern Recognition

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In general terms, sintering describes the bonding of particles into a more coherent structure, where joins form between packed particles, usually as a result of heating. Characterization of sintering is an important topic in the fields of metallurgy, steel, iron ore pellets, ceramics, and snow for understanding material properties and material strength. Characterization using image analysis has been applied in a number of these fields but is either semi-automatic, requiring human interaction in the analysis, or based on statistical sampling and stereology to characterize the sample. This paper presents a novel fully automatic image analysis algorithm to analyze and determine the degree of sintering based on analysis of the particle joins and structure. Quantitative image analysis of the sintering degree is demonstrated for samples of iron ore pellets but could be readily applied to other packed particle materials. Microscope images of polished cross-sections of iron ore pellets have been imaged in their entirety and automated analysis of hundreds of images has been performed. Joins between particles have been identified based on morphological image processing and features have been calculated based on the geometric properties and curvature of these joins. The features have been analyzed and determined to hold discriminative power based on a statistical evaluation using the Welch t-test and display properties consistent with sintering theory and results from traditional pellet diameter measurements on the heated samples.

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“…The optimisation process is then achieved with a relaxation approach. To obtain robustness to noise the maximal discrete segments from the tangential cover are replaced by the blurred maximal segments [14] which allow to take into account the amount of noise. Note that a parameter ν permits to control the sensibility to noise.…”

Section: Global Min-curvature Estimator (Gmc)mentioning

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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Optimal blurred segments decomposition of noisy shapes in linear time

Cited by 74 publications

References 17 publications

Unsupervised, Fast and Precise Recognition of Digital Arcs in Noisy Images

Unsupervised, Fast and Precise Recognition of Digital Arcs in Noisy Images

Automated measurement of sintering degree in optical microscopy through image analysis of particle joins

Discrete Contour Extraction from Reference Curvature Function

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