A Linear Algorithm for Segmentation of Digital Curves

Debled-Rennesson, Isabelle; Reveillès, Jean-Pierre

doi:10.1142/s0218001495000249

Cited by 146 publications

(105 citation statements)

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“…Previous algorithms [DR95] take advantage of this in order to obtain a linear time complexity. We present an elementary implementation designed for this special case.…”

Section: A Special Casementioning

confidence: 99%

“…Previous incremental algorithms deal with the 8-connected case [DR95] or with sophisticated machinery coming from Linear Programming [Buz03]. We present the first elementary method [Buz02] that works with any set of points (not necessarily 8-connected) and we propose a linear time algorithm under some restrictions (which were implicitly assumed in [DR95]). This paper deals with implementation details giving pseudo-code of our method.…”

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confidence: 99%

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An Elementary Algorithm for Digital Line Recognition in the General Case

Buzer

2005

Discrete Geometry for Computer Imagery

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Abstract. This paper is concerned with the naive and, more generally, α-thick digital line recognition problem. Previous incremental algorithms deal with the 8-connected case [DR95] or with sophisticated machinery coming from Linear Programming [Buz03]. We present the first elementary method [Buz02] that works with any set of points (not necessarily 8-connected) and we propose a linear time algorithm under some restrictions (which were implicitly assumed in [DR95]). This paper deals with implementation details giving pseudo-code of our method. We insist on linking the recognition problem to the intrinsic properties of convex hulls.

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“…Previous algorithms [DR95] take advantage of this in order to obtain a linear time complexity. We present an elementary implementation designed for this special case.…”

Section: A Special Casementioning

confidence: 99%

mentioning

confidence: 99%

An Elementary Algorithm for Digital Line Recognition in the General Case

Buzer

2005

Discrete Geometry for Computer Imagery

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“…Apart from the point itself, the end points of the two longest digitally straight segments (DSS:s) that extends backwards and forwards from the point of interest and along the outline, respectively, are used to compute the curvature. Such segments can be computed in linear time as shown by Debled-Rennesson and Reveillès [23].…”

Section: Curvaturementioning

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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“…Based on this definition, a recognition problem may arise: given a set of grid points, does there exists a DSS containing it ? In the literature, many algorithms have been proposed [1][2][3][4][5]. In the following, we do not go further into algorithmic details, we just focus on a main result: adding a new 4-adjacent pixel to a DSS, deciding if the resulting set is still a DSS and updating the DSS parameters can be done in constant time O(1).…”

Section: Digital Straight Segments and Digital Contour Reconstructionmentioning

confidence: 99%

“…Indeed, DSSs play an important role in polygonalization of discrete object contours [1][2][3][4][5]. Recognition algorithms are used to reconstruct a binary curve, i.e.…”

Section: Introductionmentioning

confidence: 99%