Line extraction algorithm based on image vectorization

Naouai, Mohamed; Narjess, Melki; Hamouda, Atef

doi:10.1109/icma.2010.5588627

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…This can be achieved through skeletonisation [9] which will however intoreduce distortions as the lines approach junction points [6]. To avoid these distortions, other methods can be employed to obtain the medial axis, for example, through the use of contours [22] and Delauny triangles [16] among others. Since these methods assess whether each pixel is a candidate pixel on the medial axis, they incur large computational costs which may be reduced by adapting sparse pixel tracking approaches such as that described in [18,17,1] among others.…”

Section: Related Workmentioning

confidence: 99%

Vectorisation of Sketched Drawings Using Co-occurring Sample Circles

Bonnici

Camilleri

2015

Computer Analysis of Images and Patterns

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This paper presents a drawing vectorisation algorithm which uses multiple concentric families of circles placed in a dense grid on the image space. We show that any off-centered junction within the family of circles can be located and hence show how these junction points may be linked to neighbouring junction points, thereby creating a vector representation of the drawing geometry. The proposed algorithm identified 98% of the junctions in the drawings on which it was evaluated, each within a localisation error of 4.7 ± 2.3 pixels, resulting in straight line vectors which are well placed with respect to the drawn edges.

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Section: Related Workmentioning

confidence: 99%

Vectorisation of Sketched Drawings Using Co-occurring Sample Circles

Bonnici

Camilleri

2015

Computer Analysis of Images and Patterns

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“…This object identification is made in the forms analysis phase which generally occurs after a step of image segmentation [14]. The shape analysis is used in several application areas such as medicine to detect anomalies, security to identify individuals, computer-aided design and computer-aided manufacturing process to compare design parts or mechanical objects design etc.…”

Section: Introductionmentioning

confidence: 99%

Measuring Rectangularity Using GR-signature

Hentati

Naouai

Hamouda

et al. 2011

Lecture Notes in Computer Science

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Abstract. Object recognition often operates by making decisions based on the values of several shape properties measured from an image of the object. In this paper, we propose a new exploitation of the Radon Transform using the gradient measurement to generate a new signature (GR-signature) which provides global information of a binary shape regardless its form. We also develop a new method for measuring the rectangularity based on GR-signature. This original approach looks very promising and has several useful properties that keep fundamental geometrical transformations like scale, rotation and translation.

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A Survey on Road Extraction From Color Image Using Vectorization

Sharma¹

2013

IJRET

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Line extraction algorithm based on image vectorization

Cited by 3 publications

References 27 publications

Vectorisation of Sketched Drawings Using Co-occurring Sample Circles

Vectorisation of Sketched Drawings Using Co-occurring Sample Circles

Measuring Rectangularity Using GR-signature

A Survey on Road Extraction From Color Image Using Vectorization

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