Blurred Segments in Gray Level Images for Interactive Line Extraction

Kerautret, Bertrand; Even, Philippe

doi:10.1007/978-3-642-10210-3_14

Cited by 6 publications

(7 citation statements)

References 10 publications

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“…In a former paper [12], an ecient tool to detect blurred segments of xed thickness in gray-level images was already introduced. It was based on a rst rough detection in a local image area dened by the user.…”

Section: Previous Workmentioning

confidence: 99%

“…The detection method previously described is continuously run during mouse dragging and the output blurred segment is displayed on-the-y. Details about the supervised mode are discussed in [12].…”

Section: Supervised Blurred Segments Detectionmentioning

confidence: 99%

“…User-friendly solutions are sought, with ideally no parameter to set, or at least quite few values with intuitive meaning. An interactive tool was already designed for live line extractions in gray-level images [12]. But the segment thickness was initially xed by the user and not estimated, leading to erroneous orientations of the detected lines.…”

Section: Introductionmentioning

confidence: 99%

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Thick Line Segment Detection with Fast Directional Tracking

Even¹,

Ngo²,

Kerautret³

2019

Lecture Notes in Computer Science

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This paper introduces a fully discrete framework for a new straight line detector in gray-level images, where line segments are enriched with a thickness parameter intended to provide a quality criterion on the extracted feature. This study is based on a previous work on interactive line detection in gray-level images. At rst, a better estimation of the segment thickness and orientation is achieved through two main improvements: adaptive directional scans and control of assigned thickness. Then, these advances are exploited for a complete unsupervised detection of all the line segments in an image. The new thick line detector is left available in an online demonstration.

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Section: Previous Workmentioning

confidence: 99%

Section: Supervised Blurred Segments Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Thick Line Segment Detection with Fast Directional Tracking

Even¹,

Ngo²,

Kerautret³

2019

Lecture Notes in Computer Science

Self Cite

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show abstract

“…The present work is based on former results on the extraction of blurred segments from gray-level images [18]. A brief summary is given in the following.…”

Section: Thick Segment Recognition With Intensity Pro Le Previous Wmentioning

confidence: 99%

“…lo w e r le a n in g li n e u p p e r le a n in g li n e One of them [13] was used to set up an e cient algorithm to detect blurred segments in a gray-level image [18].…”

Section: De Nition 1 [13]mentioning

confidence: 99%

Photometric Intensity Profiles Analysis for Thick Segment Recognition and Geometric Measures

Aubry

Kerautret²,

Even

et al. 2017

Mathematical Morphology - Theory and Applications

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Abstract:The segmentation or the geometric analysis of specular objects is known as a di cult problem in the computer vision domain. It is also true for the problem of line detection where the specular re ection implies numerous false positive line detection or missing lines located on the dark parts of the object. This limitation reduces its potential use for concrete industrial applications where metallic objects are frequent. In order to overcome this limitation, a new strategy to detect thick segment is proposed. It is not based on the image gradient as usually, but rather exploits the image intensity pro le de ned inside a parallel strip primitive. Associated to a digital straight segment recognition algorithm which is robust to noise, this strategy was implemented to track metallic tubular objects in gray-level images. The e ciency of the proposed method is demonstrated through extensive tests using an actual industrial application. An alternate release intended to overcome the possible impact of the digitization process on the achieved performance is also introduced. Both strategies are discussed at the end of the article.

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Digital Line Segment Detection for Table Reconstruction in Document Images

Ngo

2022

Image Analysis and Processing – ICIAP 2022

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Table detection is often involved in many applications of document analysis as tables are frequently used to present structured information. In this context, we are interested in extracting table regions in document images. More precisely, we propose a method for table detection based on a recent edge line detector which is developed in the context of digital geometry and it allows to handle noisy document images. The extracted lines are then used to reconstruct the tables contained in the image. The method has been evaluated and compared to other state-of-the-art methods and shown a very competitive result.

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Blurred Segments in Gray Level Images for Interactive Line Extraction

Cited by 6 publications

References 10 publications

Thick Line Segment Detection with Fast Directional Tracking

Thick Line Segment Detection with Fast Directional Tracking

Photometric Intensity Profiles Analysis for Thick Segment Recognition and Geometric Measures

Digital Line Segment Detection for Table Reconstruction in Document Images

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