Grouping ., -, →,[formula], into Regions, Curves, and Junctions

Lee, Mi-Suen; Medioni, Gérard

doi:10.1006/cviu.1999.0787

Cited by 29 publications

(13 citation statements)

References 25 publications

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“…Concerning facilitation, one of the best known approaches to compute contour saliency is tensor voting [133]. A vector field E x r ð Þ, called extension field, is associated with each pixel x of the image (Fig.…”

Section: Contour Saliencymentioning

confidence: 99%

Edge and line oriented contour detection: State of the art

Papari

Petkov

2011

Image and Vision Computing

315

210

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Section: Contour Saliencymentioning

confidence: 99%

Edge and line oriented contour detection: State of the art

Papari

Petkov

2011

Image and Vision Computing

315

210

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“…Therefore, we propose to use the tensor voting framework for detection and removal of noise, which results in accurate segmentation. Tensor voting was proposed by Medioni et al in [19,20] and has been applied to diverse research fields such as the inference of object boundaries [21,22]: as a consequence, its use can explain the presence of noise based on surface saliency in the image feature space. This noise can be then removed by an adaptive median filter.…”

Section: Introductionmentioning

confidence: 99%

Text segmentation in color images using tensor voting

Lim

Park

Medioni

2007

Image and Vision Computing

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“…Tensor voting has been performed by G.G. Medioni et al in [16] [17], and has been applied to various application fields such as the inference of object boundaries: as a consequence, its use can explain the presence of noises based on surface saliency in the image feature space. In our paper, noises can be removed by adaptive vector median method.…”

Section: Introductionmentioning

confidence: 99%

Restoration of Corrupted Region and Segmentation

Park

Toan

Lee

2007

2007 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing

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Image segmentation is fundamental to many image analysis problems. It aims to partition a digital image into a set of non-overlapping homogeneous regions. This paper describes a new method for restoration and segmentation in corrupted text images on the basis of color feature analysis by tensor voting in 3D. It is show how feature analysis can benefit from analyzing features using second order tensor. Proposed technique is applied to text images corrupted by manifold types of various noises. Firstly, selected dominant features in color space are analyzed by tensor voting in 3D, and noises are removed by an adaptive vector median iteratively. Finally, the region segmentation is performed by adaptive mean shift and separated clustering method respectively. We present experimental results of the proposed method operating on an image corrupted by various noises.

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Grouping ., -, →,[formula], into Regions, Curves, and Junctions

Cited by 29 publications

References 25 publications

Edge and line oriented contour detection: State of the art

Edge and line oriented contour detection: State of the art

Text segmentation in color images using tensor voting

Restoration of Corrupted Region and Segmentation

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