A New Perceptual Edge Detector in Color Images

Montesinos, Philippe; Magnier, Baptiste

doi:10.1007/978-3-642-17688-3_21

Cited by 21 publications

(57 citation statements)

References 12 publications

(21 reference statements)

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“…A simple solution to bypass this effect is to consider paths crossing each pixel in several directions. The idea developed in (Montesinos and Magnier, 2010) is to "cut" the derivative (and smoothing) kernel in two parts: a first part along a first direction and a second part along a second direction (see Fig. 4 (b).…”

Section: Flat Area Detectionmentioning

confidence: 99%

“…Once obtained ∇I , θ 1 and θ 2 , edges can easily be extracted by computing local maxima of ∇I in the direction of the angle (θ 1 + θ 2 )/2 followed by an hysteresis threshold (see (Montesinos and Magnier, 2010) for more details). In this paper, we are only interested by the two directions (θ 1 , θ 2 ) and the gradient magnitude used in our diffusion scheme in section 5.2.…”

Section: Edge Detection Using Half Smoothing Kernelsmentioning

confidence: 99%

“…In this paper, we present a rotating filter (inspired by (Montesinos and Magnier, 2010), (Magnier et al, 2011c) and (Magnier et al, 2011b)) able to detect homogenous regions and edges regions, even in highly noisy images. Then, we present an anisotropic edge detector which defines two directions for pixels belonging to edges.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we extend this method to restoration. More precisely, the diffusion is controlled both by the gradient value and two directions issued from an anisotropic edge detector (Montesinos and Magnier, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…1) in order to build a signal s which is a function of a rotation angle θ and the underlying signal. As shown in (Montesinos and Magnier, 2010), (Magnier et al, 2011c) and (Magnier et al, 2011b), smoothing with rotating filters means that the image is smoothed with a bank of rotated anisotropic Gaussian half kernels:…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A New Region-Based Pde for Perceptual Image Restoration

Magnier¹,

Montesinos²,

Diep³

2012

Proceedings of the International Conference on Computer Vision Theory and Applications

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Abstract:In this paper, we present a new image regularization method using a rotating smoothing filter. The novelty of this approach resides in the mixing of ideas coming both from pixel classification which determines roughly if a pixel belongs to a homogenous region or an edge and an anisotropic perceptual edge detector which computes two precise diffusion directions. These directions are used by an anisotropic diffusion scheme. This anisotropic diffusion is accurately controlled near edges and corners, while isotropic diffusion is applied to smooth homogeneous and highly noisy regions. Our results and a comparison with anisotropic diffusion methods applied on a real image show that our model is able to efficiently regularize images and to control the diffusion.

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Section: Flat Area Detectionmentioning

confidence: 99%

Section: Edge Detection Using Half Smoothing Kernelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A New Region-Based Pde for Perceptual Image Restoration

Magnier¹,

Montesinos²,

Diep³

2012

Proceedings of the International Conference on Computer Vision Theory and Applications

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Derivative Half Gaussian Kernels and Shock Filter

Magnier¹,

Noblet²,

Voisin³

et al. 2018

Advanced Concepts for Intelligent Vision Systems

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Shock filter represents an important family in the field of nonlinear Partial Differential Equations (PDEs) models for image restoration and enhancement. Commonly, the smoothed second order derivative of the image assists this type of method in the deblurring mechanism. This paper presents the advantages to insert information issued of oriented half Gaussian kernels in a shock filter process. Edge directions assist to preserve contours whereas the gradient direction allow to enhance and deblur images. For this purpose, the two edge directions are extracted by the oriented half kernels, preserving and enhancing well corner points and object contours as well as small objects. The proposed approach is compared to 7 other PDE techniques, presenting its robustness and reliability, without creating a grainy effect around edges.

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A Multi-scale Line Feature Detection Using Second Order Semi-Gaussian Filters

Magnier¹,

Shokouh²,

Xu³

et al. 2021

Computer Analysis of Images and Patterns

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Among the common image structures, line feature is the extensively used geometric structure for various image processing applications, including the analysis of biomedical image with blood vessels highlighting, graph-shape structures, cracks detection, satellite images or remote sensing data. Multi-scale processing of line feature is essentially required for the extraction of more relevant information or line structures of heterogeneous widths. In this paper, a multi-scale filtering-based line detection approach using second-order semi-Gaussian anisotropic kernel is proposed. Meanwhile, a strategy is introduced to calculate the strength of the observed line feature across the different scales. The proposed technique is evaluated on real images by using their tied hand-labeled images. Finally, the experimental results and comparison of images containing different line feature widths with state-of-the-art techniques have sufficiently supported the effectiveness of our technique.

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A New Perceptual Edge Detector in Color Images

Cited by 21 publications

References 12 publications

A New Region-Based Pde for Perceptual Image Restoration

A New Region-Based Pde for Perceptual Image Restoration

Derivative Half Gaussian Kernels and Shock Filter

A Multi-scale Line Feature Detection Using Second Order Semi-Gaussian Filters

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