Scale-adaptive detection and local characterization of edges based on wavelet transform

Ducottet, Christophe; Fournel, Thierry; Barat, Cécile

doi:10.1016/j.sigpro.2004.07.003

Cited by 34 publications

(36 citation statements)

References 14 publications

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“…We select multi-directional, multi-scale structural elements for edge detection, in order to filter out noise better; extract edge information of different types more accurately. Use the large-scale operators to detect overall changes; small-scale operators to detect slight changes of the image edge [7,8]. In this paper, we take the edge detection of oral images for example; apply disk and line structural elements in different directions and scales.…”

Section: Select the Structure Elementsmentioning

confidence: 99%

Edge Detection Algorithm Based on the Top-hat Operator

Wang¹,

Mu²

2017

dtcse

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Abstract. This paper proposes an image edge detection method based on multi-directional, multi-scale Top-hat operators, and applies the method to the edge detection of OSAHS (Obstructive Sleep Apnea Hypopnea Syndrome) early pathological images. Firstly, construct multi-directional, multi-scale Top-hat operators, and they are used to detect the edge of image. Then the ideal image edge is obtained by combining the edges of the image detected by each operator according to a certain weight, so that we can calculate the actual area of the oral cavity accurately, and then achieve electronic medical diagnosis. The simulation results show that the operator proposed in this paper can filter out the noise better, preserve image detail more completely, so that the edge information of the image is more accurate and complete. Compared with conventional edge operator, it is more effective for image edge detection.

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Section: Select the Structure Elementsmentioning

confidence: 99%

Edge Detection Algorithm Based on the Top-hat Operator

Wang¹,

Mu²

2017

dtcse

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“…This method was proposed by Ducottet [3] and is based on wavelet transform to perform edge detection. The exclusive feature of this method is that it performs modeling the contours in an image into three types of singularities namely, line, transition and peak.…”

Section: Transform (Spadu)mentioning

confidence: 99%

A Survey on Efficient No-Reference Blur Estimation Methods

Thomas¹,

Livingston

2013

IJRET

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“…A standard strategy is to adopt a multiscale method for edge detection. The algorithm chosen was proposed by Ducottet et al in [1]. It is a wavelet-based algorithm, which allows to detect and locate edges with an automatic adaptation of the scale to their smoothness.…”

Section: Features Computationmentioning

confidence: 99%

Feature Correspondences from Multiple Views of Coplanar Ellipses

Barat

Menudet

Louhichi

et al. 2006

Advances in Visual Computing

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Abstract. We address the problem of feature correspondences in images of coplanar ellipses with objective to benefit of robust ellipse fitting algorithm. The main difficulty is the lack of projective invariant points immediately available. Therefore, our key idea is to construct virtual line and point features using the property of tangent invariance under perspective projection. The proposed method requires first a robust detection of ellipse edge points to fit a parametric model on each ellipse. The feature lines are then obtained by computing the 4 bitangents to each couple of ellipses. The points are derived by considering the tangent points and the intersection points between bitangents. Results of experimental studies are presented to demonstrate the reliability and robustness of the feature extraction process. Subpixel accuracy is easily achieved. A real application to camera self-calibration is also described.

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Scale-adaptive detection and local characterization of edges based on wavelet transform

Cited by 34 publications

References 14 publications

Edge Detection Algorithm Based on the Top-hat Operator

Edge Detection Algorithm Based on the Top-hat Operator

A Survey on Efficient No-Reference Blur Estimation Methods

Feature Correspondences from Multiple Views of Coplanar Ellipses

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