A Novel Color Edge Detection Technique Using Hilbert Transform

Gupta, Ankush; Ganguly, Ayush; Bhateja, Vikrant

doi:10.1007/978-3-642-35314-7_82

Cited by 12 publications

(4 citation statements)

References 7 publications

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“…N represents total number of pixels present in the input image. This filter has been well applied in recent past to catalyze the performance of edge detector in noisy environments [32]- [33]. Having converted the image into homogeneous and non-homogeneous regions, it is now needed that the processing of these regions be done separately.…”

Section: Repeat and Processmentioning

confidence: 99%

Bilateral despeckling filter in homogeneity domain for breast ultrasound images

Bhateja

Misra

Urooj

et al. 2014

2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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Breast sonograms are more effective towards differentiation of cysts from solid tumours; if they could be postprocessed for minimization of speckle content without blurring of edges. The approach presented in this paper consists of a bilateral filtering in homogeneity domain so that the despeckling process do not compromises the texture and features of masses. The proposed despeckled approach decomposes the input image into homogeneous and non-homogeneous regions; which are then selectively processed using the bilateral filter. The domain filtering component is made dominant when applied to homogeneous pixels providing smoothening while the range filter dominates on the non-homogeneous pixels leading to edge preservation. Simulations carried out on breast ultrasound images depict satisfactory speckle filtering supported with improvement in values of performance parameters (PSNR, SSIM & SSI).

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Section: Repeat and Processmentioning

confidence: 99%

Bilateral despeckling filter in homogeneity domain for breast ultrasound images

Bhateja

Misra

Urooj

et al. 2014

2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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“…However, these proposals are sensitive to image noise; thus, some studies are devoted to improving the Hilbert transformbased method by reducing the influence of image noise. For example, Kumar et al [61] developed an edge enhancement method by using the one-dimensional (1D) discrete Hilbert transform of the Gaussian function to suppress image noise; Gupta et al [64] used bilateral filter to smooth the image before using Hilbert transform for colour edge detection; Davis et al [34] developed a general radial Hilbert transform (GRHLT) to permit two-dimensional edge enhancement by introducing a transfer function in which opposite halves of any radial line have a relative phase difference of 𝜋 rad. The GRHLT algorithm can suppress noise because it has longer impulse response.…”

Section: Introductionmentioning

confidence: 99%

“…[61] developed an edge enhancement method by using the one‐dimensional (1D) discrete Hilbert transform of the Gaussian function to suppress image noise; Gupta et al. [64] used bilateral filter to smooth the image before using Hilbert transform for colour edge detection; Davis et al. [34] developed a general radial Hilbert transform (GRHLT) to permit two‐dimensional edge enhancement by introducing a transfer function in which opposite halves of any radial line have a relative phase difference of

π

rad.…”

Section: Introductionmentioning

confidence: 99%

Edge feature enhancement approach using hilbert transform of Cauchy distribution and its applications

Wang

Rohde

Xiao

2021

IET Image Processing

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Building on past work showing that the Hilbert transform can be used for edge feature enhancement, a new edge feature enhancement method is developed using a twodimensional (2D) isotropic Hilbert transform of the Cauchy distribution. First, both the shape of the Hilbert kernel and the Hilbert transform of edge feature models (step and delta) and various simulated signals result in edge feature enhancement, the properties of which are derived and confirmed. Second, Cauchy distribution as low-pass filter is introduced in Hilbert transform to get new edge feature enhancement operator, and its efficiency under various criteria is comprehensively discussed. Third, a 2D isotropic extension is presented using a circularly symmetric window function. Finally, two experiments, including edge detection and image segmentation, are performed to validate the proposed edge feature enhancement method. The experimental results of the method applied to the Berkeley Segmentation Dataset and remote sensing images demonstrate that the new method is effective for edge detection and image segmentation. INTRODUCTIONEdges are important features in images and provide prominent information about objects in the natural scenes, while edge strength is essential to computer vision, image processing, and pattern recognition, especially directly edge detection, contour detection, and image segmentation. Its applications benefit many fields, including image segmentation [1-3], object recognition[4], remote sensing [5,6], medical imaging [7-9], 3D reconstruction [10], face recognition [11,12], and image retrieval [11,13]. In the past several decades, various edge feature enhancement approaches have been proposed, including gradient-based methods [14][15][16], morphology gradient methods [17,18], logicbased methods [19], machine learning methods [2,20,21], local energy [22,23], and phase congruency [24,25]. Though considerable progress has been made regarding the efficiency of edge feature enhancement, there is still a challenge, particularly obtaining more efficient edge strength for further application.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…The traditional Canny operator has the defect that being vulnerable to various noise disturbances [4], [5]. Many new methods of edge detection emerged recent years, for example, edge detection method based on morphology [6], edge detection using gray system theory [7], [8], etc [9]- [11].…”

Section: Introductionmentioning

confidence: 99%

A New Edge Detection Method Based on Hausdorff Distance

You¹,

Qing-xin²,

Guo³

2014

2014 5th International Conference on Digital Home

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Most edge detection methods are based on firstorder or second-order differential. These are local methods. Using Hausdorff distance to quantify the strength of the edge is a method with a holistic property. Firstly, downsample the image, and split the image into two sets. Secondly, get the feature image by assigning a value for each point using the scalar field map constructed by Hausdorff distance. Since the edge features have local properties, in this paper, we constructed a map which can get local feature images using sub image and combined them into a feature image. Finally we present a method to get the edge image by feature image. We experimentally verify the feasibility of this method.

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A Novel Color Edge Detection Technique Using Hilbert Transform

Cited by 12 publications

References 7 publications

Bilateral despeckling filter in homogeneity domain for breast ultrasound images

Bilateral despeckling filter in homogeneity domain for breast ultrasound images

Edge feature enhancement approach using hilbert transform of Cauchy distribution and its applications

A New Edge Detection Method Based on Hausdorff Distance

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