Oriented asymmetric kernels for corner detection

Abdulrahman, Hasan; Magnier, Baptiste; Montesinos, Philippe

doi:10.23919/eusipco.2017.8081313

Cited by 5 publications

(6 citation statements)

References 22 publications

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“…Oriented half kernels (HK) represent thin filters enable to describe the image information all around a considered pixel. As they are robust against noise and their edge directions are accurate, HK are utilized in the context of many image processing problems [14,3,25]. This study presents different manners to build HK devoted to edge detection in digital images.…”

Section: Resultsmentioning

confidence: 99%

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Image and Signal Processing

Bazzi

Mammass²,

Zaki³

et al. 2018

Lecture Notes in Computer Science

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Easy to use, oriented half kernels are reliable in image analysis. These thin filters, rotated in all the desired directions are useful to detect edges, or extract precisely their orientations, even concerning highly noisy images. Usually, the filtering process corresponds to convolutions with Gaussians and their derivatives. Other filters exist and can be implemented in order to build half kernels. However, functions used for the smoothing and derivative parts have not been studied in depth. The goal of this paper is to evaluate different types of half filters as a function of the noise level. The studied kernels have the same spatial support, enabling easier comparisons. To address the robustness of the studied filters against noise, the image quality is gradually worsened. Then, their performances are compared through objective evaluations of both segmentation and gradient direction.

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Section: Resultsmentioning

confidence: 99%

“…2(c)). These orientations are useful and efficient for image restoration via PDE [14], corner detection [3] or image descriptor [25].…”

Section: (D)mentioning

confidence: 99%

Image and Signal Processing

Bazzi

Mammass²,

Zaki³

et al. 2018

Lecture Notes in Computer Science

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“…5 for two edge directions at 0 • and 185 • respectively. These directions are useful and efficient for image restoration via PDE [4], corner detection [5] or image descriptor [6]. Finally, to obtain thin edges, as in [7], the local maxima suppression operation is processed in the η orientation, corresponding to the bisector between θ 1 and θ 2 .…”

Section: Derivative Half Gaussian Kernelsmentioning

confidence: 99%

“…1 (e) points 1 to 3. These directions can be estimated using half-filters; they are useful and effective for restoring images via PDE [4], corner detection [5] or descriptors [6]. As a result, the filter must be narrow to maintain the most robust precision possible.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Half Gaussian Filter Rotation for Edge Detection

Magnier

Moradi

Carré

2019

2019 8th European Workshop on Visual Information Processing (EUVIP)

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In image processing, contours can be extracted by linear convolution of the image with first-order derivative filters. The oriented half Gaussian filters are directed in all desired directions to analyze the images. Thus, they are useful for detecting contours or extracting precise orientations. The process of filter orientation requires the application of a rotation technique in a two-dimensional discrete domain of different scientific strategies: interpolation (which can modify image information) or discrete geometry (without modification of the image values). In this paper, different methods of discrete rotating the half Gaussian filter are compared and evaluated to detect contours in synthetic and real images. Results are qualitatively and quantitatively compared, validating which rotation technique is most beneficial for edge detection.

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“…4(c), β denotes the angle formed by θ 1 and θ 2 , corresponding to an angular sector. These directions are useful and efficient for image restoration via PDE [13], corner detection [1] or image descriptor [20]. All these entities computed via eq.…”

Section: Derivative Half Gaussian Kernelsmentioning

confidence: 99%

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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Oriented asymmetric kernels for corner detection

Cited by 5 publications

References 22 publications

Image and Signal Processing

Image and Signal Processing

Evaluation of Half Gaussian Filter Rotation for Edge Detection

Derivative Half Gaussian Kernels and Shock Filter

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