Lagrange Gradient Mask for Optical Image Processing

Ahmad, Falih; Gilbert, Matthew R.; Myers, Stephen; Pacheco, José Antonio Navarrete; Castellane, Ray M.; Miller, Ernest L.

doi:10.2174/1874328500701010004

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…This correction dramatically increases the accuracy of both the resulting orientation, φ = tan −1 (dy, dx), and magnitude, r = dx 2 + dy 2 , as calculated based on dx dy = xpos−xneg ypos−yneg in 2D image space, from resulting gradient vector Figure 2 illustrates, this approach has several advantages over conventional Sobel, Gabor and Lagrange gradients [28] in that it is invariant to region size, offers sub-pixel accuracy and has significantly higher noise resistance. Computationally it remains more efficient than Gabor and Lagrange approaches and only marginally more complex than Sobel mask convolution.…”

Section: Gradients From Centroids (Grace)mentioning

confidence: 99%

Dense gradient-based features (DEGRAF) for computationally efficient and invariant feature extraction in real-time applications

Katramados

Breckon

2016

2016 IEEE International Conference on Image Processing (ICIP)

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Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. ABSTRACTWe propose a computationally efficient approach for the extraction of dense gradient-based features based on the use of localized intensity-weighted centroids within the image. Whilst prior work concentrates on sparse feature derivations or computationally expensive dense scene sensing, we show that Dense Gradient-based Features (DeGraF) can be derived based on initial multi-scale division of Gaussian preprocessing, weighted centroid gradient calculation and either local saliency (DeGraF-α) or signal-to-noise inspired (DeGraF-β) final stage filtering. We present two variants (DeGraF-α / DeGraF-β) of which the signal-to-noise based approach is shown to perform admirably against the state of the art in terms of feature density, computational efficiency and feature stability. Our approach is evaluated under a range of environmental conditions typical of automotive sensing applications with strong feature density requirements.

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Section: Gradients From Centroids (Grace)mentioning

confidence: 99%

Dense gradient-based features (DEGRAF) for computationally efficient and invariant feature extraction in real-time applications

Katramados

Breckon

2016

2016 IEEE International Conference on Image Processing (ICIP)

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“…The characteristics of Legendre and Lagrange polynomials are the main reasons for the use of this mask. The applicability of the Lagrange mask to image processing is supported through its characterisation using the three criteria of good detection, exact localisation probability, and noise maximum probability [20]. Here, this mask is set to be spectral information and the method given in [12] is used to generate an approximately circularly symmetric 2D Lagrange filter, L. This filter is used for the modification of the complex diffusion coefficient of the method given in [14].…”

Section: Pseudospectral Lagrange Maskmentioning

confidence: 99%

“…The first modification is a convolutional modification of the coefficient. For this modification, the Lagrange mask discussed in [20] is used to generate an approximately circularly symmetric 2D Lagrange filter using the method provided in [12]. The first modification is then convolving this filter with the coefficient.…”

Section: Introductionmentioning

confidence: 99%

Image enhancement through complex conduction coefficient modifications

et al. 2012

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One important term in the diffusion equation applied to image processing is the conduction coefficient. It could be a real or complex function, depending on the chosen diffusion processes. A modification in the form of the application of filter convolution to the real-value conduction coefficient has been recommended for the improvement of the signal-to-noise ratio and the speed by which the diffusion is performed. In this study, the developments of the approximately circularly symmetric two-dimensional Lagrange filter and the Teager operator are presented. They are proposed here for the modification of the complex-value conduction coefficient for the improvement of the image diffusion process. Performance of the proposed modifications is evaluated through simulation and the use of three metrics. Experimental results suggest the validity and applicability of the proposed modifications.

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The Method of Automatic Counting of Circular Objects Based on the Matching Method of Fuzzy Circular Template

2012

AMR

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Importing ideas of Geometry and Fuzzy into traditional matching method of template, generates the matching method of fuzzy circular template. The template of this method is data of a circle with fixed radius and width of line by fuzzy function, its matching is judging the extent of similarity of the object of matching bitmap and fuzzy circular of template. This method utilizes efficiently the shape of matching object and overcome disadvantageous influence of the factors of bitmap noise and unequal size and irregular shape of matching object.

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Lagrange Gradient Mask for Optical Image Processing

Cited by 3 publications

References 19 publications

Dense gradient-based features (DEGRAF) for computationally efficient and invariant feature extraction in real-time applications

Dense gradient-based features (DEGRAF) for computationally efficient and invariant feature extraction in real-time applications

Image enhancement through complex conduction coefficient modifications

The Method of Automatic Counting of Circular Objects Based on the Matching Method of Fuzzy Circular Template

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