Group Based Asymmetry–A Fast Saliency Algorithm

Sharma, Puneet; Eiksund, Oddmar

doi:10.1007/978-3-319-27857-5_80

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…To make the saliency region stand out, we modified the asymmetry model to calculate the hyperbolic characters from the uniform background. Alsam et al [13] proposed a saliency model that uses asymmetry as a measure of significance. To calculate saliency, the input image decomposes into square blocks, and at the same time, the definition of the D4 group of elements for each block is given.…”

Section: Saliency Transformationmentioning

confidence: 99%

Salient map of hyperbolas in GPR images

Fan

2018

J Image Video Proc.

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In ground-penetrating radar (GPR) applications, a salient map is useful for data analysis and building a consensus for data interpretation. In this paper, we present a novel method to map the salient region of a hyperbola using an asymmetry measurement and seam carving. First, on the basis of the magnitude and phase information of the Zernike moment and pixel asymmetry, we construct a new hyperbola descriptor to represent the region feature. Second, in order to make the hyperbolic region stand out, we improve the calculation of seam carving to extract the saliency map with the hyperbolic region. Compared with typical methods, our method provides significant and meaningful hyperbolic information, balancing the low-and high-level contrast of the branch of a hyperbola.

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Section: Saliency Transformationmentioning

confidence: 99%

Salient map of hyperbolas in GPR images

Fan

2018

J Image Video Proc.

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“…We investigate if the inherent properties of the complete set of elements pertaining to the D 4 group can form a natural basis for calculating a feature vector suitable for image discrimination. The D 4 group has shown promising results in various computer vision applications [12][13][14][15][16][17], which motivated us to use this group for our proposed algorithm.…”

Section: Introductionmentioning

confidence: 99%

Dihedral Group D4—A New Feature Extraction Algorithm

Sharma

2020

Symmetry

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In this paper, we propose a new feature descriptor for images that is based on the dihedral group D 4 , the symmetry group of the square. The group action of the D 4 elements on a square image region is used to create a vector space that forms the basis for the feature vector. For the evaluation, we employed the Error-Correcting Output Coding (ECOC) algorithm and tested our model with four diverse datasets. The results from the four databases used in this paper indicate that the feature vectors obtained from our proposed D 4 algorithm are comparable in performance to that of Histograms of Oriented Gradients (HOG) model. Furthermore, as the D 4 model encapsulates a complete set of orientations pertaining to the D 4 group, it enables its generalization to a wide range of image classification applications.

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Group Based Asymmetry–A Fast Saliency Algorithm

Cited by 2 publications

References 18 publications

Salient map of hyperbolas in GPR images

Salient map of hyperbolas in GPR images

Dihedral Group D4—A New Feature Extraction Algorithm

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