Fault Detection Using Attention Models Based on Visual Saliency

Shafiq, Muhammad; Long, Zhiling; Di, Haibin; Regib, Ghassan A; Deriche, Mohamed

doi:10.1109/icassp.2018.8461508

Cited by 5 publications

(1 citation statement)

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“…The convolution theorem [34] represents, that under acceptable conditions, the Fourier transform of a convolution of two signals is the pointwise product of their Fourier transforms. The utilization of FFT transform domain model not only capture energy variations within multi-dimensional data successfully [35] but are also computationally less expensive [36]. The coefficient of low complexity specifies contour of the salient object.…”

Section: Introductionmentioning

confidence: 99%

Active Contour Model Using Fast Fourier Transformation for Salient Object Detection

Khan

Zhang

2021

Electronics

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The active contour model is a comprehensive research technique used for salient object detection. Most active contour models of saliency detection are developed in the context of natural scenes, and their role with synthetic and medical images is not well investigated. Existing active contour models perform efficiently in many complexities but facing challenges on synthetic and medical images due to the limited time like, precise automatic fitted contour and expensive initialization computational cost. Our intention is detecting automatic boundary of the object without re-initialization which further in evolution drive to extract salient object. For this, we propose a simple novel derivative of a numerical solution scheme, using fast Fourier transformation (FFT) in active contour (Snake) differential equations that has two major enhancements, namely it completely avoids the approximation of expansive spatial derivatives finite differences, and the regularization scheme can be generally extended more. Second, FFT is significantly faster compared to the traditional solution in spatial domain. Finally, this model practiced Fourier-force function to fit curves naturally and extract salient objects from the background. Compared with the state-of-the-art methods, the proposed method achieves at least a 3% increase of accuracy on three diverse set of images. Moreover, it runs very fast, and the average running time of the proposed methods is about one twelfth of the baseline.

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Section: Introductionmentioning

confidence: 99%