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In this work, an optimized compound receptive field modulation method is proposed to extract salient contour from complex natural scene in low-light-level (LLL) and infrared images.
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This kind of filters extracts salient elongated contours of the input image.
In this paper, we propose a fast and effective polarity-based active contour for salient object detection in grey-level images and color images.
Although many variations have been proposed until recently, it is remarkable that in most cases, the projection (voting process) is sparsely performed on contour or salient points of the image space.
We have proposed a fast and efficient active contour model for salient object detection in grey-level images and color images.
As interpolated contours become more salient, the modularity features emerge.
b Contour evolves based on salient image features.
Despite the variety of tracking means, the kernel of tracking is almost the same, namely the recognition of salient graphical clues such as lines, contours, points and colors.
If the block (b_n) belongs to a salient region and its number of contours is significant (as in Eq. 9), optical flow-based motion estimation is implemented; otherwise, we use block-based estimation.
By combining the polarity information with the active contour model of [17], the salient objects can be easily detected.
The system works as a contour identifier able to extract the salient features of an image.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com