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Structuring element

About: Structuring element is a research topic. Over the lifetime, 997 publications have been published within this topic receiving 26839 citations.


Papers
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Journal ArticleDOI
TL;DR: A non-trivial extension of a deterministic approach originally detecting erosion and dilation of binary images, which operates on grayscale images and is robust to image compression and other typical attacks.
Abstract: Mathematical morphology provides a large set of powerful non-linear image operators, widely used for feature extraction, noise removal or image enhancement. Although morphological filters might be used to remove artifacts produced by image manipulations, both on binary and gray level documents, little effort has been spent towards their forensic identification. In this paper we propose a non-trivial extension of a deterministic approach originally detecting erosion and dilation of binary images. The proposed approach operates on grayscale images and is robust to image compression and other typical attacks. When the image is attacked the method looses its deterministic nature and uses a properly trained SVM classifier, using the original detector as a feature extractor. Extensive tests demonstrate that the proposed method guarantees very high accuracy in filtering detection, providing 100% accuracy in discriminating the presence and the type of morphological filter in raw images of three different datasets. The achieved accuracy is also good after JPEG compression, equal or above 76.8% on all datasets for quality factors above 80. The proposed approach is also able to determine the adopted structuring element for moderate compression factors. Finally, it is robust against noise addition and it can distinguish morphological filter from other filters.

22 citations

Proceedings ArticleDOI
01 Oct 2006
TL;DR: By analyzing basic operations of mathematical morphology (MM) digital filter and the selection of a structuring element (SE), an adaptive generalized morphological filter is presented to fast suppress noise as discussed by the authors.
Abstract: By analyzing basic operations of mathematical morphology (MM) digital filter and the selection of a structuring element (SE), an adaptive generalized morphological filter is presented to fast suppress noise. This new algorithm of the filter has more advantages of simplicity, convenience and flexibility than the conventional ways and has a brighter future in practical application. The filter is cascaded by basic morphological transforms and their combination forms with the same weight value and applied in processing on-line monitoring power system signatures to guarantee the accuracy of further fault diagnosis by higher signal-to-noise. Results of simulation show that the adaptive generalized morphological filter can suppress different kinds of white noises and pulse noises, restore the general regularity of the data effectively, and has better performance compared with single operation of morphological filter and other filtering approaches.

22 citations

01 Jan 2018
TL;DR: The proposed Focal Dice Loss (FDL) considers the imbalance among structures of interest instead of the entire image including background, which achieves the state-of-the-art in Dice Coefficient on average with relatively low computational cost.
Abstract: For accurate tumor segmentation in brain magnetic resonance (MR) images, the extreme class imbalance not only exists between the foreground and background, but among different sub-regions of tumor. Inspired by the focal loss [3] that down-weights the well-segmented classes, our proposed Focal Dice Loss (FDL) considers the imbalance among structures of interest instead of the entire image including background. Image dilation is applied to the training samples, which enlarges the tiny sub-regions, bridges the disconnected pieces of tumor structures and promotes understanding on overall tumor rather than complex details. The structuring element for dilation is gradually downsized, resulting in a coarse-to-fine and incremental learning process with the structure of network unchanged. Our experiments on the BRATS2015 dataset achieves the state-of-the-art in Dice Coefficient on average with relatively low computational cost.

22 citations

Journal ArticleDOI
TL;DR: An improved technique using genetic algorithms to decompose arbitrarily shaped binary structuring elements is presented, which can generate the solution in less computational costs, and is suited for parallel implementation.

21 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20236
202214
202112
202019
201929
201824