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Topics
Hair Removal Methods (opens in a new tab)Hair Detection (opens in a new tab)Hair Removal (opens in a new tab)Hair Removal Algorithm (opens in a new tab)Hair Pixels (opens in a new tab)DullRazor (opens in a new tab)Independent Histogram Pursuit (opens in a new tab)Hair Mask (opens in a new tab)Dermoscopic Gel (opens in a new tab)Inpainting (opens in a new tab)
155 Citations
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Computer Science, Medicine
2013 35th Annual International Conference of the…
The preliminary results indicated the proposed method was able to remove more fine hairs and hairs in the shade, and lower false hair detection rate by 58% as compared to the DullRazor's approach.
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Computer Science, Medicine
Computer Methods and Programs in Biomedical…
A new algorithm for hair removal in dermoscopy images that includes two main stages: hair detection and inpainting is proposed that reports a true positive rate (sensitivity) and a true negative rate (specificity) of 95.75%.
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Medicine
Comput. Biol. Medicine
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Medicine, Computer Science
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This paper presents, for the first time, a hair detection and removal algorithm for 2D psoriasis images that overcomes the problem of removing skin hair without affecting the intensity or texture features of the lesions.
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Medicine, Computer Science
Multimedia Tools and Applications
The proposed algorithm performs well in removing hairs compared to previous studies and improves classification performance and the integration of the suggested algorithm has led to improvements in the performance metrics of the AlexNet architecture.
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- 2021
Computer Science, Medicine
IEEE Access
This work presents a new approach for the task of hair removal on dermoscopic images based on deep learning techniques that relies on an encoder-decoder architecture, with convolutional neural networks, for the detection and posterior restoration of hair’s pixels from the images.
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CGI
A straightforward approach to automatic hair and consequently noise removal is proposed, which starts with a median filter on each color space of RGB, a bottom hat filter, a binary conversion, a dilation and morphological opening, and then the removal of small connected pixels.
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Statistical and quantitative analyses prove the reliability of the algorithm for incorporation in CAD systems and its role in providing robust and flexible segmentation.
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