scholarly journals A review of human skin detection applications based on image processing

2021 ◽  
Vol 10 (1) ◽  
pp. 129-137
Author(s):  
Hussein Ali Hussein Al Naffakh ◽  
Rozaida Ghazali ◽  
Nidhal Khdhair El Abbadi ◽  
Ali Nadhim Razzaq
Keyword(s):  
Image Processing ◽  
Human Skin ◽  
Human Life ◽  
Research Topic ◽  
Skin Detection ◽  
Virtual Image ◽  
New Research ◽  
Human Skin Models ◽  
Tracking And Recognition ◽  
Detection And Recognition

In computer science, virtual image processing is the use of a digital computer to manipulate digital images through an algorithm for many applications. To begin with a new research topic, the must trend application that gets many requests to develop should know. Therefore, many applications based on human skin and human life are reviewed in this article, such as detection, classification, blocking, cryptography, identification, localization, steganography, segmentation, tracking, and recognition. In this article, the published articles with the topic of human skin-based image processing are investigated. The international publishers, such as Springer, IEEE, arXiv, and Elsevier are selected. The searching is implemented with the duration criteria of 2015-2019. It noted that human skin detection and recognition are the most repetitive articles with 43% and 28.5%, respectively of the total number of the investigated articles. The usage of human skin models is being widely used in the image processing of various applications.

scholarly journals PKT: fast color-based spatial model for human skin detection

2021 ◽  
Author(s):  
Grace L. Samson ◽  
Joan Lu
Keyword(s):  
Spatial Structure ◽  
Human Skin ◽  
Skin Color ◽  
Skin Detection ◽  
Quad Tree ◽  
Learning Tasks ◽  
Color Detection ◽  
Speed Up ◽  
Image Pixels ◽  
Speed Accuracy

AbstractWe present a new detection method for color-based object detection, which can improve the performance of learning procedures in terms of speed, accuracy, and efficiency, using spatial inference, and algorithm. We applied the model to human skin detection from an image; however, the method can also work for other machine learning tasks involving image pixels. We propose (1) an improved RGB/HSL human skin color threshold to tackle darker human skin color detection problem. (2), we also present a new rule-based fast algorithm (packed k-dimensional tree --- PKT) that depends on an improved spatial structure for human skin/face detection from colored 2D images. We also implemented a novel packed quad-tree (PQT) to speed up the quad-tree performance in terms of indexing. We compared the proposed system to traditional pixel-by-pixel (PBP)/pixel-wise (PW) operation, and quadtree based procedures. The results show that our proposed spatial structure performs better (with a very low false hit rate, very high precision, and accuracy rate) than most state-of-the-art models.

scholarly journals Evaluation of in vitro human skin models for studying effects of external stressors and stimuli and developing treatment modalities

View ◽  
2021 ◽  
pp. 20210012
Author(s):  
Emily Sutterby ◽  
Peter Thurgood ◽  
Sara Baratchi ◽  
Khashayar Khoshmanesh ◽  
Elena Pirogova
Keyword(s):  
Human Skin ◽  
Treatment Modalities ◽  
Human Skin Models

scholarly journals Improved epidermal barrier formation in human skin models by chitosan modulated dermal matrices

PLoS ONE ◽  
2017 ◽  
Vol 12 (3) ◽  
pp. e0174478 ◽  
Author(s):  
Arnout Mieremet ◽  
Marion Rietveld ◽  
Samira Absalah ◽  
Jeroen van Smeden ◽  
Joke A. Bouwstra ◽  
...  
Keyword(s):  
Human Skin ◽  
Epidermal Barrier ◽  
Barrier Formation ◽  
Dermal Matrices ◽  
Human Skin Models

Human Skin Detection in Color Images Using Deep Learning

2015 ◽  
Vol 5 (2) ◽  
pp. 1-13 ◽  
Author(s):  
Mohammadreza Hajiarbabi ◽  
Arvin Agah
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Human Skin ◽  
Skin Color ◽  
Color Image ◽  
Gaussian Model ◽  
Color Images ◽  
Skin Detection ◽  
Learning Methods ◽  
Rule Based

Human skin detection is an important and challenging problem in computer vision. Skin detection can be used as the first phase in face detection when using color images. The differences in illumination and ranges of skin colors have made skin detection a challenging task. Gaussian model, rule based methods, and artificial neural networks are methods that have been used for human skin color detection. Deep learning methods are new techniques in learning that have shown improved classification power compared to neural networks. In this paper the authors use deep learning methods in order to enhance the capabilities of skin detection algorithms. Several experiments have been performed using auto encoders and different color spaces. The proposed technique is evaluated compare with other available methods in this domain using two color image databases. The results show that skin detection utilizing deep learning has better results compared to other methods such as rule-based, Gaussian model and feed forward neural network.

scholarly journals Spinal cord segmentation and classification of degenerative disease

2019 ◽  
Vol 10 (3) ◽  
pp. 2426-2432 ◽  
Author(s):  
Arjun ◽  
Kanchana V
Keyword(s):  
Image Processing ◽  
Spinal Cord ◽  
Human Life ◽  
Semantic Segmentation ◽  
Processing Technique ◽  
Degenerative Disease ◽  
Image Processing Technique ◽  
Segmentation Approach ◽  
Work First ◽  
Mri Image

spinal cord plays an important role in human life. In our work, we are using digital image processing technique, the interior part of the human body can be analyzed using MRI, CT and X-RAY etc. Medical image processing technique is extensively used in medical field. In here we are using MRI image to perform our work In our proposed work, we are finding degenerative disease from spinal cord image. In our work first, we are preprocessing the MRI image and locate the degenerative part of the spinal cord, finding the degenerative part using various segmentation approach after that classifying degenerative disease or normal spinal cord using various classification algorithm. For segmentation, we are using an efficient semantic segmentation approach

scholarly journals Interaction between SARS-CoV-2 spike glycoprotein and human skin models: a molecular dynamics study

2021 ◽  
Author(s):  
Marc Domingo ◽  
Jordi Faraudo
Keyword(s):  
Stratum Corneum ◽  
Human Skin ◽  
Skin Surface ◽  
Viral Envelope ◽  
Solid Surfaces ◽  
Spike Glycoprotein ◽  
Indirect Transmission ◽  
Physico Chemical ◽  
Human Skin Models ◽  
Structure Orientation

The possibility of contamination of human skin by infectious virions plays an important role in indirect transmission of respiratory viruses but little is known about the fundamental physico-chemical aspects of the virus-skin interactions. In the case of coronaviruses, the interaction with surfaces (including the skin surface) is mediated by their large glycoprotein spikes that protrude from (and cover) the viral envelope. Here, we perform all atomic simulations between the SARS-CoV-2 spike glycoprotein and human skin models. We consider an "oily" skin covered by sebum and a "clean" skin exposing the stratum corneum. The simulations show that the spike tries to maximize the contacts with stratum corneum lipids, particularly ceramides, with substantial hydrogen bonding. In the case of "oily" skin, the spike is able to retain its structure, orientation and hydration over sebum with little interaction with sebum components. Comparison of these results with our previous simulations of the interaction of SARS-CoV-2 spike with hydrophilic and hydrophobic solid surfaces, suggests that the"soft" or "hard" nature of the surface plays an essential role in the interaction of the spike protein with materials.

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