Improvements on human skin segmentation in digital images

2015 ◽  
Author(s):  
Anderson Carlos Sousa e Santos
Keyword(s):  
Human Skin ◽  
Digital Images ◽  
Skin Segmentation

scholarly journals Color-Based Feature Extraction Method for Skin Segmentation Based on SVM

2022 ◽  
Vol 10 (1) ◽  
pp. 66-71
Author(s):  
Harsha B. K.
Keyword(s):  
Feature Extraction ◽  
Human Skin ◽  
Extraction Method ◽  
Digital Images ◽  
Color Space ◽  
Support Vector ◽  
Color Spaces ◽  
Feature Extraction Method ◽  
Skin Segmentation ◽  
Performance Results

Abstract: Different colored digital images can be represented in a variety of color spaces. Red-Green-Blue is the most commonly used color space. That can be transformed into Luminance, Blue difference, Red difference. These color pixels' defined features provide strong information about whether they belong to human skin or not. A novel color-based feature extraction method is proposed in this paper, which makes use of both red, green, blue, luminance, hue, and saturation information. The proposed method is used on an image database that contains people of various ages, races, and genders. The obtained features are used to segment the human skin using the Support-Vector- Machine algorithm, and the promising performance results of 89.86% accuracy are then compared to the most commonly used methods in the literature. Keywords: Skin segmentation, SVM, feature extraction, digital images

Burnt Human Skin Segmentation and Depth Classification Using Deep Convolutional Neural Network (DCNN)

2020 ◽  
Vol 10 (10) ◽  
pp. 2421-2429
Author(s):  
Fakhri Alam Khan ◽  
Ateeq Ur Rehman Butt ◽  
Muhammad Asif ◽  
Hanan Aljuaid ◽  
Awais Adnan ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Human Skin ◽  
Low Income ◽  
Deep Convolutional Neural Network ◽  
Image Feature ◽  
World Health ◽  
Skin Segmentation ◽  
Average Accuracy ◽  
Research Task

World Health Organization (WHO) manage health-related statistics all around the world by taking the necessary measures. What could be better for health and what may be the leading causes of deaths, all these statistics are well organized by WHO. Burn Injuries are mostly viewed in middle and low-income countries due to lack of resources, the result may come in the form of deaths by serious injuries caused by burning. Due to the non-accessibility of specialists and burn surgeons, simple and basic health care units situated at tribble areas as well as in small cities are facing the problem to diagnose the burn depths accurately. The primary goals and objectives of this research task are to segment the burnt region of skin from the normal skin and to diagnose the burn depths as per the level of burn. The dataset contains the 600 images of burnt patients and has been taken in a real-time environment from the Allied Burn and Reconstructive Surgery Unit (ABRSU) Faisalabad, Pakistan. Burnt human skin segmentation was carried by the use of Otsu's method and the image feature vector was obtained by using statistical calculations such as mean and median. A classifier Deep Convolutional Neural Network based on deep learning was used to classify the burnt human skin as per the level of burn into different depths. Almost 60 percent of images have been taken to train the classifier and the rest of the 40 percent burnt skin images were used to estimate the average accuracy of the classifier. The average accuracy of the DCNN classifier was noted as 83.4 percent and these are the best results yet. By the obtained results of this research task, young physicians and practitioners may be able to diagnose the burn depths and start the proper medication.

A Hybrid Cascade Approach for Human Skin Segmentation

2016 ◽  
Vol 17 (6) ◽  
pp. 1-14 ◽  
Author(s):  
Samy Bakheet ◽  
Ayoub Al-Hamadi
Keyword(s):  
Human Skin ◽  
Skin Segmentation

Using graph cuts in GPUs for color based human skin segmentation

2011 ◽  
Vol 18 (1) ◽  
pp. 41-59 ◽  
Author(s):  
Lucas Lattari ◽  
Anselmo Montenegro ◽  
Aura Conci ◽  
Esteban Clua ◽  
Virginia Mota ◽  
...  
Keyword(s):  
Human Skin ◽  
Graph Cuts ◽  
Skin Segmentation

scholarly journals THE ANALYSIS OF IDENTIFYING THE DIGITAL IMAGES OF HUMAN SKIN / ŽMOGAUS ODOS SKAITMENINIŲ VAIZDŲ IDENTIFIKAVIMO ANALIZĖ / АНАЛИЗ ИДЕНТИФИКАЦИИ ЦИФРОВЫХ ИЗОБРАЖЕНИЙ КОЖИ ЧЕЛОВЕКА

2011 ◽  
Vol 37 (2) ◽  
pp. 63-68
Author(s):  
Jonas Skeivalas ◽  
Silvija Kontautienė ◽  
Skaidra Valiukevičienė
Keyword(s):  
Human Skin ◽  
Digital Images ◽  
Correlation Coefficients ◽  
Wavelet Theory ◽  
Random Functions ◽  
Covariance Functions ◽  
Scale Change ◽  
Calculation Procedures

The article identifies the digital images of human skin using covariance functions and Wavelet theory. The terms of covariance functions are defined using random functions made of scattering arrays of the pixels of digital images considering columns as single vectors. The analysed digital images can differ in scale because the scale of the image changes while the frequencies of the colour waves of single pixels remain the same. Thus, the influence of scale change on calculation procedures of covariance functions can be hardly noticed. RGB colour spectrum was used for identifying the images of human skin. The paper describes the influence of single components of RGB colour spectrum on the values of covariance functions of digital images. The identity of digital images is evaluated taking into account changes in the values of correlation coefficients in the range of RGB colours using appropriate software. Santrauka Nagrinėjamas žmogaus odos skaitmeninių vaizdų identifikavimas taikant kovariacinių funkcijų ir Wavelet bangų teoriją. Kovariacinių funkcijų išraiškos sudaromos pagal atsitiktines funkcijas, sudarytas skleidžiant skaitmeninių vaizdų pikselių masyvus pagal stulpelius pavienių vektorių pavidalu. Tyrimams naudojami skaitmeniniai vaizdai gali būti skirtingo mastelio, nes, kintant vaizdo masteliui, pavienių pikselių spalvų bangų dažniai lieka nekintantys, todėl kovariacinių funkcijų skaičiavimo procedūrose mastelio kaitos įtaka nepasireiškia. žmogaus odos vaizdams identifikuoti taikytas RGB formato spalvų spektras. Analizuota RGB spalvų tenzoriaus pavienių dedamųjų įtaka skaitmeninių vaizdų kovariacinių funkcijų įverčiams. Skaitmeninių vaizdų tapatumas įvertinamas pagal koreliacijos koeficientų reikšmių kaitą RGB spalvų diapazone, taikant sudarytas kompiuterines programas. Резюме С применением теории ковариационных функций и Wavelet (маленьких волн) рассматривается идентификация цифровых изображений кожи человека. Выражения ковариационных функций определяются по случайным функциям, составленным при развертывании массивов пикселей цифровых изображений по столбикам в виде отдельных векторов. Цифровые изображения могут быть разных масштабов, так как при изменении масштаба для изображений частота волн цветов отдельных пикселей остается неизменной. В связи с этим влияние изменений масштаба в вычислительных процедурах ковариационных функций не проявляется. При идентификации цифровых изображений кожи человека использован цветовой спектр для формата RGB. Проведен анализ влияния отдельных составляющих тензора RGB цветов цифровых изображений на оценку ковариационных функций. Идентичность цифровых изображений оценивается по изменению значений коэффициентов корреляции в диапазоне спектра RGB цветов при использовании компьютерных программ.

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