Relevant bibliographies by topics / Skin color model

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Academic literature on the topic 'Skin color model'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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Journal articles on the topic "Skin color model"

Di, Hong Wei, and Wei Xu. "An Improved Adaptive Threshold Skin Color Model." Applied Mechanics and Materials 610 (August 2014): 358–61. http://dx.doi.org/10.4028/www.scientific.net/amm.610.358.

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To solve the problem that traditional threshold segmentation model is not very robust in skin segmentation under different skin colors and different illuminations, an improved adaptive skin color model is proposed. This model detects the change rate of the skin color pixels by modifying the certain threshold while fixing others, then selects the optimum threshold adaptively. The experimental results show that this algorithm can effectively distinguish skin color regions and background regions, and has strong robustness on light disturbance.

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KIPS, Robin, Loïc TRAN, Emmanuel MALHERBE, and Matthieu PERROT. "Beyond Color Correction : Skin Color Estimation In The Wild Through Deep Learning." Electronic Imaging 2020, no. 5 (2020): 82–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.5.maap-060.

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Estimating skin color from an uncontrolled facial image is a challenging task. Many factors such as illumination, camera and shading variations directly affect the appearance of skin color in the image. Furthermore, using a color calibration target in order to correct the image pixels leads to a complex user experience. We propose a skin color estimation method from images in the wild, taken with unknown camera, under an unknown lighting, and without a calibration target. While prior methods relied on explicit intermediate steps of color correction of image pixels and skin region segmentation,

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Dong, Xue Feng. "An Improved Skin Color Detection Algorithm Model." Advanced Materials Research 756-759 (September 2013): 3517–21. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3517.

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The main objective of designing skin color model is to determine whether the pixel is skin color pixels and generate the skin color mask images. The paper discusses the choise of color space and skin color model designing in skin color detection system, analysis the problems often needed to solve in it and put forward an improved skin color detection algorithm model based on ellipse boundary. The skin color detection experiment is completed. The result of experiment shows the skin color detection algorithm model is good.

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Park, Gyeong-Mi, and Young-Bong Kim. "Integrated 3D Skin Color Model for Robust Skin Color Detection of Various Races." Journal of the Korea Contents Association 9, no. 5 (2009): 1–12. http://dx.doi.org/10.5392/jkca.2009.9.5.001.

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Bergasa, L. M., M. Mazo, A. Gardel, M. A. Sotelo, and L. Boquete. "Unsupervised and adaptive Gaussian skin-color model." Image and Vision Computing 18, no. 12 (2000): 987–1003. http://dx.doi.org/10.1016/s0262-8856(00)00042-1.

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Liu, Hong Hai, and Xiang Hua Hou. "Research and Improvement on the Algorithm of Face Region Detection Based on Skin Color Model." Applied Mechanics and Materials 543-547 (March 2014): 2702–5. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2702.

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In face image with complex background, the CbCr skin color region will have offset when considering the illumination change. Therefore, the non-skin color pixels which luminance is less than 80 will be mistaken as skin color pixels and the skin color pixels which luminance is greater than 230 will be mistaken as non-skin color pixels. In order to reduce the misjudgments, an improved skin color model of nonlinear piecewise is put forward in this paper. Firstly, the skin color model of non-piecewise is analyzed and the experimental results show that by this model there is an obvious misjudgment

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Khairunnisa, Khairunnisa, Rismayanti Rismayanti, and Rully Alhari. "ANALISIS IDENTIFIKASI WAJAH MENGGUNAKAN GABOR FILTER DAN SKIN MODEL." JURNAL TEKNOLOGI INFORMASI 2, no. 2 (2019): 150. http://dx.doi.org/10.36294/jurti.v2i2.430.

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Abstract - Identification of faces in digital images is a complex process and requires a combination of various methods. The complexity of facial identification is increasing along with the increasing need for high accuracy of facial images. This research analyzes the combination of Skin Color Model and Gabor Filters in the process of identifying facial identities in digital images. The Skin Color Model method is used to separate the face area from facial images based on skin color values on facial images. The face area is then extracted using Gabor Filter. This research resulted in the highes

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HIREMATH, P. S., and AJIT DANTI. "DETECTION OF MULTIPLE FACES IN AN IMAGE USING SKIN COLOR INFORMATION AND LINES-OF-SEPARABILITY FACE MODEL." International Journal of Pattern Recognition and Artificial Intelligence 20, no. 01 (2006): 39–61. http://dx.doi.org/10.1142/s021800140600451x.

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In this paper, human faces are detected using the skin color information and the Lines-of-Separability (LS) face model. The various skin color spaces based on widely used color models such as RGB, HSV, YCbCr, YUV and YIQ are compared and an appropriate color model is selected for the purpose of skin color segmentation. The proposed approach of skin color segmentation is based on YCbCr color model and sigma control limits for variations in its color components. The segmentation by the proposed method is found to be more efficient in terms of speed and accuracy. Each of the skin segmented region

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Huang, Hui Ming, He Sheng Liu, and Guo Ping Liu. "Face Image Segmentation Using Color Information and Saliency Map." Applied Mechanics and Materials 55-57 (May 2011): 77–81. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.77.

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In this paper, we proposed an efficient method to address the problem of color face image segmentation that is based on color information and saliency map. This method consists of three stages. At first, skin colored regions is detected using a Bayesian model of the human skin color. Then, we get a chroma chart that shows likelihoods of skin colors. This chroma chart is further segmented into skin region that satisfy the homogeneity property of the human skin. The third stage, visual attention model are employed to localize the face region according to the saliency map while the bottom-up appr

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Hajiarbabi, Mohammadreza, and Arvin Agah. "Human Skin Detection in Color Images Using Deep Learning." International Journal of Computer Vision and Image Processing 5, no. 2 (2015): 1–13. http://dx.doi.org/10.4018/ijcvip.2015070101.

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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 enha

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Dissertations / Theses on the topic "Skin color model"

Faria, Rodrigo Augusto Dias. "Human skin segmentation using correlation rules on dynamic color clustering." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-01102018-101814/.

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Human skin is made of a stack of different layers, each of which reflects a portion of impinging light, after absorbing a certain amount of it by the pigments which lie in the layer. The main pigments responsible for skin color origins are melanin and hemoglobin. Skin segmentation plays an important role in a wide range of image processing and computer vision applications. In short, there are three major approaches for skin segmentation: rule-based, machine learning and hybrid. They differ in terms of accuracy and computational efficiency. Generally, machine learning and hybrid approaches outp

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Hall, Alicia V. "Body image as a function of colorism [electronic resource] : testing a theoretical model / by Alicia V. Hall." [Tampa, Fla.] : University of South Florida, 2003. http://purl.fcla.edu/fcla/etd/SFE0000111.

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Samadzadegan, Sepideh. "Automatic and Adaptive Red Eye Detection and Removal : Investigation and Implementation." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-77977.

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Redeye artifact is the most prevalent problem in the flash photography, especially using compact cameras with built-in flash, which bothers both amateur and professional photographers. Hence, removing the affected redeye pixels has become an important skill. This thesis work presents a completely automatic approach for the purpose of redeye detection and removal and it consists of two modules: detection and correction of the redeye pixels in an individual eye, detection of two red eyes in an individual face.This approach is considered as a combination of some of the previous attempts in the ar

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Feitosa, Rafael Divino Ferreira. "Modelos matemáticos para redução do espectro provável e detecção de tons de pele humana em imagens coloridas representadas nos espaços de cores RGB e HSV." Universidade Federal de Goiás, 2015. http://repositorio.bc.ufg.br/tede/handle/tede/4756.

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Submitted by Erika Demachki (erikademachki@gmail.com) on 2015-10-23T18:23:32Z No. of bitstreams: 2 Dissertação - Rafael Divino Ferreira Feitosa - 2015.pdf: 7893703 bytes, checksum: 12af470c3ca2fb4a3d0bd3885bfde46d (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Approved for entry into archive by Erika Demachki (erikademachki@gmail.com) on 2015-10-23T18:25:45Z (GMT) No. of bitstreams: 2 Dissertação - Rafael Divino Ferreira Feitosa - 2015.pdf: 7893703 bytes, checksum: 12af470c3ca2fb4a3d0bd3885bfde46d (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6

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Scott, Dario. "Identificação de atividade de voz baseada em vídeo." Universidade do Vale do Rio do Sinos, 2010. http://www.repositorio.jesuita.org.br/handle/UNISINOS/2285.

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Made available in DSpace on 2015-03-05T14:01:22Z (GMT). No. of bitstreams: 0 Previous issue date: 30 Hewlett-Packard Brasil Ltda Atualmente, existem diversos trabalhos com as mais variadas abordagens relativas ao processamento de imagens digitais para detecção de atividade de voz (VAD). As suas aplicações perpassam diferentes áreas, como por exemplo, comandos de voz em veículos e videoconferência. A motivação deste trabalho constitui-se na construção de um algoritmo que contribua para o aperfeiçoamento das técnicas de processamento de imagens aplicadas para a detecção de atividade de

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Kolman, Aleš. "Detekce obličejů ve videu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236583.

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The project is focused on face detection in video. Firstly, it contains a summary of basic color models. Secondly, you can find the description and comparison of the basic methods for detection of human skin with a practical example of implementation of parametric detector. Thirdly, a theoretical basis for face detection and face tracking in a video containing a list of basic concepts and methods of this issue follows. Greater emphasis is placed on the description of machine learning algorithm AdaBoost and description of the possible application of the Kalman filter for the purpose of face tra

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Ribeiro, Hebert Luchetti. "Reconhecimento de gestos usando segmentação de imagens dinâmicas de mãos baseada no modelo de mistura de gaussianas e cor de pele." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-27112006-132158/.

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O objetivo deste trabalho é criar uma metodologia capaz de reconhecer gestos de mãos, a partir de imagens dinâmicas, para interagir com sistemas. Após a captação da imagem, a segmentação ocorre nos pixels pertencentes às mãos que são separados do fundo pela segmentação pela subtração do fundo e filtragem de cor de pele. O algoritmo de reconhecimento é baseado somente em contornos, possibilitando velocidade para se trabalhar em tempo real. A maior área da imagem segmentada é considerada como região da mão. As regiões detectadas são analisadas para determinar a posição e a orientação da mão. A p

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Chang, Che-Yu, and 張哲豫. "Semi-Automatic Skin Color Model Correction." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/74745746512891794969.

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碩士 國立臺灣海洋大學 資訊工程學系 104 In this thesis, a novel semi-automatic skin model correction method is presented. The method improves the accuracy of skin segmentation under different lighting conditions. We use color temperature as light color, and use the complexion of user's hand as sample to estimate the color temperature of environment. Then use the color temperature of environment to correct skin color model. We also provided some testing results to proof our method improving the accuracy of skin segmentation, and to proof our method better than full-automatic methods.

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Chang, Cheng-Wen, and 張正文. "An Adaptive Skin Color Detection Model." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/85kh2s.

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碩士 東吳大學 資訊科學系 93 As the accuracy of skin colour detection is of utmost significance, skin colour detection has become one of popular tools widely applied for image detection. Since skin colour may vary under changing lighting conditions, fixed models cannot increase efficiently the accuracy of skin colour detection. Thus, it is important to study a skin colour model that offers adaptability to changing lighting conditions and self-retrieval. Firstly, this paper intends to set up skin colour probability function by way of Gaussian Model, and then study the movement track of skin col

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Po-HsiangPeng and 彭柏翔. "An intelligent model for facial skin color detection." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/3582nk.

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Books on the topic "Skin color model"

Passeport beauté. Éditions Modus Vivendi, 1997.

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The Beauty of Color: The Ultimate Beauty Guide for Skin of Color. Perigee Trade, 2006.

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Iman. The Beauty of Color: The Ultimate Beauty Guide for Skin of Color. Putnam Adult, 2005.

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Book chapters on the topic "Skin color model"

Yang, Ming-Hsuan, and Narendra Ahuja. "Skin Color Model." In Face Detection and Gesture Recognition for Human-Computer Interaction. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1423-7_4.

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Subban, Ravi, and Richa Mishra. "Human Skin Segmentation in Color Images Using Gaussian Color Model." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-01778-5_2.

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Min, Kyongpil, Junchul Chun, and Goorack Prak. "A Nonparametric Skin Color Model for Face Detection from Color Images." In Parallel and Distributed Computing: Applications and Technologies. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30501-9_27.

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Wei, Wei, Tianyong Li, Jinfu Wei, Detian Zen, and Weimin Ning. "Application of Skin Color Model in Image Segmentation." In IFIP Advances in Information and Communication Technology. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00828-4_40.

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Subban, Ravi, and Richa Mishra. "Face Detection in Color Images Based on Explicitly-Defined Skin Color Model." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36321-4_54.

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Jairath, Shrey, Samarth Bharadwaj, Mayank Vatsa, and Richa Singh. "Adaptive Skin Color Model to Improve Video Face Detection." In Machine Intelligence and Signal Processing. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2625-3_12.

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Hiremath, P. S., and Manjunath Hiremath. "Fuzzy Geometric Face Model for Face Detection Based on Skin Color Fusion Model." In Advances in Intelligent Systems and Computing. Springer India, 2013. http://dx.doi.org/10.1007/978-81-322-0740-5_118.

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Fan, Zhi-Gang, and Bao-Liang Lu. "An Adjusted Gaussian Skin-Color Model Based on Principal Component Analysis." In Advances in Neural Networks – ISNN 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28647-9_132.

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Wang, Dong, Jinchang Ren, Jianmin Jiang, and Stan S. Ipson. "Skin Detection from Different Color Spaces for Model-Based Face Detection." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85930-7_62.

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Kim, Jong-Bae, Chae-Hyun Moon, and Hang-Joon Kim. "Efficient Face Extraction Using Skin-Color Model and a Neural Network." In Intelligent Data Engineering and Automated Learning — IDEAL 2002. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45675-9_81.

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Conference papers on the topic "Skin color model"

Ueng, Shyh-Kuang, and Che-Yu Chang. "An improved skin color model." In 2016 International Conference on Applied System Innovation (ICASI). IEEE, 2016. http://dx.doi.org/10.1109/icasi.2016.7539812.

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Won, Woong Jae, Minho Lee, and Joonwoo Son. "Skin color saliency map model." In 2009 6th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE, 2009. http://dx.doi.org/10.1109/ecticon.2009.5137225.

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Chandrappa, D. N., M. Ravishankar, and D. R. RameshBabu. "Face detection in color images using skin color model algorithm based on skin color information." In 2011 3rd International Conference on Electronics Computer Technology (ICECT). IEEE, 2011. http://dx.doi.org/10.1109/icectech.2011.5941600.

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Yang, Guoliang, Huan Li, Li Zhang, and Yue Cao. "Research on a Skin Color Detection Algorithm Based on Self-adaptive Skin Color Model." In 2010 International Conference on Communications and Intelligence Information Security (ICCIIS). IEEE, 2010. http://dx.doi.org/10.1109/icciis.2010.67.

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Dawod, Ahmad Yahya, Junaidi Abdullah, and Md Jahangir Alam. "Adaptive skin color model for hand segmentation." In 2010 International Conference on Computer Applications and Industrial Electronics (ICCAIE). IEEE, 2010. http://dx.doi.org/10.1109/iccaie.2010.5735129.

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Chen Zhipeng, Hu Junda, and Zou Wenbin. "Face detection system based on skin color model." In 2010 2nd International Conference on Networking and Digital Society (ICNDS). IEEE, 2010. http://dx.doi.org/10.1109/icnds.2010.5479392.

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Hsieh, Chen-Chiung, Dung-Hua Liou, and Meng-Kai Jiang. "Fast enhanced face-based adaptive skin color model." In International Conference on Image Processing and Pattern Recognition in Industrial Engineering, edited by Zhengyu Du and Bin Liu. SPIE, 2010. http://dx.doi.org/10.1117/12.866918.

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Wang, Yali, and Mingjie Li. "A Fast detection network for skin color model." In 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP). IEEE, 2021. http://dx.doi.org/10.1109/icsp51882.2021.9408672.

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Yang, Guoliang, Jinxia Ren, Chaozhi Huang, and Huan Li. "Research on a skin color detection algorithm based on hybrid color model." In 2010 3rd International Congress on Image and Signal Processing (CISP). IEEE, 2010. http://dx.doi.org/10.1109/cisp.2010.5648205.

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Pai, Yu-Ting, Li-Te Lee, Shanq-Jang Ruan, Yen-Hsiang Chen, Saraju P. Mohanty, and Elias Kougianos. "Honeycomb Model Based Skin Color Detector for Face Detection." In 2008 15th International Conference on Mechatronics and Machine Vision in Practice (M2VIP). IEEE, 2008. http://dx.doi.org/10.1109/mmvip.2008.4749499.

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