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Articles de revues sur le sujet « Skin color model »

Auteur : Grafiati

Publié le 4 juin 2021

Mis à jour le 6 février 2022

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1

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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Tang, San. "Human Face Detection Method Based on Skin Color Model." Advanced Materials Research 706-708 (June 2013): 1877–81. http://dx.doi.org/10.4028/www.scientific.net/amr.706-708.1877.

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Face detection is the first step of face recognition, and is a very active research topic in the filed of computer vision and pattern recognition. A skin color model based face detection method for chromatic images is proposed in this paper. The H-CgCr skin color model is established by taking advantage of the color pixels clustering distribution in the HSV and YCgCr color space. The noises are eliminated based on skin color segmentation, and the face candidate region is judged according to knowledge-based, finally, the position of the face area is marked by the box. The experimental results d

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Zhou, Dong Ming, and Hong Cai. "Face Detection Method Using PCNN and Skin Color Model." Advanced Materials Research 562-564 (August 2012): 1377–81. http://dx.doi.org/10.4028/www.scientific.net/amr.562-564.1377.

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This paper presented a face detection method for the color image using pulse coupled neural network (PCNN) and skin color model. The color image which is processed well through light compensation is converted from RGB to YCbCr color space, then the skin area are divided into sub-block, and skin color segmentation is made for the image in YCbCr space. Finally, we use PCNN to extract all sub-block ignition time sequence, and calculate various sub-block difference degrees between target face and the tested image, if the difference degree is the smallest, then the target face himself is the same p

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Takiwaki, Hirotsugu, Yoshiyuki Kanno, Yuki Miyaoka, and Seiji Arase. "Computer simulation of skin color based on a multilayered skin model." Skin Research and Technology 3, no. 1 (1997): 36–41. http://dx.doi.org/10.1111/j.1600-0846.1997.tb00157.x.

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Tsumura, Norimichi, Daisuke Kawazoe, Toshiya Nakaguchi, Nobutoshi Ojima, and Yoichi Miyake. "Regression-based model of skin diffuse reflectance for skin color analysis." Optical Review 15, no. 6 (2008): 292–94. http://dx.doi.org/10.1007/s10043-008-0047-3.

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JIANG, Guo-lai, and Yao-rong LIN. "Skin color segmentation algorithm combining adaptive model and fixed model." Journal of Computer Applications 30, no. 10 (2010): 2698–701. http://dx.doi.org/10.3724/sp.j.1087.2010.02698.

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Ueng, Shyh-Kuang, and Che-Yu Chang. "Skin color model adaptation under varying lighting conditions." Advances in Mechanical Engineering 8, no. 9 (2016): 168781401666899. http://dx.doi.org/10.1177/1687814016668999.

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Lei, Shi. "A Face Detection Algorithm in Color Image Based on Skin Color Segmentation." Advanced Materials Research 811 (September 2013): 417–21. http://dx.doi.org/10.4028/www.scientific.net/amr.811.417.

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Aiming at color images under complex background, this paper put forward a face detection algorithm based on skin color segmentation, combining the geometric characteristics. The skin region can be obtained by using skin color model and OTSU method to automatically optimize threshold segmentation image. By analyzing the characteristics of skin color region, the face position is determined by criterion of ellipse area.

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Park, Sung-Wook, and Jong-Wook Park. "Skin Region Extraction Using Multi-Layer Neural Network and Skin-Color Model." Journal of the Korea Industrial Information Systems Research 16, no. 2 (2011): 31–38. http://dx.doi.org/10.9723/jksiis.2011.16.2.031.

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Jia, Xi Bin, and Lu Yi Li. "Face Detection Based on Statistical Color Model and Haar Classifier." Advanced Materials Research 532-533 (June 2012): 634–38. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.634.

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The paper realizes the face detection algorithm based on the combination of the skin model and the Haar algorithm. Firstly, a platform for sample labeling was constructed, which combines the contour extraction algorithm with manual labeling. By labeling more than 10000 images obtained randomly from the Internet, a large training dataset is available. Then, a skin histogram, a non-skin histogram and a statistical skin model are constructed by analyzing the distribution of the skin and the non-skin color on the basis of a large training dataset. Based on this statistical color model, the skin ar

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PAUL, PADMA POLASH, MD MARUF MONWAR, MARINA L. GAVRILOVA, and PATRICK S. P. WANG. "ROTATION INVARIANT MULTIVIEW FACE DETECTION USING SKIN COLOR REGRESSIVE MODEL AND SUPPORT VECTOR REGRESSION." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 08 (2010): 1261–80. http://dx.doi.org/10.1142/s0218001410008391.

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In this paper, an automatic rotation invariant multiview face detection method, which utilizes modified Skin Color Model (SCM), is presented. First, Gaussian Mixture Model (GMM) and Support Vector Machine (SVM) based hybrid models are used to classify human skin regions from color images. The novelty of the adaptive hybrid model is its ability to predict the chromatic skin color band for individual images based on calibration differences of camera and luminance condition of environment. Classified skin regions are then converted to gray scale image with a threshold based on the predicted chrom

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Hajiarbabi, Mohammadreza, and Arvin Agah. "Human Skin Color Detection Using Neural Networks." Journal of Intelligent Systems 24, no. 4 (2015): 425–36. http://dx.doi.org/10.1515/jisys-2014-0098.

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AbstractHuman skin detection is an essential phase in face detection and face recognition when using color images. Skin detection is very challenging because of the differences in illumination, differences in photos taken using an assortment of cameras with their own characteristics, range of skin colors due to different ethnicities, and other variations. Numerous methods have been used for human skin color detection, including the Gaussian model, rule-based methods, and artificial neural networks. In this article, we introduce a novel technique of using the neural network to enhance the capab

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Han, Yan Bin, and Gang Song. "Skin Color Protection Based on Wide Gamut Display." Advanced Materials Research 926-930 (May 2014): 3559–62. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3559.

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Gamut extension algorithm transforms a picture to display in wide gamut, exerting a good performance. However, human skin color belongs to static color, if extended, it would look unnatural and affect the beauty of the image. So we presents an algorithm for protecting skin color during color gamut extension. Firstly, we initially identify skin color regions. Then, according to probabilistic model, a new method that we use it to avoid looking hair as face skin with similar color. Experimental results demonstrate that our proposed method does protect skin color and improve performance.

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Osman, Mohd Zamri, Mohd Aizaini Maarof, Mohd Foad Rohani, Nilam Nur Amir Sjarif, and Nor Saradatul Akmar Zulkifli. "A multi-color based features from facial images for automatic ethnicity identification model." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 3 (2020): 1383. http://dx.doi.org/10.11591/ijeecs.v18.i3.pp1383-1390.

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<span style="font-size: 9pt; font-family: 'Times New Roman', serif;">Ethnicity identification for demographic information has been studied for soft biometric analysis, and it is essential for human identification and verification. Ethnicity identification remains popular and receives attention in a recent year especially in automatic demographic information. Unfortunately, ethnicity identification technique using color-based feature mostly failed to determine the ethnicity classes accurately due to low properties of features in color-based. Thus, this paper purposely analyses the accurac

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Zhao, Jing Ying, Xiao Dong Duan, and Hai Guo. "Design and Implementation of a Face Location and Five Sense Organs Marking Software." Advanced Materials Research 831 (December 2013): 490–94. http://dx.doi.org/10.4028/www.scientific.net/amr.831.490.

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Face recognition technology is a significant branch of the study of artificial intelligence, the recognition precision is easily affected by facial expressions, skin colors, beam angles in the images and apparels. This essay tests human face images in the format of 24 BMP and realizes face location and mark of five sense organs. Firstly, color space model is adopted to set up skin color distribution model to segment skin regions; secondly, the obtained regions are judged and screened preliminarily, and optimized based on the characteristics of segmented regions with region optimization algorit

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Chen, Fujunku, Zhigang Hu, Keqin Li, and Wei Liu. "A Hybrid Skin Detection Model from Multiple Color Spaces Based on a Dual-Threshold Bayesian Algorithm." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 07 (2016): 1655018. http://dx.doi.org/10.1142/s0218001416550181.

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As a preliminary step of many applications, skin detection serves as an irreplaceable role in image processing applications, such as face recognition, gesture recognition, web image filtering, and image retrieval systems. Combining information from multiple color spaces improves the recognition rate and reduces the error rate because the same color is represented differently in other color spaces. Consequently, a hybrid skin detection model from multiple color spaces based on a dual-threshold Bayesian algorithm (DTBA) has been proposed. In each color space, the pixels of images are divided int

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Galván, Ismael, Juan Garrido-Fernández, José Ríos, Antonio Pérez-Gálvez, Bernal Rodríguez-Herrera, and Juan José Negro. "Tropical bat as mammalian model for skin carotenoid metabolism." Proceedings of the National Academy of Sciences 113, no. 39 (2016): 10932–37. http://dx.doi.org/10.1073/pnas.1609724113.

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Animals cannot synthesize carotenoid pigments de novo, and must consume them in their diet. Most mammals, including humans, are indiscriminate accumulators of carotenoids but inefficiently distribute them to some tissues and organs, such as skin. This limits the potential capacity of these organisms to benefit from the antioxidant and immunostimulatory functions that carotenoids fulfill. Indeed, to date, no mammal has been known to have evolved physiological mechanisms to incorporate and deposit carotenoids in the skin or hair, and mammals have therefore been assumed to rely entirely on other

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Liang, Yun Juan, Xiao Ying Wu, Li Juan Ma, and Li Jun Zhang. "Face Localization in Color Images Based on Skin Color and Eye Gradient." Advanced Materials Research 268-270 (July 2011): 1382–85. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.1382.

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In color images, skin color is the important information on human face. This paper proposes a method to detect and locate human face rapidly based on skin color information and eye gradient. First, normalized RGB space is converted to HSV space; Secondly, the images are pretreated by smoothing and light compensation to overcome the uneven illumination changes, and then the defined skin color model is used to determine candidate regions of the human face, finally the human face is located accurately through eye localization based on gradient template. Experiments show that the method is fast an

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Osuna-Garcia, Jorge A., Jesús Daniel Olivares-Figueroa, Peter M. A. Toivonen, Ma Hilda Pérez-Barraza, Ricardo Goenaga, and María J. Graciano-Cristóbal. "Novel Nondestructive Technique to Determine Optimum Harvesting Stage of ‘Ataúlfo’ Mango Fruit." JOURNAL OF ADVANCES IN AGRICULTURE 12 (July 10, 2021): 61–69. http://dx.doi.org/10.24297/jaa.v12i.9069.

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A portable spectrometer was validated to determine optimum harvesting stage of ‘Ataúlfo’ using dry matter and skin color as fruit indicators. To build the model, samples were collected as follows: a. Unripe; b. Green Mature 1; c. Green Mature 2; d. Green Mature 3; and e. Fully mature. Fruit were scanned with a near infrared spectrometer at three temperatures (15, 25, and 35 °C). Skin color (‘a’ value) was measured with a Minolta 400 colorimeter. DM was attained in a conventional oven by drying samples for 72 h at 60 °C. Model was built and validated three times. The best model linearity was ob

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WACHS, JUAN, HELMAN STERN, and MARK LAST. "COLOR FACE SEGMENTATION USING A FUZZY MIN-MAX NEURAL NETWORK." International Journal of Image and Graphics 02, no. 04 (2002): 587–601. http://dx.doi.org/10.1142/s021946780200086x.

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This work presents an automated method of segmentation of faces in color images with complex backgrounds. Segmentation of the face from the background in an image is performed by using face color feature information. Skin regions are determined by sampling the skin colors of the face in a Hue Saturation Value (HSV) color model, and then training a fuzzy min-max neural network (FMMNN) to automatically segment these skin colors. This work appears to be the first application of Simpson's FMMNN algorithm to the problem of face segmentation. Results on several test cases showed recognition rates of

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Yang, Xiaoying, Nannan Liang, Wei Zhou, and Hongmei Lu. "A Face Detection Method Based on Skin Color Model and Improved AdaBoost Algorithm." Traitement du Signal 37, no. 6 (2020): 929–37. http://dx.doi.org/10.18280/ts.370606.

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This paper integrates skin color model and improved AdaBoost into a face detection method for high-resolution images with complex backgrounds. Firstly, the skin color areas were detected in a multi-color space. Each image was subject to adaptive brightness compensation, and converted into the YCbCr space, and a skin color model was established to solve face similarity. After eliminating the background interference by morphological method, the skin color areas were segmented to obtain the candidate face areas. Next, the inertia weight control factors and random search factor were introduced to

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Chen, Zhen-Xue, Cheng-Yun Liu, Fa-Liang Chang, and Xu-Zhen Han. "Fast Face Detection Algorithm Based on Improved Skin-Color Model." Arabian Journal for Science and Engineering 38, no. 3 (2012): 629–35. http://dx.doi.org/10.1007/s13369-012-0376-1.

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MASUKAWA, Yuya, Izumi NISHIDATE, Yoshihisa AIZU, Hiromichi MISHINA, and Tomonori YUASA. "315 Color Appearance of Blood in Skin Tissue : Monte Carlo Simulation for Skin Model." Proceedings of Conference of Hokkaido Branch 2001.41 (2001): 102–3. http://dx.doi.org/10.1299/jsmehokkaido.2001.41.102.

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Johan, Nurul Fatiha, Yasir Mohd Mustafah, and Nahrul Khair Alang Md Rashid. "Human Body Parts Detection Using YCbCr Color Space." Applied Mechanics and Materials 393 (September 2013): 556–60. http://dx.doi.org/10.4028/www.scientific.net/amm.393.556.

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Skin color is proved to be very useful technique for human body parts detection. The detection of human body parts using skin color has gained so much attention by many researchers in various applications especially in person tracking, search and rescue. In this paper, we propose a method for detecting human body parts using YCbCr color spaces in color images. The image captured in RGB format will be transformed into YCbCr color space. This color model will be converted to binary image by using color thresholding which contains the candidate human body parts like face and hands. The detection

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Hou, Shun Yan, Jian Min Qie, and Jing Xu. "A Novel Approach of Face Detection Based on Double Skin Models and AdaBoost Algorithm." Applied Mechanics and Materials 568-570 (June 2014): 740–43. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.740.

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A novel face detection approach based on double skin models and AdaBoost algorithm is proposed in this paper. The image segemention of skin regions is firstly got with a fixed threshold skin model in YCbCr color space. The image segemention result is used for optimizing the parameters of Gaussian skin color model which is used for the image segmentation of the skin regions secondly. The logical operations are computed with the twice results of skin segmentation and then the coarse position of candidate face regions are got by morphological processing. Finally, the accurate face regions are acq

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SHIH, FRANK Y., SHOUXIAN CHENG, CHAO-FA CHUANG, and PATRICK S. P. WANG. "EXTRACTING FACES AND FACIAL FEATURES FROM COLOR IMAGES." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 03 (2008): 515–34. http://dx.doi.org/10.1142/s0218001408006296.

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In this paper, we present image processing and pattern recognition techniques to extract human faces and facial features from color images. First, we segment a color image into skin and non-skin regions by a Gaussian skin-color model. Then, we apply mathematical morphology and region filling techniques for noise removal and hole filling. We determine whether a skin region is a face candidate by its size and shape. Principle component analysis (PCA) is used to verify face candidates. We create an ellipse model to locate eyes and mouths areas roughly, and apply the support vector machine (SVM) t

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Rijal, Yusron, and Awalia Nofitasari. "Filter Halaman Web Pornografi Menggunakan Kecocokan Kata dan Deteksi Warna Kulit." CAUCHY 1, no. 4 (2011): 207. http://dx.doi.org/10.18860/ca.v1i4.1795.

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This paper presents an effort to detect pornographic webpages. It was stated that a positive relationship exists between percentage of human skin color in an image and the image itself (Jones et.al., 1998). Based on the statement, rather than using the traditional method of text-filtering, this paper propose a new approach to detect pornographic images by using skin color detection. The skin color detection performed by using RGB, HSI, and YCbCr color model. Using algorithm stated by Ap-apid (Ap-apid, 2005), the system will classify nude and not-nude images. If one or more nude images are foun

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Lv, Chongshan, Ting Zhang, and Chengyuan Liu. "An Improved Otsu’s Thresholding Algorithm on Gesture Segmentation." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 2 (2017): 247–50. http://dx.doi.org/10.20965/jaciii.2017.p0247.

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In gesture recognition systems, segmenting gestures from complex background is the hardest and the most critical part. Gesture segmentation is the prerequisite of following image processing, and the result of segmentation has a direct influence on the result of gesture recognition. This paper proposed an algorithm of adaptive threshold gesture segmentation based on skin color. First of all, the image should be transformed from RGB color space to YCbCr color space. After eliminating luminance component Y, similarity graph of skin color will be obtained from the Gaussian model. Then Otsu adaptiv

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Bhoyar. "Skin Color Detection Model Using Neural Networks and its Performance Evaluation." Journal of Computer Science 6, no. 9 (2010): 963–68. http://dx.doi.org/10.3844/jcssp.2010.963.968.

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Lee, Jiann-Shu, Yung-Ming Kuo, Pau-Choo Chung, and E.-Liang Chen. "Naked image detection based on adaptive and extensible skin color model." Pattern Recognition 40, no. 8 (2007): 2261–70. http://dx.doi.org/10.1016/j.patcog.2006.11.016.

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., Mrunmayee V. Daithankar. "SKIN SEGMENTATION USING DIFFERENT INTEGRATED COLOR MODEL APPROACHES FOR FACE DETECTION." International Journal of Research in Engineering and Technology 03, no. 08 (2014): 348–53. http://dx.doi.org/10.15623/ijret.2014.0308054.

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Yadav, Shalini, and Neeta Nain. "A novel approach for face detection using hybrid skin color model." Journal of Reliable Intelligent Environments 2, no. 3 (2016): 145–58. http://dx.doi.org/10.1007/s40860-016-0024-8.

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Hong, Duan, and Yang Luo. "A Method of Hand Signal Segmentation Based on YCbCr Space and Background Difference." Applied Mechanics and Materials 380-384 (August 2013): 4112–15. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.4112.

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This paper proposes a method of segmentation of hand signal, based on the skin color model in YCbCr space and background subtraction under complex background. This paper discussed the reasonable threshold selection of Cb, Cr in the skin color model and the segmentation selection of skin area combining the background segmentation. Finally, an inequality of hands outline feature is proposed to complete the division processing of the palm part of the area of skin. Experiments show the accurate segmentation of gesture under a complex static background.

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Kedir, Ahmed, Mohib Ullah, and Jacob Renzo Bauer. "SPECTRANET: A DEEP MODEL FOR SKIN OXYGENATION MEASUREMENT FROM MULTI-SPECTRAL DATA." Electronic Imaging 2020, no. 15 (2020): 83–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.15.color-083.

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Skin oxygenation level is an important indicator for the anesthesiology and psychophysiology of a wide range of skin diseases. The non-contact patient monitoring approaches rely on traditional least square method which are not accurate and can’t be deployed in clinical practices. In this paper, we exploited the power of deep learning to measure the skin oxygenation level from 16 channel spectral filter array cameras (SFA). Our architecture named SpectraNet consist of three important block i.e. a chain of Convolutional Neural Network (CNN) for feature extraction from the spectral data, an chann

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BONVENTI, WALDEMAR, and ANNA HELENA REALI COSTA. "HYBRID AND INCREMENTAL FUZZY LEARNING FOR HUMAN SKIN DETECTION." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 06 (2008): 1241–65. http://dx.doi.org/10.1142/s0218001408006739.

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In this paper, a framework for detection of human skin in digital images is proposed. This framework is composed of a training phase and a detection phase. A skin class model is learned during the training phase by processing several training images in a hybrid and incremental fuzzy learning scheme. This scheme combines unsupervised- and supervised-learning: unsupervised, by fuzzy clustering, to obtain clusters of color groups from training images; and supervised to select groups that represent skin color. At the end of the training phase, aggregation operators are used to provide combinations

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Bastos, João Luiz, Aluisio J. D. Barros, Roger Keller Celeste, Yin Paradies, and Eduardo Faerstein. "Age, class and race discrimination: their interactions and associations with mental health among Brazilian university students." Cadernos de Saúde Pública 30, no. 1 (2014): 175–86. http://dx.doi.org/10.1590/0102-311x00163812.

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Although research on discrimination and health has progressed significantly, it has tended to focus on racial discrimination and US populations. This study explored different types of discrimination, their interactions and associations with common mental disorders among Brazilian university students, in Rio de Janeiro in 2010. Associations between discrimination and common mental disorders were examined using multiple logistic regression models, adjusted for confounders. Interactions between discrimination and socio-demographics were tested. Discrimination attributed to age, class and skin col

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BOURBAKIS, N., P. KAKUMANU, S. MAKROGIANNIS, R. BRYLL, and S. PANCHANATHAN. "NEURAL NETWORK APPROACH FOR IMAGE CHROMATIC ADAPTATION FOR SKIN COLOR DETECTION." International Journal of Neural Systems 17, no. 01 (2007): 1–12. http://dx.doi.org/10.1142/s0129065707000920.

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The goal of image chromatic adaptation is to remove the effect of illumination and to obtain color data that reflects precisely the physical contents of the scene. We present in this paper an approach to image chromatic adaptation using Neural Networks (NN) with application for detecting — adapting human skin color. The NN is trained on randomly chosen color images containing human subject under various illuminating conditions, thereby enabling the model to dynamically adapt to the changing illumination conditions. The proposed network predicts directly the illuminant estimate in the image so

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Wang, Zhi Wen, and Shao Zi Li. "Face Recognition Based on Template Matching and Skin-Color Segmentation." Advanced Materials Research 271-273 (July 2011): 165–70. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.165.

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In order to overcome these deficiencies that computation of recognition algorithm based on template matching is very high and the recognition rate of recognition algorithms based on skin-color segmentation is low, and is vulnerable to the impact of background which is similar with skin-color, face recognition algrithom based on skin color segmentation and template matching is presented in this paper. According to the clustering properties that the skin-color of human faces have emerged in the YCbCr color space, the regions closing to facial skin color are separated from the image by using Gaus

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Zheng, Jian-Hua, Chong-Yang Hao, Yang-Yu Fan, and Xian-Yong Zang. "ADAPTIVE SKIN DETECTION UNDER UNCONSTRAINED LIGHTING CONDITIONS USING A BIGAUSSIAN MODEL AND ILLUMINATION ESTIMATION." Image Analysis & Stereology 24, no. 1 (2011): 21. http://dx.doi.org/10.5566/ias.v24.p21-33.

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An algorithm is proposed to improve the performance of skin detection algorithms under poor illumination conditions. A hybrid skin detection model is addressed to solve these problems by combining two Gaussian models of skin under normal conditions and bright illumination. According to the distribution of the combined models, the algorithm automatically evaluates the skin segmentation result of an adaptive threshold algorithm based on a Gaussian model by estimating the illumination conditions of image. If the estimation result shows that the illumination condition is very different from the no

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Maiti, Ananjan, Biswajoy Chatterjee, and K. C. Santosh. "Skin Cancer Classification Through Quantized Color Features and Generative Adversarial Network." International Journal of Ambient Computing and Intelligence 12, no. 3 (2021): 75–97. http://dx.doi.org/10.4018/ijaci.2021070104.

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Early interpretation of skin cancer through computer-aided diagnosis (CAD) tools reduced the intricacy of the treatments as it can attain a 95% recovery rate. To frame up with computer-aided diagnosis system, scientists adopted various artificial intelligence (AI) designed to receive the best classifiers among these diverse features. This investigation covers traditional color-based texture, shape, and statistical features of melanoma skin lesion and contrasted with suggested methods and approaches. The quantized color feature set of 4992 traits were pre-processed before training the model. Th

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Ishida, Takeshi. "A model of octopus epidermis pattern mimicry mechanisms using inverse operation of the Turing reaction model." PLOS ONE 16, no. 8 (2021): e0256025. http://dx.doi.org/10.1371/journal.pone.0256025.

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Many cephalopods such as octopi and squid can purposefully and rapidly change their skin color. Furthermore, it is widely known that some octopi have the ability to rapidly change the color and unevenness of their skin to mimic their surroundings. However, there has been little research published on the mechanisms by which an octopus recognizes its surrounding landscape and changes its skin pattern. We are unaware of any hypothetical model that explains this mimicry mechanism to date. In this study, the mechanism of octopus skin pattern change was assumed to be based on the Turing pattern mode

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