Human Skin Detection Using RGB, HSV and YCbCr Color Models

Kolkur, Seema; Kalbande, Dhananjay; Shimpi, Pallavi; Bapat, Chaitanya; Jatakia, Janvi

doi:10.2991/iccasp-16.2017.51

Cited by 170 publications

(99 citation statements)

References 7 publications

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“…So, we segment the skin with wound from the image first. Currently, for the study of skin detection, many articles [13–15] convert images to the YCbCr color space to complete the task. That is because the distribution of the skin color in this space is more concentrated.…”

Section: Related Workmentioning

confidence: 99%

A Composite Model of Wound Segmentation Based on Traditional Methods and Deep Neural Networks

Wang

Liu

et al. 2018

Computational Intelligence and Neuroscience

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Wound segmentation plays an important supporting role in the wound observation and wound healing. Current methods of image segmentation include those based on traditional process of image and those based on deep neural networks. The traditional methods use the artificial image features to complete the task without large amounts of labeled data. Meanwhile, the methods based on deep neural networks can extract the image features effectively without the artificial design, but lots of training data are required. Combined with the advantages of them, this paper presents a composite model of wound segmentation. The model uses the skin with wound detection algorithm we designed in the paper to highlight image features. Then, the preprocessed images are segmented by deep neural networks. And semantic corrections are applied to the segmentation results at last. The model shows a good performance in our experiment.

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Section: Related Workmentioning

confidence: 99%

A Composite Model of Wound Segmentation Based on Traditional Methods and Deep Neural Networks

Wang

Liu

et al. 2018

Computational Intelligence and Neuroscience

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“…In contrary to the studies about the usage of color models mentioned above [26][27], in this article, we propose using parameters Hue and Value of the HSV color model to construct the color gradient in the RGB color model. In this case, parameter Saturation equals a constant value.…”

Section: An Analytical Model For the Formation Of A Heat Mapmentioning

confidence: 97%

“…Two color models, RGB and HSV, are selected for conversions [26]. The RGB model describes the color space based on Red, Green and Blue colors ( Figure 5, a).…”

Section: An Analytical Model For the Formation Of A Heat Mapmentioning

confidence: 99%

Detection the Places of the Heat Energy Leak on the Underground Thermal Pipelines Using the Computer System

Zhuravska¹,

Lernatovych²,

Burenko³

2019

Adv. sci. technol. eng. syst. j.

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The purpose of this paper is to improve the thermographic method for detection the places of heat energy losses on the urban underground thermal pipelines using the computer system. This goal was achieved by building a heat map of the surveyed heating main with data layering directly on a computer map of the area, which can be accessed from a mobile device by authorization via the Internet (profile on the social network Google, Facebook, Twitter, etc.). An analytical model was proposed for converting the temperature values measured over the underground heating main by a non-contact digital pyrometer to an image of the RGB color model using the parameters of the HSV color model. The implementation of the integrated Google technologies complex (Google Firebase, Firebase Authentication, and Google Maps) in the technological process of urban housing and public services was realized. The information-measuring computer system (IMCS) was developed. The proposed improvements in the thermographic method provided independently from each other work of different service teams due to the exchange of data between structural units through the cloud data storage. The results of the practical research allowed detecting the place of the underground breakthrough of the heat pipe quickly, to position the repair equipment over the accident section precisely using the GPS module on the mobile device. Thus, the size of the dug road section was minimized, material resources were saved. The experimental studies carried out confirmed the correctness of the theoretical assumptions.

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“…RGB color space is broadly used and is normally the default color space for storing and representing digital images [12]. We can get any other color space from a linear or non-linear conversion of RGB.…”

Section: Skin Color Segmentationmentioning

confidence: 99%

Facial Expression Recognition Using Static Facial Images

G.Sowmiya¹,

Kumutha²

2018

IJSRCSE

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Delicate concerns about the treatment of individuals during interviews and interrogations have stimulated efforts to develop "non-intrusive" technologies for rapidly assessing the credibility of statements by individuals in a variety of sensitive environments. Methods or processes that have the potential to exactly focus investigative resources will advance operational excellence and improve investigative capabilities. Facial expressions have the capacity to communicate emotion and regulate interpersonal behavior. Facial Expression Recognition -FER has been dramatically developed in recent years, especially machine learning, Image processing and human cognition. For this reason, the bang and possible usage of automatic facial expression recognition system have been mounting in a broad range of applications, including human-computer interaction, robot control and driver state observation. This paper proposes an automatic facial expression recognition using static facial images, capable of distinctive the four universal emotions: neutral, happiness, sadness and surprise. It is designed to be person independent and tailored only for static images.

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Human Skin Detection Using RGB, HSV and YCbCr Color Models

Cited by 170 publications

References 7 publications

A Composite Model of Wound Segmentation Based on Traditional Methods and Deep Neural Networks

A Composite Model of Wound Segmentation Based on Traditional Methods and Deep Neural Networks

Detection the Places of the Heat Energy Leak on the Underground Thermal Pipelines Using the Computer System

Facial Expression Recognition Using Static Facial Images

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