Segmentation of Different Skin Colors with Different Lighting Conditions by Combining Graph Cuts Algorithm with Probability Neural Network Classification, and its Application

Hwang, Chih‐Lyang; Lu, Kai-Di; Pan, Yi-Tsung

doi:10.1007/s11063-012-9275-4

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…In previous years, skin colour feature segmentation played a pivotal role in dynamic hand gesture segmentation since skin is invariant to hand scale changes and posture variations [2]. The existing body of studies on skin colour segmentation has suggested the use of colour spaces to perform human skin segmentation by particular skin colour threshold [3][4][5]. This is because utilising skin colour threshold approach can easily and simply segment the skin features including hand skin from the background to be used within dynamic hand gesture segmentation methods [6].…”

Section: Introductionmentioning

confidence: 99%

Low Cost Skin Segmentation Scheme in Videos Using Two Alternative Methods for Dynamic Hand Gesture Detection Method

Thabet

Khalid

Sulaiman

et al. 2017

Advances in Multimedia

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Recent years have witnessed renewed interest in developing skin segmentation approaches. Skin feature segmentation has been widely employed in different aspects of computer vision applications including face detection and hand gestures recognition systems. This is mostly due to the attractive characteristics of skin colour and its effectiveness to object segmentation. On the contrary, there are certain challenges in using human skin colour as a feature to segment dynamic hand gesture, due to various illumination conditions, complicated environment, and computation time or real-time method. These challenges have led to the insufficiency of many of the skin color segmentation approaches. Therefore, to produce simple, effective, and cost efficient skin segmentation, this paper has proposed a skin segmentation scheme. This scheme includes two procedures for calculating generic threshold ranges in Cb-Cr colour space. The first procedure uses threshold values trained online from nose pixels of the face region. Meanwhile, the second procedure known as the offline training procedure uses thresholds trained out of skin samples and weighted equation. The experimental results showed that the proposed scheme achieved good performance in terms of efficiency and computation time.

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Section: Introductionmentioning

confidence: 99%

Low Cost Skin Segmentation Scheme in Videos Using Two Alternative Methods for Dynamic Hand Gesture Detection Method

Thabet

Khalid

Sulaiman

et al. 2017

Advances in Multimedia

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“…In general, the process of converting colors from CMY to CIELAB can be defined as characterization (Lee and Lee, 2013), while the contrary process is often called calibration (Yang et al, 2012); obviously the paper mainly interests the latter process. Now the commonly used calibration algorithms are 3D interpolation (Pekkucuksen and Altunbasak, 2013;Liu et al, 2013;Srivastava et al, 2010), polynomial regression (Hong et al, 2001;Nussbaum et al, 2011), neural network (Kang and Anderson, 1992;Hwang et al, 2013), Neugebauer equations (Morovic et al, 2012;Hebert and Hersch Roger, 2011), and so on. Taking into account the quantity of color patches, the precision and computing efficiency of the algorithms, polynomial regression modes is selected in this paper.…”

Section: Introductionmentioning

confidence: 99%

Calculating cyan-magenta-yellow-black (CMYK) printer gray component data based on polynomial modeling

Sun¹,

Liu²,

Zhou³

2014

Sci. Res. Essays

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As gray color data is very important for printing quality, a method based on polynomial modeling is proposed to calculate the gray color data of different lightness. Firstly, the IT8.7/3 color target which defines input cyan-magenta-yellow-black (CMYK) signals is printed, and the corresponding CIELAB (Commission Internationale de l'Eclairage L*, a* and b*) values are obtained by a spectrophotometer with color sensors. Secondly, the polynomial regression method is used to determine the relationship between these two color spaces, in which the given CIELAB color's CMY value is calculated. At last, as the obtained cyan-magenta-yellow (CMY) gray color may contain Chroma information due to the computational error, a new color target is developed to find the real gray colors based on the relationship above, and the final relationship between CIE-L* and CMY are obtained. In the experiment, the average color error is 2.65ΔE, which is below the printing error threshold, hence the proposed algorithm for calculating gray color data can be used in printing process.

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Hand Gesture Segmentation from Complex Color-Texture Background Image

Verma

Wankar

Rao

et al. 2013

Lecture Notes in Computer Science

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Segmentation of Different Skin Colors with Different Lighting Conditions by Combining Graph Cuts Algorithm with Probability Neural Network Classification, and its Application

Cited by 4 publications

References 18 publications

Low Cost Skin Segmentation Scheme in Videos Using Two Alternative Methods for Dynamic Hand Gesture Detection Method

Low Cost Skin Segmentation Scheme in Videos Using Two Alternative Methods for Dynamic Hand Gesture Detection Method

Calculating cyan-magenta-yellow-black (CMYK) printer gray component data based on polynomial modeling

Hand Gesture Segmentation from Complex Color-Texture Background Image

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