Statistical skin color detection method without color transformation for real-time surveillance systems

Chen, Yen-Hsiang; Hu, Kai-Ti; Ruan, Shanq-Jang

doi:10.1016/j.engappai.2012.02.019

Cited by 31 publications

(23 citation statements)

References 20 publications

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“…5b, c respectively. FPGA gives great flexibility in prototyping, designing and development of complex hardware [25][26][27] high gate density (i.e., tens of millions logic gates) and many features necessary for neural-controller implementation. The mixed design environment: Very high speed integrated circuit hardware description language (VHDL) and Xilinx system generator [26] are used since it simplifies the development of neural-controller and it is possible to model and simulate a digital system from a high level of abstraction module designs [26].…”

Section: Design Approach and Behavioral Modeling Of Neural-controllermentioning

confidence: 99%

Design of Cognitive Decision Making Controller for Autonomous Online Adaptive Beam Steering in Free Space Optical Communication System

Raj¹,

Selvi²,

Kumar

et al. 2015

Wireless Pers Commun

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The beam motion is one of the main causes for major power loss which severely degrades the performance of the Free Space Optical Communication (FSOC) system. Designing a suitable controller to correct the beam motion to increase the beam stability at a point becomes significant. This paper presents an investigation on the performance of two types of controller designed for aiming a laser beam to be at a particular spot under dynamic disturbances generated at the transmitter. The experimental nonlinear input-output data mapping is used as the principal components for the controller design. The first design is based on the Taguchi method while the second is the artificial neural network-cognitive method. These controllers process the beam pointing (beam location) information from an opto-electronic position detector and then generate the necessary outputs to steer the beam with fast steering mirror. Conceptually, the controller design problem is addressed with looking the position error information to decide the level of nonlinear static output map for generating the correction control information. The pipelined-parallel architecture of both controllers are developed in a field programmable gate array (FPGA) and installed at the receiver station. Evidence of the suitability and the effectiveness of the proposed controller in terms of prediction exactness, response to impulse, scintillation, Q-factor and bit error rate are provided through experimental results obtained from the 155 Mbps FSOC data link set-up established for the horizontal range of 0.5 km at an altitude of 15.25 m.

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Section: Design Approach and Behavioral Modeling Of Neural-controllermentioning

confidence: 99%

Design of Cognitive Decision Making Controller for Autonomous Online Adaptive Beam Steering in Free Space Optical Communication System

Raj¹,

Selvi²,

Kumar

et al. 2015

Wireless Pers Commun

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show abstract

“…We have compared our technique with several state-ofthe-art methods, namely with (i) several global pixel-wise skin detectors [14][15][16], (ii) with methods that utilize spatial analysis and textural features [9,59,61], and (iii) with facebased adaptation schemes [11,40].…”

Section: Experimental Validationmentioning

confidence: 99%

“…Skin color can be modeled using a set of rules and thresholds defined in color spaces based on some observations [12][13][14][15]. Alternatively, given a representative training set, skin detection rules can be determined using machine learning.…”

Section: Related Literaturementioning

confidence: 99%

Self-adaptive algorithm for segmenting skin regions

Kawulok

Nalepa

et al. 2014

EURASIP J. Adv. Signal Process.

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In this paper, we introduce a new self-adaptive algorithm for segmenting human skin regions in color images. Skin detection and segmentation is an active research topic, and many solutions have been proposed so far, especially concerning skin tone modeling in various color spaces. Such models are used for pixel-based classification, but its accuracy is limited due to high variance and low specificity of human skin color. In many works, skin model adaptation and spatial analysis were reported to improve the final segmentation outcome; however, little attention has been paid so far to the possibilities of combining these two improvement directions. Our contribution lies in learning a local skin color model on the fly, which is subsequently applied to the image to determine the seeds for the spatial analysis. Furthermore, we also take advantage of textural features for computing local propagation costs that are used in the distance transform. The results of an extensive experimental study confirmed that the new method is highly competitive, especially for extracting the hand regions in color images.

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“…The intensity scale of H-S plan is [0,255] for an 8-bits digital system. Thus the coordinates of initial skin color map are: According to equations (7) to (10), various skin color can then be obtained by adjusting the range of H and S. Chen et al (2012) had defined humans skin colors by using constraints of 0 ≤ H ≤ 50 and 51 ≤ S ≤ 173. Before this, Yang et al (2011) had also defined the skin color for the east Asian races by using the constraints of 14.4 ≤ H ≤ 46.8 and 69.615 ≤ S ≤ 149.55.…”

Section: The Definition Of H-s Planmentioning

confidence: 99%

Chip-based adaptive skin color detection using trajectory constraints on hue

Lee

Kwok

et al. 2014

Vis. in Eng.

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Background: In a construction site, the safety of workers can be better secured if vehicle or robot can be properly maneuvered by using image-based gesture guidance. This kind of semi-automatic motion control is based on the features extracted in colors, outlines, textures, and motion intentions. Among these features, detecting worker? s skin color on face and hands could serve as an essential method for enabling robot to determine the region of interest (ROI). However, the performance of skin detection is usually unreliable due to interferences from reflections and shadows. Although many efficient skin color detection methods had been developed in past years, those are mostly based on complicated learning and statistical processes and are unsuitable for embedded systems. The exploration of a concise and efficient skin color detector, therefore, becomes a challenge for applications in mobile robots.

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Statistical skin color detection method without color transformation for real-time surveillance systems

Cited by 31 publications

References 20 publications

Design of Cognitive Decision Making Controller for Autonomous Online Adaptive Beam Steering in Free Space Optical Communication System

Design of Cognitive Decision Making Controller for Autonomous Online Adaptive Beam Steering in Free Space Optical Communication System

Self-adaptive algorithm for segmenting skin regions

Chip-based adaptive skin color detection using trajectory constraints on hue

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