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Human Skin Detection Using RGB, HSV and YCbCr Color Models
Human Skin detection deals with the recognition of skin-colored pixels a...
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A Fusion Approach for Efficient Human Skin Detection
A reliable human skin detection method that is adaptable to different hu...
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Predictive Inequity in Object Detection
In this work, we investigate whether state-of-the-art object detection s...
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Literature Review: Human Segmentation with Static Camera
Our research topic is Human segmentation with static camera. This topic ...
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Automated Switching System for Skin Pixel Segmentation in Varied Lighting
In Computer Vision, colour-based spatial techniquesoften assume a static...
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Learning morphological operators for skin detection
In this work we propose a novel post processing approach for skin detect...
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Semi-supervised Skin Detection by Network with Mutual Guidance
In this paper we present a new data-driven method for robust skin detect...
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Comparative Study of Statistical Skin Detection Algorithms for Sub-Continental Human Images
Object detection has been a focus of research in human-computer interaction. Skin area detection has been a key to different recognitions like face recognition, human motion detection, pornographic and nude image prediction, etc. Most of the research done in the fields of skin detection has been trained and tested on human images of African, Mongolian and Anglo-Saxon ethnic origins. Although there are several intensity invariant approaches to skin detection, the skin color of Indian sub-continentals have not been focused separately. The approach of this research is to make a comparative study between three image segmentation approaches using Indian sub-continental human images, to optimize the detection criteria, and to find some efficient parameters to detect the skin area from these images. The experiments observed that HSV color model based approach to Indian sub-continental skin detection is more suitable with considerable success rate of 91.1 true negatives.
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