Chelsia Amy Doukim

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Two types of combining strategies were evaluated namely combining skin features and combining skin classifiers. Several combining rules were applied where the outputs of the skin classifiers are combined using binary operators such as the AND and the OR operators, “Voting”, “Sum of Weights” and a new neural network. Three chrominance components from the(More)
Skin detection is an important preliminary process for subsequent feature extraction in image processing techniques. There are several techniques that are used for skin detection. In this work, the multi-layer perceptron (MLP) neural network is used. One of the important aspects of MLP is how to determine the network topology. The number of neurons in the(More)
Two methods of data fusion to improve the performance of skin detection were tested. The first method fuses two chrominance components from the same color space, while the second method fuses the outputs of two skin detection methods each based on a different color space. The color spaces used are the normalized red, green, blue (RGB) color space, referred(More)
Skin detection is used as the first step for subsequent feature extraction in image processing techniques. In this work, the performance of skin detection in three colour spaces; Normalised RGB, Modified Normalised RGB and YC C is investigated. The most famous and large database namely Compaq database is used to construct the histogram model and to test the(More)
Explosive growth of digital technologies has spawned a plethora of images available online. Therefore, content-based image classification has been the subject of many research works in recent years. This paper reviewed some of the most commonly used image classification approaches. Most of the existing approaches used low-level features and intermediate(More)
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