Mixture of GMMs and Mixture of Multiple Histograms for Image Segmentation: A Review
International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)
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Segmentation process represents the eye for the computers-robots for commencing the object detection procedure in which the good segmentation process can spot the object easily without any neglected background areas and without any further noise, as known the single modelling of any object can cause some sort of errors which can be reduced when the object is partitioned and each modelled separately, in this paper, we have applied a novel algorithm for modelling the skin-color area of the human-skin pigment by dividing the feature area of that skin-color into several partitions and each of them is modelled using single Gaussian Mixture Model (GMM), and those several resulted GMMs and fused together into a single model called Mixture GMM with some calculated weights for each. Similarity, those feature area are modeleed using lookup table based histogram technique and all of those resulted histogram are fused in one superior technique, the both resulting techniques are compared together. We have achieved a promising results that dominate many other segmentation techniques with high accuracy of skin-color locating, we have applied four well-known color models in the skin-color field that are normalized RGB which referred as rgb, HSV, YCbCr and L*a*b*.
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Gaussian Mixture Model, GMM, Mixture of GMM, color model, skin color, rgb, HSV, YCbCr, histogram, segmentation technique.