Parametric-histogramming technique for gross segmentation of color images of natural scenes (processing)
Mehmet Çelenk · 1983
This thesis describes a new systematic method for gross segmentation of color images of natural scenes. It has been developed within the context of the human visual system and mathematical pattern recognition theory. The eventual goal of the research is to integrate these two contexts to extract the visually distinct segments of an image which have vital importance for higher-level analysis or interpretation. A novel computational (pattern recognition) technique, called parametric-histogramming, is proposed in accordance with the human color perception and the Fisher criterion. This technique detects and isolates the image clusters efficiently and correctly using the 1-D parametric histograms of the L*,H('o),C* cylindrical coordinates of the (L*,a*,b*) - uniform color space in an unsupervised operation mode. In order to obtain the features most useful for a particular image, a new statistical-structural feature extraction method is devised in the form of a reference feature library containing several files. The underlying files tend to model the fundamental characteristics of uniformity, isolation, boundary, identation, texture, shadow and highlight patterns according to the grouping property of human eye and the Julesz conjecture. The dynamic operation characteristic of the feature selection phase is a significant property of the method. This type of operation, called dynamic feature space construction, enables the algorithm to select a particular feature space from the set of feature spaces so that image clusters in the selected space are more tractable and reliable.