Color Image Segmentation

Image segmentation is a crucial digital image processing step that decisively affects the outcome of any higher-level operation such as pattern recognition and image understanding. It can be defined as a procedure that subdivides (partitions) an image into its constituent regions or objects. All the pixels in the same region are similar with respect to some characteristic or computed property, such as color, intensity, or texture. Adjacent regions are significantly different with respect to the same characteristic(s). Color in a digital image often contains valuable information about the scene or object being imaged, which can be lost during the routine color-to-grayscale image conversion. This fact together with a rapid increase in the power of personal computers caused color image segmentation algorithms to be readily available on modern PCs. Numerous approaches have been developed during the last decades that enable efficient segmentation of images based on color. Sometimes conve...

In this research, an apparatus for sorting raisins has been designed and fabricated based on machine vision system. This
system was composed of conveyor belt, lighting box, controlling and processing system unit and sorting unit. Color
feature is the most important parameter in classification and sorting of raisins. In order to carry out image processing
and to extract useful features of captured images by machine vision a highly efficient algorithm was developed and
implemented in Visual Basic 6.0 environment. The algorithm consisted of background segmentation, raisin selection
and feature extraction. The developed algorithm initially extracts the raisins by removing the background from the taken
images. It then sorts the raisins according to their Hue, Saturation and Intensity (HSI) color features. By a suitable
combination of length and HSI color values raisins were graded it two classes. The final step in the algorithm was the
calculation of the center of gravity of each raisin to be later used for automatic sorting and rejection of bad raisins. In
order to evaluate the precision of the sorter statistical analysis was carried out. Experimental results indicated the
accuracy of the proposed system is about 93%.

This paper describes a color texture-based image segmentation system. The color texture information is obtained via modeling with the Multispectral Simultaneous Auto Regressive (MSAR) random field model. The general color content characterized by ratios of sample color means is also used. The image is segmented into regions of uniform color texture using an unsupervised histogram clustering approach that utilizes the