Efficient Color-Based Image Segmentation and Feature Classification for Image Processing in Embedded Systems

In this paper, a color based feature extraction and classification approach for image processing in embedded systems in presented. The algorithms and data structures developed for this approach pay particular attention to reduce memory consumption and computation power of the entire image processing, since embedded systems usually impose strong restrictions regarding those resources. The feature extraction is realized in terms of an image segmentation algorithm. The criteria of homogeneity for merging pixels and regions is provided by the color classification mechanism, which incorporates appropriate methods for defining, representing and accessing subspaces in the working color space. By doing so, pixels and regions with color values that belong to the same color class can be merged. Furthermore, pixels with redundant color values that do not belong to any pre-defined color class can be completely discarded in order to minimize computational effort. Subsequently, the extracted regions are converted to a more convenient feature representation in terms of statistical moments up to and including second order. For evaluation, the whole image processing approach is applied to a mobile representative of embedded systems within the scope of a simple real-world scenario.