  Classification is an important step in machine vision systems; it reveals the
true identity of an object using features extracted in pre-processing steps.
Practical usage requires the operation to be fast, energy efficient and easy to
implement. In this paper, we present a design of the Minimum Distance
Classifier based on an FPGA platform. It is optimized by the pipelined
structure to strike a balance between device utilization and computational
speed. In addition, the dimension of the feature space is modeled as a generic
parameter, making it possible for the design to re-generate hardware to cope
with feature space with arbitrary dimensions. Its primary application is
demonstrated in color segmentation on FPGA in the form of efficient
classification using color as a feature. This result is further extended by
introducing a multi-class component labeling module to label the segmented
color components and measure their geometric properties. The combination of
these two modules can effectively detect road signs as the region of interests.
