Mid Sweden University

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 minimum distance classifier based on FPGA platform. It is optimized by the pipelined structure to strike a balance between the device utilization and computational speed. In addition, the dimension of the feature space is modeled as 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 on color segmentation on FPGA in the form of efficient classification using color as features. This result is further extended by introducing a multi-class component labeling module to label the segmented color components and measure the geometric properties of them. The combination of these two modules can effectively detect road signs as region of interests.

In this paper we present a novel and robust algorithm for automatic recognition of road signs by using histogram of oriented gradient (HOG) as the main feature and minimum distance classifier (MDC) to classify numbers written on speed limit road signs. It also describes how other geometrical properties can be added to feature vector in order to increase the robustness of proposed algorithm.