Hardware implementation of a decision tree classifier for object recognition applications

Hardware implementation of a widely used decision tree classifier is presented in this paper. The classifier task is to perform image-based object classification. The performance evaluation of the implemented architecture in terms of resource utilization and processing speed are reported. The presented architecture is compact, flexible and highly scalable and compares favorably to software-only solutions in terms of processing speed and power consumption.