Hardware implementation of a decision tree classifier for object recognition applications

• Autorzy: Fularz M. , Kraft M.
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

decision tree; hardware implementation; FPGA; object recognition

• Wydawca: Wydawnictwo PAK
• Czasopismo: Measurement Automation Monitoring
• Rocznik: 2015
• Tom: Vol. 61, No. 7
• Strony: 379--381
• Opis fizyczny: Bibliogr. 9 poz., rys., schem., tab.

Twórcy

autor

Fularz M.

• Poznan University of Technology, Institute of Control and Information Engineering, 3a Piotrowo St., 60-965 Poznań

autor

Kraft M.

• Poznan University of Technology, Institute of Control and Information Engineering, 3a Piotrowo St., 60-965 Poznań

Bibliografia

• [1] Rokach L., Maimon O.: Top-down induction of decision trees classifiers - a survey. IEEE Transaction on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 35, pp. 476-487, 2005.
• [2] Quinlan J. R.: Decision trees and decision-making. IEEE transactions on Systems. Man and Cybernetics, vol. 20, pp. 339-346, 1990.
• [3] Breiman L.: Random forests. Machine learning, vol. 45, pp. 5-32, 2001.
• [4] Ojala T., Pietikäinen M. and Mäenpää T.: Multiresolution Gray-scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24(7), pp. 971-987, 2002.
• [5] Pedregosa F.: Scikit-learn: Machine learning in Python. The Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011.
• [6] Dalal N., Triggs B.: Histograms of oriented gradients for human detection. In Proc. Of Int. Conf. on Computer Vision and Pattern Recognition CVPR 2005, vol. 1, pp. 886-893, 2005.
• [7] Kraft M., Fularz M.: A Hardware Architecture for Calculating LBP-Based Image Region Descriptors, to appear in Proc. of the 9th Int. Conf. on Computer Recognition Systems (accepted for publication).
• [8] Fularz M., Kraft M., Schmidt A., Kasiński A.: The Architecture of an Embedded Smart Camera for Intelligent Inspection and Surveillance. Advances in Intelligent Systems and Computing, vol. 350, pp. 43-52, 2015.
• [9] https://github.com/Michal-Fularz/decision_tree