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Color Segmentation on FPGA Using Minimum Distance Classifier for Automatic Road Sign Detection
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Technology and Media. (STC)
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Technology and Media. (STC)
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Technology and Media. (STC)
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Technology and Media. (STC)
2012 (English)In: IST 2012 - 2012 IEEE International Conference on Imaging Systems and Techniques, Proceedings, IEEE conference proceedings, 2012, 516-521 p.Conference paper, Oral presentation only (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2012. 516-521 p.
Keyword [en]
FPGA;Minimum Distance Classifier; Color Segmentation; Road Sign Detection
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:miun:diva-17672DOI: 10.1109/IST.2012.6295528Scopus ID: 2-s2.0-84870664180Local ID: STCISBN: 978-1-4577-1776-5 (print)OAI: oai:DiVA.org:miun-17672DiVA: diva2:576127
Conference
2012 IEEE International Conference on Imaging Systems and Techniques, IST 2012;Manchester;16 July 2012through17 July 2012;Category numberCFP12IMG-CDR;Code93478
Projects
STC
Funder
Knowledge Foundation
Available from: 2012-12-12 Created: 2012-12-12 Last updated: 2016-10-20Bibliographically approved

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Zhao, JingboThörnberg, BennyShi, YanHashemi, Ashkan

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