Skip to main content
Article thumbnail
Location of Repository

Binary object recognition system on FPGA with bSOM

By Kofi Appiah, Andrew Hunter, Patrick Dickinson and Hongying Meng


Tri-state Self Organizing Map (bSOM), which takes binary inputs and maintains tri-state weights, has been used for classification rather than clustering in this paper. The major contribution here is the demonstration of the potential use of the modified bSOM in security surveillance, as a recognition system on FPGA

Topics: G411 Computer Architectures, G700 Artificial Intelligence
Publisher: IEEE
Year: 2010
OAI identifier:

Suggested articles


  1. (2009). A binary self-organizing map and its FPGA implementation,” doi
  2. (2000). A comparison of global versus local color histograms for object recognition,” doi
  3. (1997). A decision-theoretic generalization of on-line learning and an application to boosting,” doi
  4. (2002). A fast model-free morphologybased object tracking algorithm,” doi
  5. (1998). A general framework for object detection,” doi
  6. (2008). A probabilistic self-organizing map for facial recognition,” doi
  7. (2010). Accelerated hardware video object segmentation: from foreground detection to connected components labelling,” Computer Vision and Image Understanding, doi
  8. (2009). Adaptive object classification in surveillance system by exploiting scene context,” doi
  9. (2003). Automatic recognition by gait: progress and prospects,” doi
  10. (2010). Binary histogram based split/merge object detection using FPGAs,” doi
  11. (2009). Colour-based object tracking in surveillance application,”
  12. (2004). FPGA implementation of adaboost algorithm for detection of face biometrics,” doi
  13. (2004). How iris recognition works,” doi
  14. (2004). Improving object classification in far-field video,” doi
  15. (2001). Maps, third extended edition ed.
  16. (2007). Multi-object tracking using color, texture and motion,” doi
  17. (2002). Neural networks for video surveillance,” doi
  18. (2007). New methods in iris recognition,” doi
  19. (2009). Parallelized architecture of multiple classifiers for face detection,” doi
  20. (2007). Real-time object classifi-cation in video surveillance based on appearance learning,” doi
  21. (2007). Real-time object classification in video surveillance based on appearance learning,” doi
  22. (2004). Robust real-time face detection,” doi
  23. (2004). Robust tracking and object classification towards automated video surveillance,” doi
  24. (2006). Self Organizing Feature Map for Color Quantization on FPGA. doi
  25. (1995). Self-Organizing Maps. doi

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.