Stereo imaging using hardwired self-organizing object segmentation

Ching Han Chen, Guan Wei Lan, Ching Yi Chen, Yen Hsiang Huang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Stereo vision utilizes two cameras to acquire two respective images, and then determines the depth map by calculating the disparity between two images. In general, object segmentation and stereo matching are some of the important technologies that are often used in establishing stereo vision systems. In this study, we implement a highly efficient self-organizing map (SOM) neural network hardware accelerator as unsupervised color segmentation for real-time stereo imaging. The stereo imaging system is established by pipelined, hierarchical architecture, which includes an SOM neural network module, a connected component labeling module, and a sum-of-absolute-difference-based stereo matching module. The experiment is conducted on a hardware resources-constrained embedded system. The performance of stereo imaging system is able to achieve 13.8 frames per second of 640 × 80 resolution color images.

Original languageEnglish
Article number5833
Pages (from-to)1-15
Number of pages15
JournalSensors (Switzerland)
Issue number20
StatePublished - 2 Oct 2020


  • Object segmentation
  • SOM
  • Stereo vision


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