Stereo imaging using hardwired self-organizing object segmentation

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

研究成果: 雜誌貢獻期刊論文同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文???core.languages.en_GB???
文章編號5833
頁(從 - 到)1-15
頁數15
期刊Sensors (Switzerland)
20
發行號20
DOIs
出版狀態已出版 - 2 10月 2020

指紋

深入研究「Stereo imaging using hardwired self-organizing object segmentation」主題。共同形成了獨特的指紋。

引用此