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- Title
Stereo Imaging Using Hardwired Self-Organizing Object Segmentation.
- Authors
Chen, Ching-Han; Lan, Guan-Wei; Chen, Ching-Yi; Huang, Yen-Hsiang
- Abstract
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 × 480 resolution color images.
- Subjects
STEREO vision (Computer science); STEREO image; STEREOPHONIC sound systems; IMAGING systems; SELF-organizing maps; VISION
- Publication
Sensors (14248220), 2020, Vol 20, Issue 20, p5833
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s20205833