{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:13:01Z","timestamp":1760710381522,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2020,10,15]],"date-time":"2020-10-15T00:00:00Z","timestamp":1602720000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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 \u00d7 480 resolution color images.<\/jats:p>","DOI":"10.3390\/s20205833","type":"journal-article","created":{"date-parts":[[2020,10,15]],"date-time":"2020-10-15T09:02:03Z","timestamp":1602752523000},"page":"5833","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Stereo Imaging Using Hardwired Self-Organizing Object Segmentation"],"prefix":"10.3390","volume":"20","author":[{"given":"Ching-Han","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1309-8804","authenticated-orcid":false,"given":"Guan-Wei","family":"Lan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ching-Yi","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Information and Telecommunications Engineering, Ming Chuan University, Taoyuan 333321, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yen-Hsiang","family":"Huang","sequence":"additional","affiliation":[{"name":"National Chung-Shan Institute of Science and Technology, Taoyuan 32546, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/0734-189X(88)90022-9","article-title":"A survey of thresholding techniques","volume":"4","author":"Sahoo","year":"1988","journal-title":"Comput. 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