Distorted dense analogs intelligent recognition in fisheye lenses by partially featured points calibrating and clustering
Applied Optics, ISSN: 2155-3165, Vol: 61, Issue: 7, Page: D85-D91
2022
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Dense analogs intelligent recognition (DAIR) has many potential applications in various fields as a new cross-disciplinary frontier of artificial intelligence and optical technology. However, with extensive application of fisheye lenses, inherent distortions in fisheye images have brought new challenges to DAIR. To solve this problem, we propose and experimentally demonstrate a partially featured points calibrating method that needs only correction of central points of the bounding boxes output by a convolutional neural network (CNN). The key to our method is a central-coordinate calibrating and clustering algorithm (CCCCA) based on a hemispheric double longitude projection model. Experimental results show that the CCCCA reduces the classification error rate by 6.05%, enhancing the classification accuracy of distorted DAIR up to 99.31%. Such classification accuracy is about 2.74% higher than that achieved by the mainstream online hard example mining algorithm, effectively modifying recognition errors induced by the CNN.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85125458938&origin=inward; http://dx.doi.org/10.1364/ao.444602; http://www.ncbi.nlm.nih.gov/pubmed/35297831; https://opg.optica.org/abstract.cfm?URI=ao-61-7-D85; https://dx.doi.org/10.1364/ao.444602; https://opg.optica.org/ao/abstract.cfm?uri=ao-61-7-D85
Optica Publishing Group
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