Spectral Analysis on Color Detection Sharpness of Animal Vision toward Polychromatic Vision System
Source abstract
Abstract
Abstract Spectral discrimination by animal visions such as human, bird, and butterfly is numerically compared by angular analysis of photo‐response ( PR ) which will be recorded at photo‐receptors. Hyperspectral imaging system is utilized to simulate various animal vision. Bird vision is acute to discriminate colors among different vegetables due to its evenly spaced and narrow spectral responsivity of photo‐receptors compared to that of human. Butterfly vision is excellent in discriminating red tomato ripening due to the exclusive photo‐receptors detecting only over 600 nm. Even real and fake fruits in the same perceived color for human is discriminated by bird vision. Artificial vision with finely resolved polychromatic artificial cone cells are demonstrated surpassing human vision using visible multispectral camera. This provides insights on designing novel bio‐inspired vision system.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · CCD and CMOS Imaging Sensors · Image Processing Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Samsung Advanced Institute of Technology Suwon Gyeonggi 16678 Republic of Korea
School of Integrative Engineering Chung‐Ang University Seoul 06974 Republic of Korea
Department of physics Dongguk University Seoul 04620 Republic of Korea
References (38)
Show source-provided references
1. 10.1017/S095252381200003X10.1017/S095252381200003X ↗
2. 10.3389/fnins.2020.60279610.3389/fnins.2020.602796 ↗
3. 10.1038/scientificamerican0706-6810.1038/scientificamerican0706-68 ↗
4. 10.1016/j.cub.2014.10.02510.1016/j.cub.2014.10.025 ↗
5. e)E.Warrant Cambridge University Press 2006;
6. 10.3389/fncir.2018.0001610.3389/fncir.2018.00016 ↗
7. 10.1111/ahe.1243210.1111/ahe.12432 ↗
8. 10.3390/rs1508210610.3390/rs15082106 ↗
9. 10.1038/275127a010.1038/275127a0 ↗
10. 10.1016/S0042-6989(99)00158-310.1016/S0042-6989(99)00158-3 ↗
11. 10.1126/science.111524810.1126/science.1115248 ↗
12. 10.1038/nature0502410.1038/nature05024 ↗
13. 10.1002/adfm.20170520210.1002/adfm.201705202 ↗
14. 10.1038/nature1208310.1038/nature12083 ↗
15. 10.1126/science.112305310.1126/science.1123053 ↗
16. 10.1002/lpor.20220053510.1002/lpor.202200535 ↗
17. 10.1038/s41528-023-00262-310.1038/s41528-023-00262-3 ↗
18. 10.1038/s41565-019-0501-310.1038/s41565-019-0501-3 ↗
19. 10.1038/s41377-023-01079-510.1038/s41377-023-01079-5 ↗
20. 10.1038/s41928-020-0429-510.1038/s41928-020-0429-5 ↗
21. 10.1126/sciadv.adk386010.1126/sciadv.adk3860 ↗
22. 10.1038/s41928-022-00713-110.1038/s41928-022-00713-1 ↗
23. 10.1038/s41467-017-01824-610.1038/s41467-017-01824-6 ↗
24. 10.1038/s41467-023-40925-310.1038/s41467-023-40925-3 ↗
25. 10.1073/pnas.050189110210.1073/pnas.0501891102 ↗
26. 10.1016/S0925-5214(02)00066-210.1016/S0925-5214(02)00066-2 ↗
27. 10.1016/j.foodchem.2017.09.10510.1016/j.foodchem.2017.09.105 ↗
28. 10.3390/s2013372310.3390/s20133723 ↗
29. 10.1002/jrs.647110.1002/jrs.6471 ↗
30. 10.1021/acs.jpcb.7b0481410.1021/acs.jpcb.7b04814 ↗
31. 10.1002/advs.20230432010.1002/advs.202304320 ↗
32. 10.1002/978111865312810.1002/9781118653128 ↗
33. 10.1016/j.crfs.2019.11.00110.1016/j.crfs.2019.11.001 ↗
34. Health Functional Food Code from Ministry of Food and Drug Safety. MFDS Ed2023.
35. Gaurav S. · 2003
36. 10.1016/j.snb.2023.13344210.1016/j.snb.2023.133442 ↗
37. 10.3390/app1305298810.3390/app13052988 ↗
38. Rush A. · J. Soc. Photograph. Sci. Technol. Japan · 2003 · 66 · 57