{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T16:18:11Z","timestamp":1768925891963,"version":"3.49.0"},"reference-count":43,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2019,7,31]],"date-time":"2019-07-31T00:00:00Z","timestamp":1564531200000},"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>This paper proposes a self-calibration method that can be applied for multiple larger field-of-view (FOV) camera models on an advanced driver-assistance system (ADAS). Firstly, the proposed method performs a series of pre-processing steps such as edge detection, length thresholding, and edge grouping for the segregation of robust line candidates from the pool of initial distortion line segments. A novel straightness cost constraint with a cross-entropy loss was imposed on the selected line candidates, thereby exploiting that novel loss to optimize the lens-distortion parameters using the Levenberg\u2013Marquardt (LM) optimization approach. The best-fit distortion parameters are used for the undistortion of an image frame, thereby employing various high-end vision-based tasks on the distortion-rectified frame. In this study, an investigation was carried out on experimental approaches such as parameter sharing between multiple camera systems and model-specific empirical    \u03b3   -residual rectification factor. The quantitative comparisons were carried out between the proposed method and traditional OpenCV method as well as contemporary state-of-the-art self-calibration techniques on KITTI dataset with synthetically generated distortion ranges. Famous image consistency metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and position error in salient points estimation were employed for the performance evaluations. Finally, for a better performance validation of the proposed system on a real-time ADAS platform, a pragmatic approach of qualitative analysis has been conducted through streamlining high-end vision-based tasks such as object detection, localization, and mapping, and auto-parking on undistorted frames.<\/jats:p>","DOI":"10.3390\/s19153369","type":"journal-article","created":{"date-parts":[[2019,7,31]],"date-time":"2019-07-31T11:37:07Z","timestamp":1564573027000},"page":"3369","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Feasible Self-Calibration of Larger Field-of-View (FOV) Camera Sensors for the Advanced Driver-Assistance System (ADAS)"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4165-0021","authenticated-orcid":false,"given":"Vijay","family":"Kakani","sequence":"first","affiliation":[{"name":"Information and Communication Engineering, Inha University, 100 Inharo, Nam-gu Incheon 22212, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4232-3804","authenticated-orcid":false,"given":"Hakil","family":"Kim","sequence":"additional","affiliation":[{"name":"Information and Communication Engineering, Inha University, 100 Inharo, Nam-gu Incheon 22212, Korea"}]},{"given":"Mahendar","family":"Kumbham","sequence":"additional","affiliation":[{"name":"Valeo Vision Systems, Dunmore Road, Tuam, Co. Galway H54, Ireland"}]},{"given":"Donghun","family":"Park","sequence":"additional","affiliation":[{"name":"Information and Communication Engineering, Inha University, 100 Inharo, Nam-gu Incheon 22212, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8486-5738","authenticated-orcid":false,"given":"Cheng-Bin","family":"Jin","sequence":"additional","affiliation":[{"name":"Information and Communication Engineering, Inha University, 100 Inharo, Nam-gu Incheon 22212, Korea"}]},{"given":"Van Huan","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ziebinski, A., Cupek, R., Erdogan, H., and Waechter, S. (2016). A survey of ADAS technologies for the future perspective of sensor fusion. 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