Reconstruction of scene using corneal reflection
Corneal reflection extracted from an eye image identifies the relationship between the subject of the image and the scene in front of the subject. The reconstructed scene from corneal reflection provides detailed information about the environment opposite to the subject. It also provides scrutiny ab...
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| Published in | Multimedia tools and applications Vol. 80; no. 14; pp. 21363 - 21379 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.06.2021
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1380-7501 1573-7721 |
| DOI | 10.1007/s11042-020-10409-3 |
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| Abstract | Corneal reflection extracted from an eye image identifies the relationship between the subject of the image and the scene in front of the subject. The reconstructed scene from corneal reflection provides detailed information about the environment opposite to the subject. It also provides scrutiny about any critical scenario, a subject is encountered with. This research area has significant applications in computer vision, human-computer interaction, psychology, and image forgery detection. Digital image processing and computer vision techniques have been used to reconstruct the scene from cornea image. The proposed model involved the following steps, i.e., identification of the corneal area in an eye, unnecessary reflection removal from cornea surface, developing eye geometric model to correct the spherical effect of the eye, and implementation of super-resolution (SR) algorithm to reconstruct the lost visual information present in the environment. The proposed study is able to reconstruct the SR scene image from cornea image. The effectiveness of the study is evaluated by using subjective as well as objective evaluation measures. Some useful insights related to cornea reflection construction have been described to make this study more effective. |
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| AbstractList | Corneal reflection extracted from an eye image identifies the relationship between the subject of the image and the scene in front of the subject. The reconstructed scene from corneal reflection provides detailed information about the environment opposite to the subject. It also provides scrutiny about any critical scenario, a subject is encountered with. This research area has significant applications in computer vision, human-computer interaction, psychology, and image forgery detection. Digital image processing and computer vision techniques have been used to reconstruct the scene from cornea image. The proposed model involved the following steps, i.e., identification of the corneal area in an eye, unnecessary reflection removal from cornea surface, developing eye geometric model to correct the spherical effect of the eye, and implementation of super-resolution (SR) algorithm to reconstruct the lost visual information present in the environment. The proposed study is able to reconstruct the SR scene image from cornea image. The effectiveness of the study is evaluated by using subjective as well as objective evaluation measures. Some useful insights related to cornea reflection construction have been described to make this study more effective. |
| Author | Bajwa, Usama Ijaz Gilanie, Ghulam Rafiq, Maimoona Anwar, Waqas |
| Author_xml | – sequence: 1 givenname: Maimoona surname: Rafiq fullname: Rafiq, Maimoona organization: Department of Computer Science, COMSATS University Islamabad, Lahore Campus – sequence: 2 givenname: Usama Ijaz orcidid: 0000-0001-5755-1194 surname: Bajwa fullname: Bajwa, Usama Ijaz email: usamabajwa@cuilahore.edu.pk organization: Department of Computer Science, COMSATS University Islamabad, Lahore Campus – sequence: 3 givenname: Ghulam surname: Gilanie fullname: Gilanie, Ghulam organization: Department of Computer Science, COMSATS University Islamabad, Lahore Campus – sequence: 4 givenname: Waqas surname: Anwar fullname: Anwar, Waqas organization: Department of Computer Science, COMSATS University Islamabad, Lahore Campus |
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| Cites_doi | 10.1007/978-3-319-10593-2_13 10.1007/978-3-030-01249-6_29 10.1007/978-3-030-22649-7_31 10.1109/CVPRW.2017.151 10.5244/C.26.22 10.2197/ipsjtcva.5.1 10.1109/CVPR.2018.00503 10.1109/ICME.2019.00222 10.1364/JOSAA.33.002264 10.1587/transinf.2017MVP0020 10.1109/CVPR.2019.00837 10.1007/978-3-319-54187-7_3 10.1002/9781118976005.ch17 10.1007/978-3-030-01219-9_40 10.1145/3338286.3344388 10.1007/978-3-642-33709-3_12 10.1109/CVPRW.2017.150 10.1109/CVPR.2017.19 10.1109/CVPR.2019.00174 10.1109/TIP.2012.2214050 10.1371/journal.pone.0083325 10.1007/s11263-006-6274-9 10.1109/CVPR.2016.207 |
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| Keywords | Cornea reflection Super resolution Scene reconstruction Generative adversarial network |
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| References | Kumar R (2019) Eye protection department. Arovision pharma a ray of hope to enhance vision. https://arovisionpharma.com/childcare-department/. Accessed 22 December 2020 Helland T (2013) A simple algorithm for correcting lens distortion. Tanner Helland. https://tannerhelland.com/2013/02/11/simple-algorithm-correcting-lens-distortion.html. Accessed 15 July 2019 MacKenzie I (2018) The Wiley handbook of human computer interaction. In: Chapter Fitts’ Law. John Wiley & Sons, Ltd, Chichester, UK Wei K, Yang J, Fu Y, Wipf D, Huang H (2019) Single image reflection removal exploiting misaligned training data and network enhancements. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8178–8187 Yang J, Gong D, Liu L, Shi Q (2018) Seeing deeply and bidirectionally: A deep learning approach for single image reflection removal. In: Proceedings of the european conference on computer vision (ECCV), pp 654–669 NishinoKNayarSKCorneal imaging system: Environment from eyesInt J Comput Vis2006701234010.1007/s11263-006-6274-9 Nakazawa A, Nitschke C (2012) Point of gaze estimation through corneal surface reflection in an active illumination environment. In: European conference on computer vision. Springer, pp 159–172 Agustsson E, Timofte R (2017) Ntire 2017 challenge on single image super-resolution: Dataset and study. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 126–135 Nitschke C, Nakazawa A (2012) Super-resolution from corneal images. In: BMVC, pp 1–12 Rong J, Huang S, Shang Z, Ying X (2016) Radial lens distortion correction using convolutional neural networks trained with synthesized images. In: Asian conference on computer vision. Springer, pp 35–49. Numakura K, Takemura K (2019) Indoor human localization based on the corneal reflection of illumination. In: Proceedings of the 21st international conference on human-computer interaction with mobile devices and services, pp 1–6 Raza MF (2009) For visions come not to polluted eyes, flickr. https://www.flickr.com/photos/m-f-raza/4205563005/. Accessed 10 June 2019 NakazawaANitschkeCNishidaTRegistration of eye reflection and scene images using an aspherical eye modelJOSA A201633112264227610.1364/JOSAA.33.002264 Xue Z, Xue N, Xia GS, Shen W (2019) Learning to calibrate straight lines for fisheye image rectification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1643–1651 OgawaTNakazawaANishidaTPoint of gaze estimation using corneal surface reflection and omnidirectional camera imageIEICE Trans Inf Syst201810151278128710.1587/transinf.2017MVP0020 Wang C, Wan R, Gao F, Shi B, Duan LY (2019) Learning to remove reflections for text images. In: 2019 IEEE international conference on multimedia and expo (ICME). IEEE, pp. 1276–1281 JenkinsRKerrCIdentifiable images of bystanders extracted from corneal reflectionsPloS ONE2013812e8332510.1371/journal.pone.0083325 Shi W, Caballero J, Huszár F, Totz J, Aitken AP, Bishop R, Rueckert D, Wang Z (2016) Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1874–1883 NitschkeCNakazawaATakemuraHCorneal imaging revisited: An overview of corneal reflection analysis and applicationsIPSJ Trans Comput Vision Appl2013511810.2197/ipsjtcva.5.1 Pedersen SJK (2007) Circular hough transform, Aalborg University, vision, graphics, and interactive systems. In Zhang X, Ng R, Chen Q (2018) Single image reflection separation with perceptual losses. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4786–4794 Ledig C, Theis L, Huszár F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4681–4690 Lim B, Son S, Kim H, Nah S, Mu Lee K (2017) Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 136–144 Dong C, Loy CC, He K, Tang X (2014) Learning a deep convolutional network for image super-resolution. In: European conference on computer vision. Springer, pp 184–199 Yin X, Wang X, Yu J, Zhang M, Fua P, Tao D (2018) Fisheyerecnet: A multi-context collaborative deep network for fisheye image rectification. In: Proceedings of the european conference on computer vision (ECCV), pp. 469–484 Nagamatsu T, Hiroe M, Rigoll G (2019) Corneal-reflection-based wide range gaze tracking for a car. In: International conference on human-computer interaction. Springer, pp 385-400 MittalAMoorthyAKBovikACNo-reference image quality assessment in the spatial domainIEEE Trans Image Process2012211246954708300114510.1109/TIP.2012.2214050 10409_CR10 A Nakazawa (10409_CR12) 2016; 33 R Jenkins (10409_CR4) 2013; 8 10409_CR19 K Nishino (10409_CR13) 2006; 70 A Mittal (10409_CR9) 2012; 21 10409_CR14 10409_CR11 10409_CR18 10409_CR16 T Ogawa (10409_CR17) 2018; 101 10409_CR20 10409_CR8 10409_CR21 10409_CR5 10409_CR7 10409_CR6 10409_CR1 10409_CR3 10409_CR2 C Nitschke (10409_CR15) 2013; 5 10409_CR24 10409_CR25 10409_CR22 10409_CR23 10409_CR26 10409_CR27 |
| References_xml | – reference: Ledig C, Theis L, Huszár F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4681–4690 – reference: Pedersen SJK (2007) Circular hough transform, Aalborg University, vision, graphics, and interactive systems. In – reference: NitschkeCNakazawaATakemuraHCorneal imaging revisited: An overview of corneal reflection analysis and applicationsIPSJ Trans Comput Vision Appl2013511810.2197/ipsjtcva.5.1 – reference: Zhang X, Ng R, Chen Q (2018) Single image reflection separation with perceptual losses. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4786–4794 – reference: Helland T (2013) A simple algorithm for correcting lens distortion. Tanner Helland. https://tannerhelland.com/2013/02/11/simple-algorithm-correcting-lens-distortion.html. Accessed 15 July 2019 – reference: Nakazawa A, Nitschke C (2012) Point of gaze estimation through corneal surface reflection in an active illumination environment. In: European conference on computer vision. Springer, pp 159–172 – reference: MittalAMoorthyAKBovikACNo-reference image quality assessment in the spatial domainIEEE Trans Image Process2012211246954708300114510.1109/TIP.2012.2214050 – reference: Kumar R (2019) Eye protection department. Arovision pharma a ray of hope to enhance vision. https://arovisionpharma.com/childcare-department/. Accessed 22 December 2020 – reference: Nagamatsu T, Hiroe M, Rigoll G (2019) Corneal-reflection-based wide range gaze tracking for a car. In: International conference on human-computer interaction. Springer, pp 385-400 – reference: Dong C, Loy CC, He K, Tang X (2014) Learning a deep convolutional network for image super-resolution. In: European conference on computer vision. Springer, pp 184–199 – reference: OgawaTNakazawaANishidaTPoint of gaze estimation using corneal surface reflection and omnidirectional camera imageIEICE Trans Inf Syst201810151278128710.1587/transinf.2017MVP0020 – reference: Agustsson E, Timofte R (2017) Ntire 2017 challenge on single image super-resolution: Dataset and study. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 126–135 – reference: JenkinsRKerrCIdentifiable images of bystanders extracted from corneal reflectionsPloS ONE2013812e8332510.1371/journal.pone.0083325 – reference: Shi W, Caballero J, Huszár F, Totz J, Aitken AP, Bishop R, Rueckert D, Wang Z (2016) Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1874–1883 – reference: Wei K, Yang J, Fu Y, Wipf D, Huang H (2019) Single image reflection removal exploiting misaligned training data and network enhancements. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8178–8187 – reference: Yang J, Gong D, Liu L, Shi Q (2018) Seeing deeply and bidirectionally: A deep learning approach for single image reflection removal. In: Proceedings of the european conference on computer vision (ECCV), pp 654–669 – reference: Raza MF (2009) For visions come not to polluted eyes, flickr. https://www.flickr.com/photos/m-f-raza/4205563005/. Accessed 10 June 2019 – reference: NakazawaANitschkeCNishidaTRegistration of eye reflection and scene images using an aspherical eye modelJOSA A201633112264227610.1364/JOSAA.33.002264 – reference: Rong J, Huang S, Shang Z, Ying X (2016) Radial lens distortion correction using convolutional neural networks trained with synthesized images. In: Asian conference on computer vision. Springer, pp 35–49. – reference: Nitschke C, Nakazawa A (2012) Super-resolution from corneal images. In: BMVC, pp 1–12 – reference: Lim B, Son S, Kim H, Nah S, Mu Lee K (2017) Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 136–144 – reference: Numakura K, Takemura K (2019) Indoor human localization based on the corneal reflection of illumination. In: Proceedings of the 21st international conference on human-computer interaction with mobile devices and services, pp 1–6 – reference: NishinoKNayarSKCorneal imaging system: Environment from eyesInt J Comput Vis2006701234010.1007/s11263-006-6274-9 – reference: Xue Z, Xue N, Xia GS, Shen W (2019) Learning to calibrate straight lines for fisheye image rectification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1643–1651 – reference: MacKenzie I (2018) The Wiley handbook of human computer interaction. In: Chapter Fitts’ Law. John Wiley & Sons, Ltd, Chichester, UK – reference: Wang C, Wan R, Gao F, Shi B, Duan LY (2019) Learning to remove reflections for text images. In: 2019 IEEE international conference on multimedia and expo (ICME). IEEE, pp. 1276–1281 – reference: Yin X, Wang X, Yu J, Zhang M, Fua P, Tao D (2018) Fisheyerecnet: A multi-context collaborative deep network for fisheye image rectification. In: Proceedings of the european conference on computer vision (ECCV), pp. 469–484 – ident: 10409_CR2 doi: 10.1007/978-3-319-10593-2_13 – ident: 10409_CR26 doi: 10.1007/978-3-030-01249-6_29 – ident: 10409_CR10 doi: 10.1007/978-3-030-22649-7_31 – ident: 10409_CR18 – ident: 10409_CR7 doi: 10.1109/CVPRW.2017.151 – ident: 10409_CR5 – ident: 10409_CR14 doi: 10.5244/C.26.22 – ident: 10409_CR3 – volume: 5 start-page: 1 year: 2013 ident: 10409_CR15 publication-title: IPSJ Trans Comput Vision Appl doi: 10.2197/ipsjtcva.5.1 – ident: 10409_CR27 doi: 10.1109/CVPR.2018.00503 – ident: 10409_CR22 doi: 10.1109/ICME.2019.00222 – volume: 33 start-page: 2264 issue: 11 year: 2016 ident: 10409_CR12 publication-title: JOSA A doi: 10.1364/JOSAA.33.002264 – volume: 101 start-page: 1278 issue: 5 year: 2018 ident: 10409_CR17 publication-title: IEICE Trans Inf Syst doi: 10.1587/transinf.2017MVP0020 – ident: 10409_CR23 doi: 10.1109/CVPR.2019.00837 – ident: 10409_CR20 doi: 10.1007/978-3-319-54187-7_3 – ident: 10409_CR8 doi: 10.1002/9781118976005.ch17 – ident: 10409_CR25 doi: 10.1007/978-3-030-01219-9_40 – ident: 10409_CR16 doi: 10.1145/3338286.3344388 – ident: 10409_CR11 doi: 10.1007/978-3-642-33709-3_12 – ident: 10409_CR1 doi: 10.1109/CVPRW.2017.150 – ident: 10409_CR6 doi: 10.1109/CVPR.2017.19 – ident: 10409_CR24 doi: 10.1109/CVPR.2019.00174 – volume: 21 start-page: 4695 issue: 12 year: 2012 ident: 10409_CR9 publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2012.2214050 – volume: 8 start-page: e83325 issue: 12 year: 2013 ident: 10409_CR4 publication-title: PloS ONE doi: 10.1371/journal.pone.0083325 – volume: 70 start-page: 23 issue: 1 year: 2006 ident: 10409_CR13 publication-title: Int J Comput Vis doi: 10.1007/s11263-006-6274-9 – ident: 10409_CR19 – ident: 10409_CR21 doi: 10.1109/CVPR.2016.207 |
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| SubjectTerms | Algorithms Computer Communication Networks Computer Science Computer vision Cornea Data Structures and Information Theory Digital computers Digital imaging Evaluation Image processing Image reconstruction Multimedia Information Systems Psychology Special Purpose and Application-Based Systems |
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| Title | Reconstruction of scene using corneal reflection |
| URI | https://link.springer.com/article/10.1007/s11042-020-10409-3 https://www.proquest.com/docview/2531845466 |
| Volume | 80 |
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