Optimization Method for Infrared Eye Movement Image Segmentation

Chang Li; Yu Liu; Jinglin Sun

doi:10.3788/LOP202259.0210016

[1] Zhang X B, Yuan S M. An eye tracking analysis for video advertising: relationship between advertisement elements and effectiveness[J]. IEEE Access, 6, 10699-10707(2018).

[2] Ramot M, Walsh C, Reimann G E et al. Distinct neural mechanisms of social orienting and mentalizing revealed by independent measures of neural and eye movement typicality[J]. Communications Biology, 3, 48(2020).

[3] Anderson T J, MacAskill M R. Eye movements in patients with neurodegenerative disorders[J]. Nature Reviews Neurology, 9, 74-85(2013).

[4] Wang X T, Yu K, Wu S X et al. ESRGAN: enhanced super-resolution generative adversarial networks[M]. Leal-Taixé L, Roth S. Computer vision-ECCV 2018 workshops. Lecture notes in computer science, 11133, 63-79(2019).

[5] Li Y, Qi H Z, Dai J F et al. Fully convolutional instance-aware semantic segmentation[C], 4438-4446(2017).

[6] Zhang X F, Liu J, Shi Z S et al. Review of deep learning-based semantic segmentation[J]. Laser & Optoelectronics Progress, 56, 150003(2019).

[7] Zhang Z H, Fang W, Du L L et al. Semantic segmentation of remote sensing image based on encoder-decoder convolutional neural network[J]. Acta Optica Sinica, 40, 0310001(2020).

[8] Cai Y, Huang X G, Zhang Z A et al. Real-time semantic segmentation algorithm based on feature fusion technology[J]. Laser & Optoelectronics Progress, 57, 021011(2020).

[9] Garbin S J, Shen Y R, Schuetz I et al. OpenEDS: open eye dataset[EB/OL]. https:∥arxiv.org/abs/1905.03702

[10] Perry J, Fernandez A. MinENet: a dilated CNN for semantic segmentation of eye features[C], 3671-3676(2019).

[11] Valenzuela A, Arellano C, Tapia J. An efficient dense network for semantic segmentation of eyes images captured with virtual reality lens[C], 28-34(2019).

[12] Chaudhary A K, Kothari R, Acharya M et al. RITnet: real-time semantic segmentation of the eye for gaze tracking[C], 3698-3702(2019).

[13] Milletari F, Navab N, Ahmadi S A. V-Net: fully convolutional neural networks for volumetric medical image segmentation[C], 565-571(2016).

[14] Sudre C H, Li W Q, Vercauteren T et al. Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations[M]. Cardoso M J, Arbel T, Carneiro G, et al. Deep learning in medical image analysis and multimodal learning for clinical decision support. Lecture notes in computer science, 10553, 240-248(2017).

[15] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation[M]. Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science, 9351, 234-241(2015).

[16] Kervadec H, Bouchtiba J, Desrosiers C et al. Boundary loss for highly unbalanced segmentation[C], 102, 285-296(2019).