Open Access
Wuhan Univ. J. Nat. Sci.
Volume 28, Number 2, April 2023
Page(s) 150 - 162
Published online 23 May 2023
  1. Lee J, Cho C W, Shin K Y, et al. 3D gaze tracking method using Purkinje images on eye optical model and pupil[J]. Optics and Lasers in Engineering, 2012, 50(5): 736-751. [NASA ADS] [CrossRef] [Google Scholar]
  2. Biondi F, Rossi R, Gastaldi M, et al. Beeping ADAS: Reflexive effect on drivers' behavior[J]. Transportation Research Part F Psychology & Behaviour, 2014, 25: 27-33. [Google Scholar]
  3. D'Amato A, Pianese C, Arsie I, et al. Development and on-board testing of an ADAS-based methodology to enhance cruise control features towards CO2 reduction [C]// IEEE International Conference on Models and Technologies for Intelligent Transportation Systems.New York:IEEE, 2017: 503-508. [Google Scholar]
  4. Wang W D, Xin B J, Deng N, et al. Single vision based identification of yarn hairiness using adaptive threshold and image enhancement method [J]. Journal of the International Measurement Confederation, 2018, 128: 220-230. [NASA ADS] [CrossRef] [Google Scholar]
  5. Yan Z, Zhang J, Yang Z, et al. Kapur's entropy for underwater multilevel thresholding image segmentation based on whale optimization algorithm[J]. IEEE Access, 2021, 9: 41294-41319. [CrossRef] [Google Scholar]
  6. Kumar V, Lal T, Dhuliya P, et al. A study and comparison of different image segmentation algorithms[C]// Proceedings of the 2016 2nd International Conference on Advances in Computing, Communication, & Automation (ICACCA). New York: IEEE, 2016. [Google Scholar]
  7. Yan S J, Tang G A, Li F Y, et al. An edge detection based method for extraction of loess shoulder-line from grid DEM[J]. Geomatics and Information Science of Wuhan University, 2011, 36 (3): 363-367. [Google Scholar]
  8. Zhou W, Du X, Wang S. Techniques for image segmentation based on edge detection[C]// 2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology. New York:IEEE, 2021: 400-403. [Google Scholar]
  9. Sun Q D, Qiao Y M, Wu H, et al. An edge detection method based on adjacent dispersion[J]. International Journal of Pattern Recognition and Artificial Intelligence, 2016, 30(10): 943-951. [Google Scholar]
  10. Guan W L, Wang T T, Qi J Q, et al. Edge-aware convolution neural network based salient object detection[J]. IEEE Signal Processing Letters, 2019, 26 (1): 114-118. [NASA ADS] [CrossRef] [Google Scholar]
  11. Yang Y F, Peng H , Jiang Y, et al. A region-based image segmentation method under P systems [J]. Journal of Information and Computational Science, 2013, 10 (10): 2943-2950. [Google Scholar]
  12. Xu S Y, Peng C L,Chen K, et al. Measurement method of wheat stalks cross section parameters based on sector ring region image segmentation[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(4): 53-59. [Google Scholar]
  13. Chen Y L , Ma Y D ,Kim D H, et al. Region-based object recognition by color segmentation using a simplified PCNN[J]. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26 (8): 1682-1697. [Google Scholar]
  14. Su L, Fu X J, Zhang X D, et al. Delineation of carpal bones from hand X-ray images through prior model, and integration of region-based and boundary-based segmentations[J]. IEEE Access, 2018, 6: 19993-20008. [CrossRef] [Google Scholar]
  15. Wang H, Wang Y, Zhao X, et al. Lane detection of curving road for structural highway with straight-curve model on vision[C]// IEEE Transactions on Vehicular Technology, 2019, 68(6): 5321-5330. [Google Scholar]
  16. AI-Kofahi Y, Zaltsman A, Graves R, et al. A deep learning-based algorithm for 2-D cell segmentation in microscopy images[J]. BMC Bioinformatics, 2018, 19 (1):6-8. [Google Scholar]
  17. Li L H, Qian B, Lian J, et al. Traffic scene segmentation based on RGB-D image and deep learning[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19 (5) :1664-1669. [CrossRef] [Google Scholar]
  18. Garcia-Garcia A, Orts-Escolano S, Oprea S, et al. A survey on deep learning techniques for image and video semantic segmentation[J]. Applied Soft Computing Journal, 2018, 70: 41-65. [Google Scholar]
  19. Chen L C, Papandreou G, Kokkinos I, et al. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40 (4): 834-848. [CrossRef] [PubMed] [Google Scholar]
  20. Lu R T, Shen T, Yang X G, et al. Infrared dim moving target detection algorithm assisted by incremental inertial navigation information in highdynamic air to ground background (Invited)[J]. Infrared and Laser Engineering, 2022, 51 (4):50-60. [Google Scholar]
  21. Mammeri A, Zuo T, Boukerche A. Extending the detection range of vision-based vehicular instrumentation[J]. IEEE Transactions on Instrumentation and Measurement, 2016, 65 (4): 856-873. [NASA ADS] [CrossRef] [Google Scholar]
  22. Li J N, Liang X D, Shen S M, et al. Scale-aware fast R-CNN for pedestrian detection[J]. IEEE Transactions on Multimedia, 2018, 20 (4): 985-996. [Google Scholar]
  23. Long Z R, Wei B, Feng P, et al. A fully convolutional networks (FCN) based image segmentation algorithm in binocular imaging system[C]// International Conference on Optical Instruments and Technology, 2018: 10621. [Google Scholar]
  24. Badrinarayanan V, Kendall A, Cipolla R. Segnet: A deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(12): 2481-2495. [CrossRef] [PubMed] [Google Scholar]
  25. Gan J H, Zhang R Q. Ultrasound image segmentation algorithm of thyroid nodules based on improved U-Net network[C]// Proceedings of the 2022 3rd International Conference on Control, Robotics and Intelligent System (CCRIS '22). New York: Association for Computing Machinery, 2022: 61-66. [Google Scholar]
  26. Qian H, Ma Y, Chen W, et al. Traffic signs detection and segmentation based on the improved mask R-CNN[C]//2021 40th Chinese Control Conference (CCC). New York: Association for Computing Machinery, 2021: 8241-8246. [Google Scholar]
  27. Wang H F, Shan Y H, Hao T, et al. Vehicle-road environment perception under low-visibility condition based on polarization features via deep learning[C]// IEEE Transactions on Intelligent Transportation Systems, 2022, 23(10): 17873-17886. [Google Scholar]
  28. Mittal A, Hooda R, Sofat S. LF-SegNet: A fully convolutional encoder-decoder network for segmenting lung fields from chest radiographs,wireless personal communications[J] Wireless Personal Communications, 2018, 101(1): 511-529. [CrossRef] [Google Scholar]
  29. Shan J C, Li X Z, Song M, et al. Semantic segmentation based on deep convolution neural network[C]// Journal of Physics: Conference Series, 2018, 1069(1): 012169. [Google Scholar]
  30. Gadde R, Jampani V, Kiefel M, et al. Super pixel convolutional networks using bilateral inceptions[C]// Lecture Notes in Computer Science. Berlin: Springer-Verlag, 2016, 9905: 597-613. [Google Scholar]
  31. Wu F Y. The Potts model[J]. Reviews of Modern Physics,1982, 54(1): 235-268. [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]

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