Skip to main content
SpringerLink
Log in

The Vision–Brain Hypothesis

  • Chapter
  • First Online:
Brain-Inspired Intelligence and Visual Perception

Part of the book series: Research on Intelligent Manufacturing ((REINMA))

Abstract

In this chapter, the vision–brain hypothesis is illustrated in three steps. First, we hypothesize that vision decides the robots’ attention and the attention could be regulated without brain-inspired objects detection and tracking. This could be highlighted by the difference in attention mechanisms between the manned and unmanned systems . Regulated attention in unmanned systems has a significant implication to the robots’ cognition accuracy and response speed. Therefore, the current learning systems must be optimized. Such optimization can be interpreted as an integration of deep learning with other hybrid adaptive algorithms . Second, we hypothesize that if a region of interest had been located by a “vision–brain,” then scene understanding and partition could be smoothly carried out, which help cognition systems to reduce the loss from mid-aligned samples addition by employing non-adaptive random projections instead of self-taught learning . Third, we hypothesize that cognition rates of the “vision–brain” could approach to 100%, which finally establishes the robustness and efficiency of the vision–brain. A broad learning system (BLS) was integrated with a decision layer (the vision–brain) to address the issue whether face recognition rates can reach 100%. And in the next chapter, we will show that face recognition rates can reach 100% in BLS with the vision–brain, as verified by a challenging AR database with real occlusion . BLS performance in face recognition on other bigger databases remains unknown and worthy of further attempts.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. S. Medasani, Y. Owechko, Evolutionary optimization and graphical models for robust recognition of behaviors in video imagery. Proc. SPIE—Int. Soc. Opt. Eng. 12(3), 361–371 (2007)

    Google Scholar 

  2. O.P. Popoola, K. Wang, Video-based abnormal human behavior recognition—a review. IEEE Trans. Syst. Man Cybern. Part C 42(6), 865–878 (2012)

    Article  Google Scholar 

  3. T. Huynh-The, O. Banos, B.V. Le et al., Traffic behavior recognition using the pachinko allocation model. Sensors 15(7), 16040–16059 (2015)

    Article  Google Scholar 

  4. R.T. Collins, Y. Liu, Online selection of discriminative tracking features. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1631–1643 (2005)

    Article  Google Scholar 

  5. K. Huang, T. Tan, Vs-star: a visual interpretation system for visual surveillance. Pattern Recogn. Lett. 31(14), 2265–2285 (2010)

    Article  Google Scholar 

  6. L. Jing, Incremental learning for robust visual tracking. Int. J. Comput. Vision 77(1–3), 125–141 (2008)

    Google Scholar 

  7. M.A.A. Dewan, E. Granger, G.L. Marcialis, et al., Adaptive appearance model tracking for still-to-video face recognition. Pattern Recog. 49(C), 129–151 (2016)

    Article  Google Scholar 

  8. B. Babenko, M.H. Yang, S. Belongie, Robust object tracking with online multiple instance learning. IEEE Trans. Pattern Anal. Mach. Intell. 33(8), 1619–1632 (2011)

    Article  Google Scholar 

  9. Y. Wu, N. Jia, J. Sun, Real-time multi-scale tracking based on compressive sensing. Visual Comput. Int. J. Comput. Graph. 31(4), 471–484 (2015)

    Google Scholar 

  10. X. Mei, H. Ling, Robust visual tracking and vehicle classification via sparse representation. IEEE Trans. Softw. Eng. 33(11), 2259–2272 (2011)

    Google Scholar 

  11. N. Ovcharova, F. Gauterin, Assessment of an adaptive predictive collision warning system based on driver’s attention detection. Clin. Exp. Metas. 8(2), 215–224 (2012)

    Google Scholar 

  12. A. Finn, K. Rogers, Accuracy requirements for unmanned aerial vehicle-based acoustic atmospheric tomography. J. Acoust. Soc. Am. 139(4), 2097 (2016)

    Article  Google Scholar 

  13. J. Chen, X. Zhang, B. Xin et al., Coordination between unmanned aerial and ground vehicles: a taxonomy and optimization perspective. IEEE Trans. Cybern. 46(4), 959–972 (2016)

    Article  Google Scholar 

  14. Z. Zheng, Y. Liu, X. Zhang, The more obstacle information sharing, the more effective real-time path planning? Knowl. Based Syst. 114, 36–46 (2016)

    Article  Google Scholar 

  15. M.W. Whalen, D. Cofer, A. Gacek, Requirements and architectures for Secure Vehicles. IEEE Softw. 33(4), 22–25 (2016)

    Article  Google Scholar 

  16. R. Czyba, G. Szafrański, A. Ryś, Design and control of a single tilt tri-rotor aerial vehicle. J. Intell. Robot. Syst. 1–14 (2016)

    Google Scholar 

  17. X. Zhang, H. Duan, An improved constrained differential evolution algorithm for unmanned aerial vehicle global route planning. Appl. Soft Comput. 26(C), 270–284 (2015)

    Article  Google Scholar 

  18. G. Mati, M. Jankovec M, D. Jurman, et al., Feasibility study of attitude determination for all-rotating unmanned aerial vehicles in steady flight. J. Intell. Robot. Syst. 80(2), 341–360 (2015)

    Google Scholar 

  19. J.G. Lee, K.J. Kim, S. Lee et al., Can autonomous vehicles be safe and trustworthy? Effects of appearance and autonomy of unmanned driving systems. Int. J. Hum. Comput. Interact. 31(10), 682–691 (2015)

    Article  MathSciNet  Google Scholar 

  20. J. Han, J. Park, T. Kim et al., Precision navigation and mapping under bridges with an unmanned surface vehicle. Auton. Robots 38(4), 1–14 (2015)

    Article  Google Scholar 

  21. J.L. Crespo, A. Faiña, R.J. Duro, An adaptive detection/attention mechanism for real time robot operation. Neurocomputing 72(4–6), 850–860 (2009)

    Article  Google Scholar 

  22. Barbara Webb, Computational intelligence: from natural to artificial systems. Connection Sci. 14(2), 163–164 (2002)

    Article  Google Scholar 

  23. E. Bonabeau, C. Meyer, Computational intelligence. A whole new way to think about business. Harvard Bus. Rev. 79(5), 106–114 (2001)

    Google Scholar 

  24. M. Dorigo, M. Birattari, C. Blum, Ant Colony Optimization and Computational intelligence, vol. 49, no. 8 (Springer, Berlin, 1995), pp. 767–771

    Google Scholar 

  25. S. Garnier, J. Gautrais, G. Theraulaz, The biological principles of computational intelligence. Comput. Intell. 1(1), 3–31 (2007)

    Google Scholar 

  26. M. Dorigo, M. Birattari, C. Blum, et al. Ant Colony Optimization and Computational intelligence, 4th International Workshop, ANTS 2004, Brussels, Belgium, 5–8 Sept 2004, Proceedings, vol. 49, no. 8. Lecture Notes in Computer Science (2004), pp. 767–771

    Google Scholar 

  27. C.J. Wan, L.Q. Zhu, Y.H. Liu et al., Proton-conducting graphene oxide-coupled neuron transistors for brain-inspired cognitive systems. Adv. Mater. 28(3), 3557–3563 (2016)

    Article  Google Scholar 

  28. P. Gkoupidenis, D.A. Koutsouras, T. Lonjaret et al., Orientation selectivity in a multi-gated organic electrochemical transistor. Sci. Rep. 6, 27007 (2016)

    Article  Google Scholar 

  29. X. Liu, Y. Zeng, T. Zhang, et al., Parallel brain simulator: a multi-scale and parallel brain-inspired neural network modeling and simulation platform. Cogn. Comput. 1–15 (2016)

    Google Scholar 

  30. R. Velik, A Brain-inspired multimodal data mining approach for human activity recognition in elderly homes. J. Ambient Intell. Smart Environ. 6(4), 447–468 (2014)

    Google Scholar 

  31. J.J. Wong, S.Y. Cho, A brain-inspired framework for emotion recognition. Magn. Reson. Imaging 32(9), 1139–1155 (2006)

    Google Scholar 

  32. R. Kozma, W.J. Freeman, Neurodynamics of cognition and consciousness. The Workshop on PERFORMANCE Metrics for Intelligent Systems (ACM, 2009), pp. 147–148

    Google Scholar 

  33. J.J. Wong, S.Y. Cho, A local experts organization model with application to face emotion recognition. Expert Syst. Appl. 36(1), 804–819 (2009)

    Article  Google Scholar 

  34. J.G. Lee, K.J. Kim, S. Lee et al., Can autonomous vehicles be safe and trustworthy? Effects of appearance and autonomy of unmanned driving systems. Int. J. Hum. Comput. Interact. 31(10), 682–691 (2015)

    Article  MathSciNet  Google Scholar 

  35. Y. Yao, X. Xu, C. Zhu et al., A hybrid fusion algorithm for GPS/INS integration during GPS outages. Measurement 103, 42–51 (2017)

    Article  Google Scholar 

  36. Y. Chen, J. Gao, G. Yang, et al., Solving equilibrium standby redundancy optimization problem by hybrid PSO algorithm. Soft Comput. 1–15 (2017)

    Google Scholar 

  37. A.M. Durán-Rosal, M.D.L. Paz-Marín, P.A. Gutiérrez, et al., Identifying market behaviours using european stock index time series by a hybrid segmentation algorithm. Neural Process. Lett. 1–24 (2017)

    Google Scholar 

  38. P. Guo, W. Cheng, Y. Wang, Hybrid evolutionary algorithm with extreme machine learning fitness function evaluation for two-stage capacitated facility location problems. Expert Syst. Appl. 71, 57–68 (2017)

    Article  Google Scholar 

  39. F. Li, K.Y. Lam, L. Wang, Power allocation in cognitive radio networks over Rayleigh-fading channels with hybrid intelligent algorithms. Wireless Netw. 1–11 (2017)

    Google Scholar 

  40. B. Jafrasteh, N. Fathianpour, A hybrid simultaneous perturbation artificial bee colony and back-propagation algorithm for training a local linear radial basis neural network on ore grade estimation. Neurocomputing 235, 217–227 (2017)

    Article  Google Scholar 

  41. S. Yao, Z. Li, Robust tracking via locally structured representation. Int. J. Comput. Vision 1–35 (2016)

    Google Scholar 

  42. G. Han, X. Wang, J. Liu et al., Robust object tracking based on local region sparse appearance model. Neurocomputing 184, 145–167 (2016)

    Article  Google Scholar 

  43. P. Wang, W. Qian, Q. Chen, Robust visual tracking with contiguous occlusion constraint. Opt. Rev. 23(1), 40–52 (2016)

    Article  Google Scholar 

  44. S. Chen, S. Li, R. Ji, et al., Discriminative local collaborative representation for online object tracking. Knowl. Based Syst. 100(C), 13–24 (2016)

    Article  Google Scholar 

  45. E.J. Candes, T. Tao, Decoding by linear programming. IEEE Trans. Inf. Theor. 51(12), 4203–4215 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  46. D. Achlioptas, Database-friendly random projections: Johnson-Lindenstrauss with binary coins. J. Comput. Syst. Sci. 66(4), 671–687 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  47. V.S. Borkar, R. Dwivedi, N. Sahasrabudhe, Gaussian approximations in high dimensional estimation. Syst. Control Lett. 92, 42–45 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  48. L. Liu, P.W. Fieguth, Texture classification from random features. IEEE Trans. Pattern Anal. Mach. Intell. 34(3), 574–586 (2011)

    Article  Google Scholar 

  49. S. Paul, M. Magdon-Ismail, P. Drineas, Feature selection for linear SVM with provable guarantees. Pattern Recogn. 60, 205–214 (2016)

    Article  Google Scholar 

  50. C. Vondrick, A. Khosla, H. Pirsiavash et al., Visualizing object detection features. Int. J. Comput. Vision 119(2), 145–158 (2016)

    Article  MathSciNet  Google Scholar 

  51. S.Z. Li, Z.Q. Zhang, FloatBoost learning and statistical face detection. Trans. Pattern Anal. Mach. Intell. IEEE 26(9), 1112–1123 (2004)

    Article  Google Scholar 

  52. J. Romberg, Compressive sensing by random convolution. Siam J. Imaging Sci. 2(4), 1098–1128 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  53. T.T. Do, L. Gan, N.H. Nguyen et al., Fast and efficient compressive sensing using structurally random matrices. IEEE Trans. Signal Process. 60(1), 139–154 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  54. J. Romberg, Compressive Sensing by Random Convolution. Siam J. Imaging Sci. 2(4), 1098–1128 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  55. S. Osher, Y. Mao, B. Dong et al., Fast linearized bregman iteration for compressive sensing and sparse denoising. Math. Comput. 8(1), 93–111 (2011)

    MathSciNet  MATH  Google Scholar 

  56. Y. Chen, Y. Chi, Robust spectral compressed sensing via structured matrix completion. IEEE Trans. Inf. Theory 60(10), 6576–6601 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  57. J. Zhang, G. Han, Y. Fang, Deterministic construction of compressed sensing matrices from protograph ldpc codes. IEEE Signal Process. Lett. 22(11), 1960–1964 (2015)

    Article  Google Scholar 

  58. N. Eslahi, A. Aghagolzadeh, S.M.H. Andargoli, Image/video compressive sensing recovery using joint adaptive sparsity measure. Neurocomputing 200(C), 88–109 (2016)

    Article  Google Scholar 

  59. H. Jiang, W. Deng, Z. Shen, Surveillance Video Processing Using Compressive Sensing. Inverse Prob. Imaging 6(2), 201–214 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  60. H. Jiang, S. Zhao, Z. Shen et al., Surveillance video analysis using compressive sensing with low latency. Bell Labs Techn. J. 18(4), 63–74 (2014)

    Article  Google Scholar 

  61. W.F. Wang, X. Chen, H.Y. Wang et al., Locally Compressive sensing for behaviors recognition. J. Tsinghua Univ. (Sci & Technol) 24(4), 118–121 (2007)

    Google Scholar 

  62. M. Yang, L. Zhang, J. Yang, D. Zhang, Regularized robust coding for face recognition. IEEE Trans. Image Process. 22(5), 1753–1766 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  63. J. Wright, A.Y. Yang, A. Ganesh, S.S. Sastry, Y. Ma, Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)

    Article  Google Scholar 

  64. J. Očenášek, J. Schwarz, “The Parallel Bayesian Optimization Algorithm” in The State of the Art in Computational Intelligence (Physica-Verlag HD, 2000)

    Google Scholar 

  65. M. Yang, L. Zhang, Gabor feature based sparse representation for face recognition with Gabor occlusion dictionary. In Proc. Eur. Conf. Comput. Vis. 448–461 (2010)

    Google Scholar 

  66. Z. Mahmood, T. Ali, S.U. Khan, Effects of pose and image resolution on automatic face recognition. IET Biometrics 5(2), 111–119 (2017)

    Article  Google Scholar 

  67. B.K. Tripathi, On the complex domain deep machine learning for face recognition. Appl. Intell. 47(3), 1–15 (2017)

    Google Scholar 

  68. A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks, in Advances in Neural Information Processing Systems 25 (NIPS 2012), ed. by F. Pereira, C.J.C. Burges, L. Bottou, K.Q. Weinberger (Curran Associates Inc, New York, NY, USA, 2012), pp. 1097–1105

    Google Scholar 

  69. I. Goodfellow, Y. Bengio, A. Courville, Deep Learning (MIT Press, Cambridge, MA, USA, 2016)

    MATH  Google Scholar 

  70. G.E. Hinton, S. Osindero, Y.-W. Teh, A fast learning algorithm for deep belief nets. Neural Comput. 18, 0899–7667 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  71. G.E. Hinton, R.R. Salakhutdinov, Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  72. R. Salakhutdinov, G.E. Hinton, Deep boltzmann machines, in Proceedings of the AISTATS, vol. 1 (2009), p. 3

    Google Scholar 

  73. Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  74. K. Simonyan, A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition. Available: https://arxiv.org/abs/1409.1556

  75. J. Tang, C. Deng, G.-B. Huang, Extreme learning machine for multilayer perceptron. IEEE Trans. Neural Netw. Learn. Syst. 27(4), 809–821 (2016)

    Article  MathSciNet  Google Scholar 

  76. M. Gong, J. Zhao, J. Liu, Q. Miao, L. Jiao, Change detection in synthetic aperture radar images based on deep neural networks. IEEE Trans. Neural Netw. Learn. Syst. 27(1), 125–138 (2016)

    Article  MathSciNet  Google Scholar 

  77. W. Hou, X. Gao, D. Tao, X. Li, Blind image quality assessment via deep learning. IEEE Trans. Neural Netw. Learn. Syst. 26(6), 1275–1286 (2015)

    Article  MathSciNet  Google Scholar 

  78. M.M. Ghazi, H.K. Ekenel, A Comprehensive Analysis of Deep Learning Based Representation for Face Recognition. IEEE Computer Vision and Pattern Recognition Workshops (2016), pp. 102–109

    Google Scholar 

  79. K. Grm, V. Štruc, A. Artiges, M. Caron, H.K. Ekenel, Strengths and weaknesses of deep learning models for face recognition against image degradations. IET Biometrics 7(1), 81–89 (2018)

    Article  Google Scholar 

  80. J. Lezama, Q. Qiu, G. Sapiro, Not Afraid of the Dark: NIR-VIS Face Recognition via Cross-Spectral Hallucination and Low-Rank Embedding. IEEE Conference on Computer Vision and Pattern Recognition, pp. 6628–6637, 2017

    Google Scholar 

  81. M. S. Sarfraz, R. Stiefelhagen, Deep Perceptual Mapping for Cross-Modal Face Recognition. Kluwer Academic Publishers (2017)

    Google Scholar 

  82. G. Goswami, R. Bhardwaj, R. Singh, M. Vatsa, MDLFace: Memorability Augmented Deep Learning for Video Face Recognition. IEEE International Joint Conference on Biometrics (2014), pp. 1–7

    Google Scholar 

  83. P. Sharma, R.N. Yadav, K.V. Arya, Face Recognition from Video Using Generalized Mean Deep Learning Neural Network. IEEE International Symposium on Computational and Business Intelligence (2016), pp. 195–199

    Google Scholar 

  84. C.L.P. Chen, Z.L. Liu, Broad Learning system: an effective and efficient incremental learning system without the need for deep architecture. IEEE Trans. Neural Networks Learn. Syst. 29(1), 10–24 (2018)

    Article  MathSciNet  Google Scholar 

  85. C.L.P. Chen, Z.L. Liu, Broad learning system: A new learning paradigm and system without going deep. IEEE Autom. 1271–1276 (2017)

    Google Scholar 

  86. Y.H. Pao, G.H. Park, D.J. Sobajic, Learning and generalization characteristics of the random vector functional-link net. Neurocomputing 6(2), 163–180 (1994)

    Article  Google Scholar 

  87. K.S. Narendra, K. Parthasarathy, Identification and control of dynamical systems using neural networks. IEEE Trans. Neural Netw. 1(1), 4–27 (1990)

    Article  Google Scholar 

  88. I.Y. Tyukin, D.V. Prokhorov, Feasibility of random basis function approximators for modeling and control, in Proceedings of the IEEE Control Application of Intelligent Control (ISIC) (CCA) (2009), pp. 1391–1396

    Google Scholar 

  89. C.L.P. Chen, C.Y. Zhang, Data-intensive applications, challenges, techniques and technologies: a survey on big data. Inf. Sci. 275, 314–347 (2014)

    Article  Google Scholar 

  90. C.L.P. Chen, J.Z. Wan, A rapid learning and dynamic stepwise updating algorithm for flat neural networks and the application to timeseries prediction. IEEE Trans. Syst., Man, Cybern. B, Cybern. 29(1), 62–72 (1999)

    Article  Google Scholar 

  91. H. Yu, J. Yang, A direct LDA algorithm for high-dimensional data with application to face recognition. Pattern Recogn. 34(10), 2067–2070 (2001)

    Article  MATH  Google Scholar 

  92. X.S. Zhuang, D.Q. Dai, Improved discriminate analysis for high-dimensional data and its application to face recognition. Pattern Recogn. 40(5), 1570–1578 (2007)

    Article  MATH  Google Scholar 

  93. A. Sagheer, Improved SOM search algorithm for high-dimensional data with application to face recognition across pose and illumination. IEEE Soft Comput. Pattern Recogn. 247–252 (2011)

    Google Scholar 

  94. P.M. Narendra, K. Fukunaga, A branch and bound algorithm for feature subset selection. IEEE Trans. Comput. 26(9), 917–922 (1977)

    Article  MATH  Google Scholar 

  95. A. Rakotomamonjy, Variable selection using SVM-based criteria. J. Mach. Learn. Res. 3, 1357–1370 (2003)

    MathSciNet  MATH  Google Scholar 

  96. L. Breiman, Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  97. R.G. Baraniuk, M.B. Wakin, Random projections of smooth manifolds. Found. Comput. Math. 9(1), 51–77 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  98. Y. Chen, H. Jiang, C. Li, X. Jia, P. Ghamisi, Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans. Geosci. Remote Sens. 54(10), 6232–6251 (2016)

    Article  Google Scholar 

  99. A. Stuhlsatz, J. Lippel, T. Zielke, Feature extraction with deep neural networks by a generalized discriminant analysis. IEEE Trans. Neural Netw. Learn. Syst. 23(4), 596–608 (2012)

    Article  Google Scholar 

  100. M. Courbariaux, Y. Bengio, J.P. David, BinaryConnect: Training Deep Neural Networks with Binary Weights During Propagations. International Conference on Neural Information Processing Systems (2015) pp. 3123–3131

    Google Scholar 

  101. A. Ben-Israel, T. Greville, Generalized Inverses: Theory and Applications (Wiley, New York, NY, USA, 1974)

    MATH  Google Scholar 

  102. C.R. Rao, S.K. Mitra, Generalized Inverse of a Matrix and its Applications. Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1 (1972), pp. 601–620

    Google Scholar 

  103. D. Serre, “Matrices”, in Theory and Applications (Graduate Texts in Mathematics) (Springer, New York, NY, USA, 2002)

    Google Scholar 

  104. A.E. Hoerl, R.W. Kennard, Ridge regression: biased estimation for nonorthogonal problems. Technometrics 42(1), 80–86 (2000)

    Article  MATH  Google Scholar 

  105. C. Leonides, “Control and dynamic systems V18”, in Advances in Theory and Applications (Control and dynamic systems) (Elsevier, Amsterdam, The Netherlands, 2012)

    Google Scholar 

  106. J. Tapson, A.V. Schaik, “Learning the pseudoinverse solution to network weights. Neural Netw. 45(3), 94–100 (2013)

    Article  MATH  Google Scholar 

  107. L. Grasedyck, D. Kressner, C. Tobler, A literature survey of lowrank tensor approximation techniques. GAMM-Mitteilungen 36(1), 53–78 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  108. I. Markovsky, “Low rank approximation”, in Algorithms, Implementation, Applications (Communications and Control Engineering) (Springer, London, U.K., 2011)

    Google Scholar 

  109. Z. Yang, Y. Xiang, K. Xie, Y. Lai, Adaptive method for nonsmooth nonnegative matrix factorization. IEEE Trans. Neural Netw. Learn. Syst. 28(4), 948–960 (2017)

    Article  Google Scholar 

  110. C.L.P. Chen, A rapid supervised learning neural network for function interpolation and approximation. IEEE Trans. Neural Netw. 7(5), 1220–1230 (1996)

    Article  Google Scholar 

  111. M. Yang, L. Zhang, J. Yang, D. Zhang, Robust Sparse Coding for Face Recognition. Proceedings of the IEEE Conference of Computer Vision and Pattern Recognition (2011), pp. 625–632

    Google Scholar 

  112. J. Rommes, N. Martins, Exploiting structure in large-scale electrical circuit and power system problems. Linear Algebra Appl. 431(3), 318–333 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  113. X. Li, C. Chen, Y. Luo, M. Chen, “Optimization Scheme Based on Parallel Computing Technology. International Symposium on Parallel Architecture” in Algorithm and Programming. Springer, Singapore (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CNITECH, Chinese Academy of Sciences, Institute of Advanced Manufacturing Technology, Ningbo, Zhejiang, China

    Wenfeng Wang

  2. Naval Aeronautical University, Yantai, Shandong, China

    Xiangyang Deng & Limin Zhang

  3. Harbin Institute of Technology, Harbin, Heilongjiang, China

    Liang Ding

Authors
  1. Wenfeng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiangyang Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Liang Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Limin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wenfeng Wang .

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Huazhong University of Science and Technology Press, Wuhan and Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Wang, W., Deng, X., Ding, L., Zhang, L. (2020). The Vision–Brain Hypothesis. In: Brain-Inspired Intelligence and Visual Perception. Research on Intelligent Manufacturing. Springer, Singapore. https://doi.org/10.1007/978-981-13-3549-5_2

Download citation

Publish with us

Policies and ethics

Search

Navigation