• Lianfa Bai
  • Jing Han
  • Jiang Yue


Night-vision technology is used to extend human activities beyond the limits of natural visual ability. For example, it is widely used in military and civilian fields for observation, monitoring and low-light detection. Night-vision research includes low-level-light (LLL) vision, infrared thermal imaging, ultraviolet imaging and active near-infrared systems.


  1. Baldi, P., & Hornik, K. (1989). Neural networks and principal component analysis: Learning from examples without local minima. Neural Networks, 2(1), 53–58.CrossRefGoogle Scholar
  2. Belkin, M., & Niyogi, P. (2003). Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 15, 1373–1396.CrossRefGoogle Scholar
  3. Burl, M. C., Fowlkes, C., Roden, J., Stechert, A., Muukhtar, S. (1999). Diamond eye: A distributed architecture for image data mining. In SPIE Conference on Data Mining and Knowledge Discovery (Vol. 3695, pp. 197–206).Google Scholar
  4. Chang, H., & Yeung, D. (2006). Robust locally linear embedding. Pattern Recognition, 39(6), 1053–1065.CrossRefGoogle Scholar
  5. Chapelle, O., Scholkopf, B., Zien, A. (2006). Semi-supervised learning. The MIT Press.Google Scholar
  6. Chen, H., Chang, H., Liu, T. (2005). Local discriminant embedding and its variants. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (Vol. 2, pp. 846–853).Google Scholar
  7. Chen, J., Ma, S. (2011). Locally linear embedding: A review. International Journal of Pattern Recognition and Artificial Intelligence, 25(07).MathSciNetCrossRefGoogle Scholar
  8. Choi, H., & Choi, S. (2007). Robust kernel Isomap. Pattern Recognition, 40, 853–862.CrossRefGoogle Scholar
  9. Coifman, R. R., & Lafon, S. (2006). Diffusion maps. Applied and Computational Harmonic Analysis, 221(1), 5–30.MathSciNetCrossRefGoogle Scholar
  10. Cox, T., & Cox, M. (2008). Multidimensional scaling (pp. 315–347). Berlin Heidelberg: Springer.zbMATHGoogle Scholar
  11. Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 886–893), San Diego, CA, USA.Google Scholar
  12. Datcu, M., Seidel, K., Walessa, M. (1998). Spatial information retrieval from remote sensing images. Part I: Information theoretical perspective. IEEE Transactions on Geoscience & Remote Sensing, 36(5), 1431–1445.Google Scholar
  13. de Ridder, D., Kouropteva, O., Okun, O., Pietikäinen, M., & Duin, R. P. W. (2003). Supervised locally linear embedding. ICANN/ICONIP, 2(714), 333–341.zbMATHGoogle Scholar
  14. Donoho, D. (2000). High-dimensional data analysis: The curses and blessings of dimensionality. In Mathematical Challenges of the 21st Century. Los Angeles, CA: American Mathematical Society.Google Scholar
  15. Donoho, D. L., Grimes, C. (2003). Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data. Proceedings of National Academy of Sciences of the United States of America, 100(10), 5591–5596.MathSciNetCrossRefGoogle Scholar
  16. Duda, R. O., Hart, P E., Stork, D. G. (2000). Pattern classification. Wiley-lnterscience Publication.Google Scholar
  17. Edelman, S. (1999). Representation and recognition in vision. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
  18. Foschi, P. G., Kolippakkam, D., Liu, H., Mandvikar, A. (2002). Feature extraction for image mining. In International Workshop on Multimedia Information Systems (pp. 103–109).Google Scholar
  19. Foschi, P. G., Kolippakkam, D., Liu, H. (2003). Feature selection for image data via learning. IMMCN, 1299–1302.Google Scholar
  20. Ge, S. S., He, H., & Shen, C. (2012). Geometrically local embedding in manifolds for dimension reduction. Pattern Recognition, 45, 1455–1470.CrossRefGoogle Scholar
  21. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical leaming. New York: Springer.CrossRefGoogle Scholar
  22. He, X., Cai, D., Yan, S., Zhang, H. J. (2005). Neighbourhood preserving embedding. In Proceedings 10th IEEE International Conference on Computer Vision (pp. 1208–1213).Google Scholar
  23. He, X., Niyogi, P. (2003). Locality-preserving projections (LPP). Advances in Neural Information Processing Systems, 155–160.Google Scholar
  24. Jegou, H., Douze, M., & Schmid, C. (2010). Improving bag-of-features for large scale image search. International Journal of Computer Vision, 87(3), 316–336.CrossRefGoogle Scholar
  25. Lawrence, K. S., Sam, T. R. (2003). Think globally, fit locally: Unsupervised learning of low dimensional manifolds. Journal of Machine Learning Research, 119–155.Google Scholar
  26. Lazebnik, S., Schmid, C., Ponce, J. (2006). Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 2169–2178), New York.Google Scholar
  27. Li, J. (2012). Gabor filter based optical image recognition using Fractional Power Polynomial model based common discriminant locality-preserving projection with kernels. Optics and Lasers in Engineering, 50, 1281–1286.CrossRefGoogle Scholar
  28. Li, J., Pan, J.-S., & Chen, S.-M. (2011). Kernel self-optimised locality-preserving discriminant analysis for feature extraction and recognition. Neurocomputing, 74, 3019–3027.CrossRefGoogle Scholar
  29. Li, F. F., Perona, P. (2005). A Bayesian hierarchical model for learning natural scene categories. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 524–531), San Diego, CA, USA.Google Scholar
  30. Li, B., & Zhang, Y. (2011). Supervised locally linear embedding projection (SLLEP) for machinery fault diagnosis. Mechanical Systems and Signal Processing, 25, 3125–3134.CrossRefGoogle Scholar
  31. Liu, H., Mandvikar, A., Foschi, P. (2003). An active learning approach to Egeria densa detection in digital imagery. In Next generation of data-mining applications (pp. 189–210). Wiley.Google Scholar
  32. Liu, L., Wang, L., Liu, X. (2011). In defence of soft-assignment coding. In IEEE International Conference on Computer Vision (ICCV) (pp. 2486–2493), Barcelona, Spain.Google Scholar
  33. Ojala, T., Pietikäinen, M., & Mäenpää, T. (2002). Multiresolution grayscale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971–987.CrossRefGoogle Scholar
  34. Ordonez, C., Omieeinski, E. (1999). Discovering association rules based on image content. In Proceedings IEEE Forum on Research and Technology Advances in Digital Libraries (Vol. 1999, pp. 38–49).Google Scholar
  35. Pan, Y., Ge, S. S., & Mamun, A. A. (2009). Weighted locally linear embedding for dimension reduction. Pattern Recognition, 42, 798–811.CrossRefGoogle Scholar
  36. Philbin, J., Chum, O., Isard, M., et al. (2008). Lost in quantisation: Improving object retrieval in large scale image databases. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1–8), Anchorage, AK, USA.Google Scholar
  37. Roweis,S. T., Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500), 2323–2326.CrossRefGoogle Scholar
  38. Rubner, Y., Tomasi, C., & Guibas, L. J. (2010). The earth mover’s distance as a metric for image retrieval. International Journal of Computer Vision, 40(2), 99–121.CrossRefGoogle Scholar
  39. Rüdiger, W., Joche, H. (2000). GRISP–DM: Towards a standard process model for data mining. In Proceedings of the Fourth International Conference on the Practical Application of Knowledge Discovery and Data Mining (pp. 29–39).Google Scholar
  40. Rumelhart, D. E., Hinton, G. E., Williams, R. J. (1988). Learning representations by back-propagating errors. In Neurocomputing: Foundations of research (pp. 533–536). MIT Press.Google Scholar
  41. Schölkopf, B., Platt, J., Hofmann, T. (2006). Greedy layer-wise training of deep networks. In International Conference on Neural Information Processing Systems (pp. 153–160). MIT Press.Google Scholar
  42. Scholkopf, B., Smola, A. J. (2002). Learning with kernels: Support vector machines, regularisation, optimisation, and beyond. The MIT Press.Google Scholar
  43. Seung, H. S., Lee, D. D. (2000). The manifold ways of perception. Science, 2268–2269.CrossRefGoogle Scholar
  44. Shawe-Taylor, J., Cristianini, N. (2004). Kernel methods for pattern analysis. Cambridge University Press.Google Scholar
  45. Tenenbaum, J., De Silva, V., & Langford, J. C. (2000). A global geometric framework for nonlinear dimension reduction. Science, 290(5500), 2319–2323.CrossRefGoogle Scholar
  46. Wang, J., Yang, J., Yu, K., et al. (2010). Locality-constrained linear coding for image classification. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 3360–3367), San Francisco, CA, USA.Google Scholar
  47. Weinberger, K. Q., & Saul, L. K. (2006). Unsupervised learning of image manifolds by semi-definite programming. International Journal of Computer Vision, 70(1), 77–90.CrossRefGoogle Scholar
  48. Wen, G. (2009). Relative transformation-based neighbourhood optimisation for isometric embedding. Neurocomputing, 72, 1205–1213.CrossRefGoogle Scholar
  49. Xiao, R. (2011). Discriminant analysis and manifold learning of high dimensional space patterns. Shanghai Jiao Tong University.Google Scholar
  50. Yan, S., Xu, D., Zhang, B., Zhang, J., Yang, Q., & Lin, S. (2007). Graph embedding and extensions: A general framework for dimensionality reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(1), 40–51.CrossRefGoogle Scholar
  51. Yang, L. (2005a). Building k-edge-connected neighbourhood graphs for distance-based data projection. Pattern Recognition Letters, 26(13), 2015–2021.CrossRefGoogle Scholar
  52. Yang, L. (2005b). Building k edge-disjoint spanning trees of minimum total length for isometric data embedding. In IEEE Transaction on Pattern Analysis and Machine Intelligence, 27(10), 1680–1683.CrossRefGoogle Scholar
  53. Yang, L. (2006a). Building k-Connected Neighbourhood Graphs for Isometric Data Embedding. IEEE Transaction on Pattern Analysis and Machine Intelligence, 28(5), 827–831.CrossRefGoogle Scholar
  54. Yang, L. (2006b). Locally multidimensional scaling for nonlinear dimensionality reduction. In 18th International Conference Pattern Recognition (ICPR’06) (Vol. 4, pp. 202–205).Google Scholar
  55. Zaiane, O. R., Han, J. (2000). Discovering spatial associations in images. In Proceedings of SPIE (Vol. 4057, pp. 138–147).Google Scholar
  56. Zaiane, O. R., Han, J., Li, S., Chee, S. H., Chiang, J. Y. (1998). MultiMediaMiner: A system prototype for multimedia data mining. In ACM SIGMOD International Conference on Management of Data (Vol. 27, No. 2, pp. 581–583).CrossRefGoogle Scholar
  57. Zhang, S. (2009). Enhanced supervised locally linear embedding. Pattern Recognition Letters, 30, 1208–1218.CrossRefGoogle Scholar
  58. Zhang, L. (2010). Research and application of large-scale machine learning theory. Zhejiang University.Google Scholar
  59. Zhang, J., Hsu, W., Lee, M. (2001). An information-driven framework for image mining. In The 12th International Conference on Database and Expert Systems Applications (Vol. 2113, pp. 232–242).Google Scholar
  60. Zhang, Z., Yang, F., Xia, K., & Yang, R. (2008). A supervised LPP algorithm and its application to face recognition. Journal of Electronics and Information Technology, 30(3), 539–541.CrossRefGoogle Scholar
  61. Zhang, T., Yang, J., & Zhao, D. (2007). Linear local tangent space alignment and application to face recognition. Neuro computing, 70(7–9), 1547–1553.Google Scholar
  62. Zhang, Z., & Zha, H. (2005). Principal manifolds and nonlinear dimension reduction via local tangent space alignment. SIAM Journal on Scientific Computing, 26(1), 313–338.CrossRefGoogle Scholar
  63. Zhao, L., & Zhang, Z. (2009). Supervised locally linear embedding with probability based distance for classification. Computers & Mathematics with Applications, 57, 919–926.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Electronic and Optical EngineeringNanjing University of Science and TechnologyNanjingChina
  2. 2.School of Electronic and Optical EngineeringNanjing University of Science and TechnologyNanjingChina
  3. 3.National Key Laboratory of Transient PhysicsNanjing University of Science and TechnologyNanjingChina

Personalised recommendations