DOI QR코드

DOI QR Code

Annealed Hopfield Neural Network for Recognizing Partially Occluded Objects

부분적으로 가려진 물체 인식을 위한 어닐드 홉필드 네트워크

  • Yoon, Suk-Hun (Department of Industrial and Information Systems Engineering, Soongsil University)
  • Received : 2021.04.29
  • Accepted : 2021.05.16
  • Published : 2021.05.31

Abstract

The need for recognition of partially occluded objects is increasing in the area of computer vision applications. Occlusion causes significant problems in identifying and locating an object. In this paper, an annealed Hopfield network (AHN) is proposed for detecting threat objects in passengers' check-in baggage. AHN is a deterministic approximation that is based on the hybrid Hopfield network (HHN) and annealing theory. AHN uses boundary features composed of boundary points and corner points which are extracted from input images of threat objects. The critical temperature also is examined to reduce the run time of AHN. Extensive computational experiments have been conducted to compare the performance of the AHNwith that of the HHN.

컴퓨터 비전 적용 분야에서 부분적으로 가려진 물체 인식의 필요성은 증가하고 있다. 물체를 확인하고 위치를 지정하는 데에 물체가 가려진 것은 심각한 문제를 야기한다. 이 논문은 여행자 소지 수하물에서 위험 물건을 발견하기 위하여 어닐드 홉필드 네트워크를 제안한다. 어닐드홉필드 네트워크는 하이브리드 홉필드 네트워크와 어닐링 이론에 기초한 확정적 근사방법이다. 하이브리드 홉필드 네트워크는 위험 물체의 이미지에서 발췌한 경계 점들과 코너 점들을 이용한다. 또한 어닐드 홉필드 네트워크의 런타임을 줄이기 위해 임계 온도를 조사하였다. 어닐드 홉필드 네트워크와 하이브리드 홉필드 네트워크의 성능을 비교하기 위하여 광범위한 컴퓨터 실험이 실행되었다.

Keywords

References

  1. Agliari, E., Barra, A., and Notarnicola, M., "The relativistic Hopfield network: rigorous results," Journal of Mathematical Physics, Vol. 60, No. 3, pp. 1-11, 2019.
  2. Aiyer, S. V. B., Niranjan, M., and Fallside, F., "A Theoretical Investigation into the Performance of the Hopfield Model," IEEE Transactions on Neural Networks, Vol. 1, No. 2, pp. 204-215, 1990. https://doi.org/10.1109/72.80232
  3. Cen, F. and Wang, G., "Boosting Occluded Image Classification via Subspace Decomposition-Based Estimation of Deep Features," IEEE Transactions on Cybernetics, Vol. 50, No. 7, pp. 3409-3422, 2020. https://doi.org/10.1109/tcyb.2019.2931067
  4. Cong, Y., Tian, D., Feng, Y., Fan, B. and Yu, H., "Speedup 3-D texture-less object recognition against self-occlusion for intelligent manufacturing," IEEE Transactions on Cybernetics," Vol. 49, No. 11, pp. 3887-3897, 2019. https://doi.org/10.1109/tcyb.2018.2851666
  5. de Castro, F. Z. and Valle, M. E., "A broad class of discrete-time hypercomplex-valued Hopfield neural networks," Neural Net- works, Vol. 122, pp. 54-67, 2020. https://doi.org/10.1016/j.neunet.2019.09.040
  6. Hopfield, J. J. and Tank, D. W., " 'Neural' Computation of Decisions in Optimization Problems," Biological Cybernetics, Vol. 52, pp. 141-152, 1985. https://doi.org/10.1007/BF00339943
  7. Hopfield, J. J. and Tank, D. W., "Computing with neural circuits: a model," Science, Vol. 233, pp. 625-633, 1986. https://doi.org/10.1126/science.3755256
  8. Hopfield, J. J., "Neural networks and physical systems with emergent collective computational abilities," Proceedings of the National Academy of Sciences of the United States of America, Vol. 79, pp. 2554-2558, 1982. https://doi.org/10.1073/pnas.79.8.2554
  9. Hopfield, J.J ., "Neurons with graded response have collective computational properties like those of two-state neurons," Proceedings of the National Academy of Sciences of the United States of America, pp. 3088-3092, 1984.
  10. Kim, J. H., Yoon, S. H., Kim, Y. H., Park, E. H., and Ntuen et al., "Efficient matching algorithm by a hybrid Hopfield network for object recognition," Proc. SPIE 1709, Applications of Artificial Neural Networks III, Orlando, FL, September 16, 1992.
  11. Kortylewski, A., Liu, Q., Wang, A., Sun, Y., and Yuille, "A., Compositional convolutional neural networks: a robust and interpretable model for object recognition under occlusion," International Journal of Computer Vision, Vol. 129, pp. 736-760, 2021. https://doi.org/10.1007/s11263-020-01401-3
  12. Montgomery, D. C., Design and Analysis of Experiments (10th Ed.), Wiley, New York, 2020.
  13. Nasrabadi, N.M. and Li, W., "Object recognition by a Hopfield neural network," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 21, No. 6, pp. 1523-1535, 1991. https://doi.org/10.1109/21.135694
  14. Priya, L. and Anand, S., "Object recognition and 3D reconstruction of occluded objects using binocular stereo," Cluster Computing, Vol. 21, pp. 29-38, 2018. https://doi.org/10.1007/s10586-017-0891-7
  15. Sohn, K. Alexander, W. E., Kim, J. H., and Snyder, W. E., "A constrained regularization approach to robust corner detection," IEEE Transactions on System, Man, and Cybernetics, Vol. 24, No. 5, pp. 820-828, 1994. https://doi.org/10.1109/21.293500
  16. van den Bout, D. E. and Miller III, T.K ., "Graph partitioning using annealed neural networks," International 1989 Joint Conference on Neural Networks, Washington DC, USA, pp. 521-528.
  17. Wang, X.-Y. and Li, Z.-M., "A color image encryption algorithm based on Hopfield chaotic neural network," Optics and Lasers in Engineering, Vol. 115, pp. 107-118, 2019. https://doi.org/10.1016/j.optlaseng.2018.11.010