Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회지)

Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회)
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Automatic Registration between Multiple IR Images Using Simple Pre-processing Method and Modified Local Features Extraction Algorithm

단순 전처리 방법과 수정된 지역적 피쳐 추출기법을 이용한 다중 적외선영상 자동 기하보정

  • Received : 2017.11.20
  • Accepted : 2017.12.28
  • Published : 2017.12.31
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Abstract

This study focuses on automatic image registration between multiple IR images using simple preprocessing method and modified local feature extraction algorithm. The input images were preprocessed by using the median and absolute value after histogram equalization, and it could be effectively applied to reduce the brightness difference value between images by applying the similarity of extracted features to the concept of angle instead of distance. The results were evaluated using visual and inverse RMSE methods. The features that could not be achieved by the existing local feature extraction technique showed high image matching reliability and application convenience. It is expected that this method can be used as one of the automatic registration methods between multi-sensor images under specific conditions.

본 연구는 단순 전처리 방법과 수정된 지역적 피쳐 추출기법을 이용하여 특성이 다른 적외선영상 자동 기하보정에 초점을 맞추고 있다. 입력영상은 히스토그램 평활화를 통해 중앙값과 절댓값을 이용하여 전처리를 수행하였으며, 추출 피쳐의 유사도를 거리가 아닌 각 개념으로 변경하여 적용함으로써, 영상간 밝기값 차이를 줄이는데 효과적으로 적용할 수 있도록 하였다. 기하보정 결과는 시각적인 방법과 Inverse RMSE 방식을 사용하여 평가하였으며, 영상의 특성 차이로 인해 기존의 지역적 피쳐 추출기법 적용으로 해결될 수 없었던 자동 기하보정이 본 알고리즘을 적용함으로써 높은 정합 신뢰도와 적용 편의성을 보임을 확인할 수 있었다. 이를 통해, 제안 방법이 특정 조건의 다중 센서 영상간 자동 기하보정 기법 중 하나로 사용될 수 있을 것으로 기대한다.

Keywords

References

  1. Alitappeh, R.J. and Mahmoudi, F. (2013), MGS-SIFT: a new illumination invariant feature based on SIFT descriptor, International Journal of Computer Theory and Engineering, Vol. 5, No. 1, pp. 99-103.
  2. Fischler, M.A. and Bolles, R.C. (1981), Random sample consensus: a paradigm for model fitting with application to image analysis and automated cartography, Communications of the ACM, Vol. 24, No. 6, pp. 381-395. https://doi.org/10.1145/358669.358692
  3. Gwon, H.G., Lee, I.H., and Choi, T.S. (2013), Electro-optics and infrared image registration using gaussian pyramids, Advanced Science and Technology Letters, Vol. 29, pp. 55-59.
  4. Han, D.Y, Kim, D.S., Lee, J.B., Oh, J.H., and Kim, Y.I. (2006), Automatic image-to-image registration of middleand low-resolution satellite images using scale-invariant feature transform technique, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 24, No. 5, pp. 409-416. (in Korean with English abstract)
  5. Irani, M. and Anandan, P. (1998), Robust multi-sensor image alignment, Sixth International Conference on Computer Vision, pp. 959-966.
  6. Kim, D.S., Kim, Y.I., and Eo, Y.D. (2007), A study on automatic co-registration and band selection of hyperion hyperspectral images for change detection, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 24, No. 5, pp. 383-392. (in Korean with English abstract)
  7. Kim, K.S. (2015), Survey on registration techniques of visible and infrared images, IT CoNvergence PRActice (INPRA), Vol. 3, No. 2, pp. 25-35.
  8. Li, H., Zhang, A., and Hu, S. (2015), A multispectral image creating method for a new airborne four-camera system with different bandpass filters, Sensors, Vol. 15, pp. 17453-17469. https://doi.org/10.3390/s150717453
  9. Li, H. and Zhou, Y.T. (1995), Automatic EO/IR sensor image registration, Proceeding of IEEE International Conference on Image Processing, pp. 240-243.
  10. Liu, F. and Seipel, S. (2015), Infrared-visible image registration for augmented reality-based thermographic building diagnostics, Visualization in Engineering, Vol. 3, No. 16, pp. 1-15. https://doi.org/10.1186/s40327-014-0014-y
  11. Lowe, D.G. (2004), Distinctive image features from scaleinvariant keypoints, International Journal on Computer Vision, IJCV, Vol. 60, No. 2, pp. 91-110. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  12. Mikolajczyk, K. and Schmid, C. (2005), A performance evaluation of local descriptors, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 27, No. 10, pp. 1615-1630. https://doi.org/10.1109/TPAMI.2005.188
  13. Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., and Gool, L.V. (2005), A comparison of affine region detectors, International Journal of Computer Vision, Vol. 65, No.1, pp. 43-72. https://doi.org/10.1007/s11263-005-3848-x
  14. Moigne, J.L., Netanyahu, N.S., and Eastman, R.D. (2011), Image Registration for Remote Sensing, Cambridge University Press, Cambridge, UK, pp. 3-19 & pp. 35-65.
  15. Morel, J.M. and Yu, G. (2009), ASIFT: a new framework for fully affine invariant image comparison, SIAM Journal on Imaging Sciences, Vol. 2, No. 2, pp. 438-469. https://doi.org/10.1137/080732730
  16. Park, J.H., Park, K.W., Baeg, S.H., and Baeg, M.H. (2010), ${\pi}$-SIFT: A Photometric and Scale Invariant Feature Transform, Patten Recognition, Recent Advances, INTECH Open Access Publisher, pp. 137-150.
  17. Sohn, Y. and Rebello, N.S. (2002), Supervised and unsupervised spectral angle classifiers, Photogrammetric Engineering & Remote Sensing, ASPRS, Vol. 68, No. 12, pp. 1271-1280.
  18. Yu, Y., Huang, K., Chen, W., and Tan, T. (2012), A novel algorithm for view and illumination invariant image matching, IEEE Transaction on Image Processing, Vol. 21, No. 1, pp. 229-240. https://doi.org/10.1109/TIP.2011.2160271
  19. Wu, F., Wang, B., Yi, X., Hao, J, Qin, H., and Zhou, H. (2015), Visible and infrared image registration based on visual salient features, Journal of Electronic Imaging, Vol. 24, No. 5, No Page Description(Open Access with Internet)
  20. Zitova, B. and Flusser, J. (2003), Image registration methods: a survey, Image and Vision Computing, Vol. 21, No. 11, pp. 977-1000. https://doi.org/10.1016/S0262-8856(03)00137-9