AIRR  >> Vol. 5 No. 2 (May 2016)

    Comparison of Classic Algorithm for Orientation Estimation

  • 全文下载: PDF(458KB) HTML   XML   PP.35-40   DOI: 10.12677/AIRR.2016.52004  
  • 下载量: 263  浏览量: 1,474  


李大龙:海军航空工程学院青岛校区,山东 青岛

方向估计图像处理误差比较Orientation Estimation Image Processing Error Comparison



Orientation estimation aims to compute the orientation angles of multi-dimensional signals and can be applied to many basic tasks in image processing and computer vision. In this paper, a short review of existing methods for estimating local orientation tensors has been given and error comparison was done to facilitate further research work and to design more accurate orientation estimation methods.

李大龙. 典型方向估计方法比较研究[J]. 人工智能与机器人研究, 2016, 5(2): 35-40.


[1] Spies, H. and Forssén, P.-E. (2003) Two-Dimensional Channel Representation for Multiple Velocities, Image Analysis. Volume 2749 of the Series Lecture Notes in Computer Science, Springer, Berlin Heidelberg, 356-362.
[2] Mester, R. (2000) Orientation Estimation: Conventional Techniques and a New Non-Differential Approach. Proceeding of the 10th European Signal Processing Conference, 2, 921-924.
[3] Yang, X.D., Chau, W. and Wong, S.K.M. (1993) Multi-Scale Orientation Estimation for Unstructured Sample Points. IEEE CCGEI.
[4] Fu, S. and Zhang, C. (2012) Fringe Pattern Denoising Using Averaging Based on Nonlocal Self-Similarity. Optics Communications, 285, 2541-2544.
[5] Shrivastava, A. and Srivastava, D.K. (2014) Fingerprint Identification Using Feature Extraction: A Survey. International Conference on Contemporary Computing and Informatics (IC3I), Mysore, 522-525.
[6] Maltoni, D. and Maio, D. (2003) Handbook of Fingerprint Recognition. Springer, 85-91.
[7] Singh, K., Kapoor, R. and Nayar, R. (2015) Fingerprint Denoising Using Ridge Orientation Based Clustered Dictionaries. Neurocomputing, 167, 418-423.
[8] Grigoryeva, I.V. (2012) Segmentation Algorithm with Several Dominant Directions. 11th International Conference on Actual Problems of Electronics Instrument Engineering (APEIE), Novosibirsk, 16-18.
[9] Feng, J., Zhou, J. and Jain, A.K. (2013) Orientation Field Estimation for Latent Fingerprint Enhancement. IEEE Trans- actions on Pattern Analysis and Machine Intelligence, 35, 925-940.
[10] Sourice, A., Plantier, G. and Saumet, J.L. (2003) Autocorrelation Fitting for Texture Orientation Estimation. International Conference on Image Processing.
[11] Zhou, J. and Gu, J.W. (2004) A Model-Based Method for the Computation of Fingerprints’ Orientation Field. IEEE Transactions on Image Processing, 13, 821-835.
[12] Kim, S., Yoo, H., Ryu, S., Ham, B. and Sohn, K. (2013) ABFT: Anisotropic Binary Feature Transform Based on Structure Tensor Space. 20th IEEE International Conference on Image Processing (ICIP), Melbourne, 2920-2923.
[13] Yu, L., Tian, M.S. and Li, G. (2014) A Novel Video Super-Resolution Algorithm Based on Non-Local Normalized Convolution, Unifying Electrical Engineering and Electronics Engineering. Vol. 238 of the Series Lecture Notes in Electrical Engineering, 1141-1150.
[14] Granlund, G.H. and Knutsson, H. (1995) Signal Processing for Computer Vision. Kluwer Academic Publishers.
[15] Stavros, T., Stavros, S., et al. (2014) Multiresolution Edge Detection Using Enhanced Fuzzy c-Means Clustering for Ultrasound Image Speckle Reduction. Medical Physics, 41, Article ID: 072903.
[16] Knutsson, H., Westin, C.-F. and Andersson, M. (2012) Structure Tensor Estimation: Introducing Monomial Quadrature Filter Sets. In: Laidlaw, D.H. and Vilanova, A., Eds., New Developments in the Visualization and Processing of Tensor Fields, Part of the Series Mathematics and Visualization, Springer, Berlin, 3-28.
[17] Lee, J.W. and Cho, J.S. (2009) Effective Lane Detection and Tracking Method Using Statistical Modeling of Color and Lane Edge-Orientation. 4th International Conference on Computer Sciences and Convergence Information Technology, ICCIT’09, Seoul, 24-26 November 2009, 1586-1591.
[18] Gottschlich, C. (2012) Curved-Region-Based Ridge Frequency Estimation and Curved Gabor Filters for Fingerprint Image Enhancement. IEEE Transactions on Image Processing, 21, 2220-2227.
[19] Geng, H., Li, J.C., Zhou, J.W. and Chen, D. (2015) An Improved Gabor Enhancement Method for Low-Quality Fingerprint Images. AOPC 2015: Image Processing and Analysis, Proceedings of the SPIE, 9675, Article ID: 96751J.
[20] Westin. C.-F. (1994) A Tensor Framework for Multidimensional Signal Processing. SE-581 83, Dissertation No. 348, PhD Thesis, Linkoping University, Linkoping.
[21] Knutsson, H., Westin, C.-F. and Westelius, C.-J. (1993) Filtering of Uncertain Irregularly Sampled Multidimensional Data. 27th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, 1-3 November 1993, 1301-1309.
[22] Knutsson, H. and Westin, C.-F. (1993) Normalized and Differential Convolution: Methods for Interpolation and Filtering of Incomplete and Uncertain Data. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New York, 15-17 Jun 1993, 515-523.
[23] Johansson, B. and Farneback, G. (2002) A Theoretical Comparison of Different Orientation Tensors. Proceedings SSAB02 Symposium on Image Analysis, Lund, March 2002, 69-73.
[24] Wang, H.X. and Qian, K.M. (2012) Quality-Guided Orientation Unwrapping for Fringe Direction Estimation. Applied Optics, 51, 413-421.
[25] Cabeen, R.P., Bastin, M.E. and Laidlaw, D.H. (2016) Kernel Regression Estimation of Fiber Orientation Mixtures in Diffusion MRI. Neuroimage, 127, 158-172.