Images Crack Detection Technology based on Improved K-means Algorithm

Crack detection is very important to prevent major accident in civil engineering works, but it is still problematic in implementation. The traditional K-means algorithm only takes pixel values into account, which causes the extraction of pavement crack is not accurate. In order to improve the efficiency and accuracy, a novel algorithm is proposed. It is a combination of the improved K-means algorithm and the region growing algorithm, which designs a novel distance function and increases a weight related to crack distance region. The proposed algorithm can effectively abstract the crack information in non-uniform illumination, and improve the performance. The algorithm firstly utilizes histogram algorithm to find the initial clustering center, and then uses the improved K-means algorithm to extract crack. This algorithm overcomes the drawbacks of center indeterminacy and slow speed. Applying the improved K-means algorithm to extract pavement crack image with non-uniform illumination can solve the problem of crack extraction and enhance the reliability and accuracy of pavement crack detection. The results show that compared with traditional K-means algorithm, our proposed algorithm has remarkable effects and can extract the crack information in condition of non-uniform illumination.

[1]  Terry S. Yoo,et al.  Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis , 2004 .

[2]  Gary E. Christensen,et al.  Consistent image registration , 2001, IEEE Transactions on Medical Imaging.

[3]  George Wolberg,et al.  Robust image registration using log-polar transform , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[4]  D. R. Fish,et al.  A patient-to-computed-tomography image registration method based on digitally reconstructed radiographs. , 1994, Medical physics.

[5]  R W Cox,et al.  Real‐time 3D image registration for functional MRI , 1999, Magnetic resonance in medicine.

[6]  Lawrence A. Ray,et al.  2-D and 3-D Image Registration for Medical, Remote Sensing, and Industrial Applications , 2005, J. Electronic Imaging.

[7]  Jin-yi Chang,et al.  An edge detection improved algorithm based on morphology and wavelet transform , 2010, 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE).

[8]  Benoit M. Dawant,et al.  The adaptive bases algorithm for intensity-based nonrigid image registration , 2003, IEEE Transactions on Medical Imaging.

[9]  David Fofi,et al.  A review of recent range image registration methods with accuracy evaluation , 2007, Image Vis. Comput..

[10]  Nicholas Ayache,et al.  Non-parametric Diffeomorphic Image Registration with the Demons Algorithm , 2007, MICCAI.

[11]  J. F. Bradshaw,et al.  The principal axes transformation--a method for image registration. , 1990, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[12]  Nick McKeown,et al.  Algorithms for packet classification , 2001, IEEE Netw..

[13]  Kaijian Xia,et al.  Adaptive error control mechanism based on link layer frame importance valuation for wireless multimedia sensor networks , 2010, 2010 2nd International Conference on Advanced Computer Control.

[14]  Michael Unser,et al.  Optimization of mutual information for multiresolution image registration , 2000, IEEE Trans. Image Process..

[15]  Max A. Viergever,et al.  Interpolation Artefacts in Mutual Information-Based Image Registration , 2000, Comput. Vis. Image Underst..

[16]  Manuel Guizar-Sicairos,et al.  Efficient subpixel image registration algorithms. , 2008, Optics letters.

[17]  Siamak Khorram,et al.  A feature-based image registration algorithm using improved chain-code representation combined with invariant moments , 1999, IEEE Trans. Geosci. Remote. Sens..

[18]  Brian B. Avants,et al.  Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain , 2008, Medical Image Anal..

[19]  David R. Haynor,et al.  PET-CT image registration in the chest using free-form deformations , 2003, IEEE Transactions on Medical Imaging.

[20]  Kaijian Xia,et al.  A Case of Parallel EEG Data Processing upon a Beowulf Cluster , 2009, 2009 15th International Conference on Parallel and Distributed Systems.

[21]  B. N. Chatterji,et al.  An FFT-based technique for translation, rotation, and scale-invariant image registration , 1996, IEEE Trans. Image Process..

[22]  Kaijian Xia,et al.  Research in Clustering Algorithm for Diseases Analysis , 2013, J. Networks.

[23]  Max A. Viergever,et al.  Image registration by maximization of combined mutual information and gradient information , 2000, IEEE Transactions on Medical Imaging.

[24]  Karl J. Friston,et al.  Incorporating Prior Knowledge into Image Registration , 1997, NeuroImage.

[25]  Jean-Philippe Thirion,et al.  New feature points based on geometric invariants for 3D image registration , 1996, International Journal of Computer Vision.

[26]  Ardeshir Goshtasby,et al.  A Region-Based Approach to Digital Image Registration with Subpixel Accuracy , 1986, IEEE Transactions on Geoscience and Remote Sensing.

[27]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[28]  Joe Y. Chang,et al.  Validation of an accelerated ‘demons’ algorithm for deformable image registration in radiation therapy , 2005, Physics in medicine and biology.

[29]  B. Ardekani,et al.  A Fully Automatic Multimodality Image Registration Algorithm , 1995, Journal of computer assisted tomography.

[30]  Kaijian Xia,et al.  The representation and simulation for reasoning about action based on Colored Petri Net , 2010, 2010 2nd IEEE International Conference on Information Management and Engineering.

[31]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

[32]  Max A. Viergever,et al.  Image Registration by Maximization of Combined Mututal Information and Gradient Information , 2000, MICCAI.

[33]  J C Mazziotta,et al.  Automated image registration: II. Intersubject validation of linear and nonlinear models. , 1998, Journal of computer assisted tomography.

[34]  Jan Modersitzki,et al.  Curvature Based Image Registration , 2004, Journal of Mathematical Imaging and Vision.

[35]  Nicholas Ayache,et al.  The Correlation Ratio as a New Similarity Measure for Multimodal Image Registration , 1998, MICCAI.

[36]  K. Brock,et al.  Feasibility of a novel deformable image registration technique to facilitate classification, targeting, and monitoring of tumor and normal tissue. , 2006, International journal of radiation oncology, biology, physics.

[37]  Jian Cai,et al.  Research on Improved Network Data Fault-Tolerant Transmission Optimization Algorithm , 2012 .