×

Shadow separation of pavement images based on morphological component analysis. (English) Zbl 1459.94015

Summary: The shadow of pavement images will affect the accuracy of road crack recognition and increase the rate of error detection. A shadow separation algorithm based on morphological component analysis (MCA) is proposed herein to solve the shadow problem of road imaging. The main assumption of MCA is that the image geometric structure and texture structure components are sparse within a class under a specific base or overcomplete dictionary, while the base or overcomplete dictionaries of each sparse representation of morphological components are incoherent. Thereafter, the corresponding image signal is transformed according to the dictionary to obtain the sparse representation coefficients of each part of the information, and the coefficients are shrunk by soft thresholding to obtain new coefficients. Experimental results show the effectiveness of the shadow separation method proposed in this paper.

MSC:

94A08 Image processing (compression, reconstruction, etc.) in information and communication theory
68U10 Computing methodologies for image processing
68T10 Pattern recognition, speech recognition
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Starck, J.-L.; Elad, M.; Donoho, D., Redundant multiscale transforms and their application for morphological component separation, Advances in Imaging and Electron Physics, 132, 82, 287-348 (2004) · doi:10.1016/s1076-5670(04)32006-9
[2] Starck, J.-L.; Elad, M.; Donoho, D. L., Image decomposition via the combination of sparse representations and a variational approach, IEEE Transactions on Image Processing, 14, 10, 1570-1582 (2005) · Zbl 1288.94012 · doi:10.1109/tip.2005.852206
[3] Zakaria, F. A.; Maiz, S.; El Badaoui, M.; Khalil, M., First-and second-order cyclostationary signal separation using morphological component analysis, Digital Signal Processing, 58, 134-144 (2016) · doi:10.1016/j.dsp.2016.07.002
[4] Chi, J.; Eramian, M., Enhancing textural differences using wavelet-based texture characteristics morphological component analysis: a preprocessing method for improving image segmentation, Computer Vision and Image Understanding, 158, 49-61 (2017) · doi:10.1016/j.cviu.2017.01.006
[5] Ravi Shankar Reddy, G.; Rao, R., Oscillatory-plus-transient signal decomposition using TQWT and MCA, Journal of Electronic Science and Technology, 17, 2, 135-146 (2019)
[6] Jia, W.; Zhao, J.; Wang, C., Improved MCA-TV algorithm for interference hyperspectral image decomposition, Optics and Lasers in Engineering, 75, 81-87 (2015)
[7] Javidi, M.; Harati, A.; Pourreza, H., Retinal image assessment using bi-level adaptive morphological component analysis, Artificial Intelligence in Medicine, 99, 101-120 (2019) · doi:10.1016/j.artmed.2019.07.010
[8] Huang, W.; Wang, R.; Chen, Y., Regularized non-stationary morphological reconstruction algorithm for weak signal detection in microseismic monitoring: methodology, Geophysical Journal International, 213, 2, 1189-1211 (2018) · doi:10.1093/gji/ggy054
[9] Huang, W.; Wang, R.; Zu, S.; Chen, Y., Low-frequency noise attenuation in seismic and microseismic data using mathematical morphological filtering, Geophysical Journal International, 211, 3, 1296-1318 (2017) · doi:10.1093/gji/ggx371
[10] Huang, W.; Wang, R.; Zhang, D., Mathematical morphological filtering for linear noise attenuation of seismic data, Geophysics, 82, 6, 1-78 (2017) · doi:10.1190/geo2016-0580.1
[11] Li, H.; Wang, R.; Cao, S.; Chen, Y.; Tian, N.; Chen, X., Weak signal detection using multiscale morphology in microseismic monitoring, Journal of Applied Geophysics, 133, 39-49 (2016) · doi:10.1016/j.jappgeo.2016.07.015
[12] Luo, S.; Shen, H.; Li, H.; Chen, Y., Shadow removal based on separated illumination correction for urban aerial remote sensing images, Signal Processing, 165, 197-208 (2019) · doi:10.1016/j.sigpro.2019.06.039
[13] Gomes, V.; Barcellos, P.; Scharcanski, J., Stochastic shadow detection using a hypergraph partitioning approach, Pattern Recognition, 63, 30-44 (2017) · doi:10.1016/j.patcog.2016.09.008
[14] Qi, D.; Liu, Y.; Zhao, Q., Detecting soft shadows in a single outdoor image: from local edge-based models to global constraints, Computer & Graphics, 38, 310-319 (2014)
[15] Gao, F.; You, J.; Wang, J.; Sun, J.; Yang, E.; Zhou, H., A novel target detection method for SAR images based on shadow proposal and saliency analysis, Neurocomputing, 267, 220-231 (2017) · doi:10.1016/j.neucom.2017.06.004
[16] Pearlmutter, B. A.; Potluru, V. K., Sparse separation: principles and tricks, Proceedings of the International Society for Optical Engineering
[17] Ying, Li; Zhang, Y.; Xing, X., MCA based on sparse representation, Journal of Electric, 37, 1, 146-152. (2009)
[18] Candès, E. J.; Donoho, D. L., Curvelets, Department of Statistics (1999), Stanford, CL, USA: Stanford University, Stanford, CL, USA
[19] Garg, R. K.; Duncan, M. R.; Saksena, D. N., Water quality and conservation management of Ramsagar reservoir, Datia, Madhya Pradesh, Journal of Environmental Biology, 30, 5 Suppl, 909-916 (2009)
[20] Rao, E. J.; Curvelet, D. L. D.; Cohen, C. R. A.; Curves, L. L. S.; Nashville, S., A surprisingly effective nonadaptive representation for objects with edges (2000), Vanderbilt, TN, USA: Vanderbilt University Press, Vanderbilt, TN, USA
[21] Candes, E. J.; Demanet, L.; Donoho, D. L., Fast discrete curvelet transforms, Applied and Computational Mathematics, 1-43 (2005), Pasadena, CL, USA: California Institute of Technology, Pasadena, CL, USA
[22] Candės, E. J.; Ridgelets, M., Theory and Applications (1998), Stanford, CL, USA: Statistics of Stanford University, Stanford, CL, USA
[23] Candès, E. J.; Ridgelets, M., For the Representation of Images with edges (1999), Stanford, CL, USA: Department of Statistics, Stanford University, Stanford, CL, USA
[24] Prati, A.; Mikic, I.; Trivedi, M. M.; Cucchiara, R., Detecting moving shadows: algorithms and evaluation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 7, 918-923 (2003) · doi:10.1109/tpami.2003.1206520
[25] Qin, R.; Liao, S.; Lei, Z., Moving cast shadow removal based on local descriptors, Proceedings of the 20th International Conference on Pattern Recognition, IEEE
[26] Choi, J.; Yoo, Y. J.; Choi, J. Y., Adaptive shadow estimator for removing shadow of moving object, Computer Vision and Image Understanding, 114, 9, 1017-1029 (2010) · doi:10.1016/j.cviu.2010.06.003
[27] Wang, B.; Feng, J. Y.; Guo, H. F., Adaptive background updating and shadow detection in traffic scenes, Journal of Image and Graphics, 17, 11, 1391-1399 (2012)
[28] Ling, Z.; Lu, X.; Wang, Y., Adaptive moving cast shadow detection by integrating multiple cues, Chinese Journal of Electronics, 22, 4, 757-762 (2013)
[29] Qiu, Y. C.; Zhang, Y. Y.; Liu, C. M., Vehicle shadow removal with multi-feature fusion, Journal of Image and Graphics, 20, 3, 311-319 (2015)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.