@inproceedings{
 title = {Special Image Post-Processing For Recognition of Compound Structures},
 type = {inproceedings},
 year = {2010},
 publisher = {Sociedad Mexicana de Materiales A. C.},
 id = {099922ab-bef6-31d4-99a0-2860a1500f12},
 created = {2022-08-29T17:43:13.224Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:13.224Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 private_publication = {false},
 abstract = {Necessity of reconstruction of compound structures appears in different applications. As the rule, a tomography is used for this aim. However, obtained result usually contains sufficient visible noise. To eliminate such noise a post-processing is used, which consists in application of special image processing techniques and transforms. In this paper, we consider two types of special image postprocessing. The fist type is based on Recursive Spline Smoothing method [1], which is realized as fast and stable algorithm and MATLAB software adapted to image reconstruction. If recuperating structure has piecewise constant characteristics with known values, then the algorithm includes also the projection of the pre-reconstructed data to the known set of values with respect to the absolute or relative criterion. The second type is based on dual-tree complex wavelet transform (DT-CWT), proposed in [2]. The approach follows four basic procedures such as, image denoising, band suppression, morphological transformation and inverse complex wavelet transform. The procedure of image denoising is carried out with a thresholding algorithm that computes recursively the optimal threshold at each level of wavelet decomposition. Both of proposed approaches are tested on numerical examples, which demonstrate their good quality and acceptable range of errors. Acknowledgments. This research is sponsored by Mexican National Council of Science and Technology, CONACyT, Project #109417 [1] A. I. Grebennikov Spline Approximation Method and Its Applications, MAX Press, Russia, 2004 (in English). [2] V. Alarcon-Aquino, O. Starostenko, et al. Detection of microcalcifications in digital mammograms using the dual-tree complex wavelet transform, Journal Engineering Intelligent Systems, #1, 2009, pp.49-63. S3-},
 bibtype = {inproceedings},
 author = {O. Starostenko A. Grebennikov, V Alarcon-Aquino},
 booktitle = {SYMPOSIUM 3 STRUCTURAL AND CHEMICAL CHARACTERIZATION OF METALS ALLOYS AND COMPOUNDS}
}

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