http://iet.metastore.ingenta.com
1887

Two-dimensional shape-adaptive windowing functions for image analysis

Two-dimensional shape-adaptive windowing functions for image analysis

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Two-dimensional (2D) windowing functions (e.g. Hann's) defined on square (or rectangular) sub-matrices are routinely used in image processing when the local 2D Fourier transform has to be computed. However, in applications where the square-shaped 2D Fourier transform has to be computed from a spatially limited subset of image data of irregular shape (e.g. from an area obtained by segmenting), windowing functions defined on square sub-matrices cannot be used. Therefore, there is a need for 2D weighting functions whose support shape is adaptable to the shape of a given binary object. Several design variants of 2D shape-adaptive windowing functions (SAW) are presented as a proposed solution to this problem. In order to quantitatively assess and compare the design variants, five criteria for measurement of 2D SAW qualities are proposed. Based on extensive testing undertaken on both simulated and real-life data, it can be concluded that qualities of each of the proposed 2D SAW design variants are generally superior to the quality of an evenly-weighting window according to these test criteria. In conclusion, one of these 2D SAW design variants is recommended as superior for generic use in image processing.

References

    1. 1)
      • 1. Jan, J.: ‘Digital signal filtering, analysis and restoration’ (Institution of Electrical Engineers, London, 2000). IEE telecommunications series, 44. ISBN 0852967608.
    2. 2)
      • 2. Zhang, Y., Candra, P., Wang, G., et al: ‘2-D entropy and short-time Fourier transform to leverage GPR data analysis efficiency’, IEEE Trans. Instrum. Meas., 2015, 64, (1), pp. 103111.
    3. 3)
      • 3. Samiee, K., Kovács, P., Gabbouj, M.: ‘Epileptic seizure classification of EEG time-series using rational discrete short-time Fourier transform’, IEEE Trans. Biomed. Eng., 2015, 62, (2), pp. 541552.
    4. 4)
      • 4. Veer, K., Agarwal, R.: ‘Wavelet and short-time Fourier transform comparison-based analysis of myoelectric signals’, J. Appl. Stat., 2015, 42, (7), pp. 15911601.
    5. 5)
      • 5. Harris, F.J.: ‘On the use of windows for harmonic analysis with the discrete Fourier transform’, Proc. IEEE, 1978, 66, (1), pp. 5183.
    6. 6)
      • 6. Donciu, C., Temneanu, M.: ‘An alternative method to zero-padded DFT’, Measurement, 2015, 70, pp. 1420.
    7. 7)
      • 7. Claeys, T., Vanoost, D., Peuteman, J., et al: ‘An iterative interpolated DFT to remove spectral leakage in time-domain near-field scanning’, IEEE Trans. Electromagn. Compat., 2018, 60, (1), pp. 202210.
    8. 8)
      • 8. Breitenbach, A.: ‘Against spectral leakage’, Measurement, 1999, 25, (2), pp. 135142.
    9. 9)
      • 9. Qian, W., Xiao, Y., Yong, R.: ‘Spectrum leakage suppression for multi-frequency signal based on DFT’. 2017 13th IEEE Int. Conf. on Electronic Measurement & Instruments (ICEMI), Yangzhou, China, 2017, pp. 394399.
    10. 10)
      • 10. Morsbach, F., Desbiolles, L., Raupach, R., et al: ‘Noise texture deviation: a measure for quantifying artifacts in computed tomography images with iterative reconstructions’, Invest. Radiol., 2017, 52, (2), pp. 8794.
    11. 11)
      • 11. Dolly, S., Chen, H.C., Anastasio, M., et al: ‘Practical considerations for noise power spectra estimation for clinical CT scanners’, J. Appl. Clin. Med. Phys., 2016, 17, (3), pp. 392407.
    12. 12)
      • 12. Li, G., Liu, X., Dodge, C.T., et al: ‘A noise power spectrum study of a new model-based iterative reconstruction system: Veo 3.0’, J. Appl. Clin. Med. Phys., 2016, 17, (5), pp. 428439.
    13. 13)
      • 13. Ghani, M.U., Ren, L., Wong, M., et al: ‘Noise power characteristics of a micro-computed tomography system’, J. Comput. Assist. Tomogr., 2017, 41, (1), pp. 8289.
    14. 14)
      • 14. Solomon, J., Samei, E.: ‘Are uniform phantoms sufficient to characterize the performance of iterative reconstruction in CT?’. Medical Imaging 2013: Physics of Medical Imaging, Proc. SPIE, Lake Buena Vista, USA, 2013, vol. 8668.
    15. 15)
      • 15. Walek, P., Jan, J., Ourednicek, P., et al: ‘Methodology for estimation of tissue noise power spectra in iteratively reconstructed MDCT data’. 21st Int. Conf. in Central Europe on Computer Graphics, Visualization and Computer Vision, WSCG 2013 – Full Papers Proc., Pilsen, 2013, pp. 243252.
    16. 16)
      • 16. Takita, K., Muquit, M.A., Aoki, T., et al: ‘A sub-pixel correspondence search technique for computer vision applications’, IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 2004, E87-A, (8), pp. 19131923.
    17. 17)
      • 17. Foroosh, H., Zerubia, J.B., Berthod, M.: ‘Extension of phase correlation to subpixel registration’, IEEE Trans. Image Process., 2002, 11, (3), pp. 188200.
    18. 18)
      • 18. Kolar, R., Harabis, V., Odstrcilik, J.: ‘Hybrid retinal image registration using phase correlation’, Imaging Sci. J., 2013, 61, (4), pp. 369384.
    19. 19)
      • 19. Henke, M., Junker, A., Neumann, K., et al: ‘Automated alignment of multi-modal plant images using integrative phase correlation approach’, Front. Plant Sci., 2018, 9, pp. 110.
    20. 20)
      • 20. Ye, Z., Tong, X., Xu, Y., et al: ‘An improved subpixel phase correlation method with application in videogrammetric monitoring of shaking table tests’, Photogramm. Eng. Remote Sens., 2018, 84, (9), pp. 579592.
    21. 21)
      • 21. Tomar, G., Singh, S.C., Montagner, J.P.: ‘Sub-sample time shift and horizontal displacement measurements using phase-correlation method in time-lapse seismic’, Geophys. Prospect., 2017, 65, (2), pp. 407425.
    22. 22)
      • 22. Sikora, T.: ‘Low complexity shape-adaptive DCT for coding of arbitrarily shaped image segments’, Signal Process. Image Commun., 1995, 7, (4-6), pp. 381395.
    23. 23)
      • 23. Kumar, S., Singh, K., Saxena, R.: ‘Analysis of Dirichlet and generalized hamming window functions in the fractional Fourier transform domains’, Signal Process., 2011, 91, (3), pp. 600606.
    24. 24)
      • 24. Welch, P.: ‘The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms’, IEEE Trans. Audio Electroacoust., 1967, 15, (2), pp. 7073.
    25. 25)
      • 25. Soille, P.: ‘Morphological image analysis’ (Springer-Verlag, Berlin, 2003).
    26. 26)
      • 26. Hildebrand, T., Ruegsegger, P.: ‘A new method for the model-independent assessment of thickness in three-dimensional images’, J. Microsc., 1997, 185, (1), pp. 6775.
    27. 27)
      • 27. Blum, H., Nagel, R.N.: ‘Shape description using weighted symmetric axis features’, Pattern Recognit., 1978, 10, (3), pp. 167180.
    28. 28)
      • 28. Choi, W.P., Lam, K.M., Siu, W.C.: ‘Extraction of the Euclidean skeleton based on a connectivity criterion’, Pattern Recognit., 2003, 36, (3), pp. 721729.
    29. 29)
      • 29. Moreno, R., Borga, M., Smedby, Ö.: ‘Estimation of trabecular thickness in gray-scale images through granulometric analysis’, 2012, p. 831451.
    30. 30)
      • 30. Geckinli, N., Yavuz, D.: ‘Some novel windows and a concise tutorial comparison of window families’, IEEE Trans. Acoust. Speech Signal Process., 1978, 26, (6), pp. 501507.
    31. 31)
      • 31. Klein, S., Staring, M., Pluim, J.P.W.: ‘Evaluation of optimization methods for nonrigid medical image registration using mutual information and B-splines’, IEEE Trans. Image Process., 2007, 16, (12), pp. 28792890.
    32. 32)
      • 32. Boedeker, K.L., Cooper, V.N., McNitt-Gray, M.F.: ‘Application of the noise power spectrum in modern diagnostic MDCT: part I. Measurement of noise power spectra and noise equivalent quanta’, Phys. Med. Biol., 2007, 52, (14), pp. 40274046.
    33. 33)
      • 33. Siewerdsen, J.H., Cunningham, I.A., Jaffray, D.A.: ‘A framework for noise-power spectrum analysis of multidimensional images’, Med. Phys., 2002, 29, (11), p. 2655.
    34. 34)
      • 34. Yang, K., Kwan, A.L.C., Huang, S.Y., et al: ‘Noise power properties of a cone-beam CT system for breast cancer detection’, Med. Phys., 2008, 35, (12), p. 5317.
    35. 35)
      • 35. Dolly, S., Chen, H.C., Anastasio, M., et al: ‘Practical considerations for noise power spectra estimation for clinical CT scanners’, J. Appl. Clin. Med. Phys., 2016, 17, (3), pp. 392407.
    36. 36)
      • 36. Jain, M., Mandloi, A.S., Parihar, A., et al: ‘A new spectral efficient window for designing of efficient FIR filter’. 2015 Int. Conf. on Computer, Communication and Control (IC4), Indore, India, 2015, pp. 14.
    37. 37)
      • 37. Mohindru, P., Khanna, R., Bhatia, S.S.: ‘Spectral analysis of generalized triangular and welch window functions using fractional Fourier transform’, Automatika, 2016, 57, (1), pp. 221229.
    38. 38)
      • 38. Walek, P., Jan, J., Ourednicek, P., et al: ‘Preprocessing for quantitative statistical noise analysis of MDCT brain images reconstructed using hybrid iterative (iDose) algorithm’, J. WSCG, 2012, 20, pp. 7380.
    39. 39)
      • 39. Foi, A., Katkovnik, V., Egiazarian, K.: ‘Signal-dependent noise removal in pointwise shape-adaptive DCT domain with locally adaptive variance’, Eur. Signal Process. Conf., 2007, 16, (5), pp. 21592163.
    40. 40)
      • 40. Foi, A., Katkovnik, V., Egiazarian, K.: ‘Pointwise shape-adaptive DCT for high-quality denoising and deblocking of grayscale and color images’, IEEE Trans. Image Process., 2007, 16, (5), pp. 13951411.
    41. 41)
      • 41. Jamunarani, M., Vasanthanayaki, C.: ‘Shape-adaptive DCT compression for high quality surveillance using wireless sensor networks’, Cluster Comput., 2018, 1, pp. 111.
    42. 42)
      • 42. Belalia, A., Belloulata, K., Kpalma, K.: ‘Region-based image retrieval using shape-adaptive DCT’, Int. J. Multimed. Inf. Retr., 2015, 4, (4), pp. 261276.
    43. 43)
      • 43. Belalia, A., Belloulata, K., Kpalma, K.: ‘Region-based image retrieval in the compressed domain using shape-adaptive DCT’, Multimedia Tools Appl., 2016, 75, (17), pp. 1017510199.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2018.5697
Loading

Related content

content/journals/10.1049/iet-ipr.2018.5697
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address