Abstract

Non-contact three-dimensional (3D) measuring technology is used to identify defects in miniature products, such as optics, polymers, and semiconductors. Hence, this technology has garnered significant attention in computer vision research. In this paper, we focus on shape from focus (SFF), which is an optical passive method for 3D shape recovery. In existing SFF techniques using interpolation, all datasets of the focus volume are approximated using one model. However, these methods cannot demonstrate how a predefined model fits all image points of an object. Moreover, it is not reasonable to explain various shapes of datasets using one model. Furthermore, if noise is present in the dataset, an error will be generated. Therefore, we propose an algorithm based on polynomial regression analysis to address these disadvantages. Our experimental results indicate that the proposed method is more accurate than existing methods.

PDF Article

References

  • View by:
  • |

  1. T.-S. Choi and M. A. S. MalikVision and ShapeSejong Publishing CompanyKorea2008Chapter 1
  2. M. Muhammad and T.-S. ChoiSampling for shape from focus in optical microscopyIEEE Trans. Patten. Anal. Mach. Intel.201234564573
  3. S. Pertuz, D. Puig, and M. A. GarciaAnalysis of focus measure operators for shape-from-focusPattern Recognit.20134614151432
  4. M. T. Mahmood, W. J. Choi, and T.-S. ChoiPCA-based method for 3D shape recovery of microscopic objects from image focus using discrete cosine transformMicrosc. Res. Tech.200871897907
  5. M. J. Russell and T. S. DouglasEvaluation of autofocus algorithms for tuberculosis microscopyProc. 29th Annual International Conference of the IEEE Engineering in Medicine and Biology SocietyLyon, France2007Aug.34893492
  6. Y. Sun, S. Duthaler, and B. J. NelsonAutofocusing in computer microscopy: selecting the optimal focus algorithmMicros. Res. Tech.200465139149
  7. H.-J. Kim, M. T. Mahmood, and T.-S. ChoiAn efficient neural network for shape from focus with weight passing methodAppl. Sci.201881648
  8. S.-O. Shim, W. Aziz, A. Banjar, A. Alamri, and M. AlqarniImproving depth computation from robust focus approximationIEEE Access201972014420149
  9. W. Huang and Z. JingEvaluation of focus measures in multi-focus image fusionPattern Recognit. Lett.200728493500
  10. A. M. Eskicioglu and P. S. FisherImage quality measures and their performanceIEEE Trans. Commun.19954329592965
  11. M. T. Mahmood, S.-O. Shim, and T.-S. ChoiWavelet and PCA-based approach for 3D shape recovery from image focusProc. SPIE2008707370731S
  12. C. Y. Wee and R. ParamesranImage sharpness measure using eigenvaluesProc. 9th International Conference on Signal ProcessingBeijing, China2008Oct.840843
  13. S. K. Nayar and Y. NakagawaShape from focusIEEE Trans. Patten. Anal. Mach. Intel.199416824831
  14. M. T. Mahmood, S.-O. Shim, A. Khan, and T.-S. ChoiAccurate Depth Approximation through Bezier-Bernstein Polynomial for 3D CamerasProc. Digest of Technical Papers International Conference on Consumer ElectronicsLas Vegas, USA2009Jan.12
  15. M. S. Muhammad and T.-S. Choi3D shape recovery by image focus using Lorentzian-Cauchy functionProc. IEEE International Conference on Image ProcessingHong Kong, China2010Sep.40654068
  16. M. S. Muhammad and T.-S. ChoiA novel method for shape from focus in microscopy using Bezier surface approximationMicrosc. Res. Technol.201073140151
  17. A. S. Malik, T. L. Song, and T.-S. ChoiDepth map estimation based on linear regression using image focusInt. J. Imaging. Syst. Technol.201121241246
  18. A. S. Malik and T.-S. ChoiA novel algorithm for estimation of depth map using image focus for 3D shape recovery in the presence of noisePattern Recognit.20084122002225
  19. Y. Frommer, R. Ben-Ari, and N. KiryatiX. Xie, M. W. Jones, and G. K. L. TamShape from focus with adaptive focus measure and high order derivativesProc. British Machine Vision Conference (BMVC)BMVA PressUK2015134.1134.12
  20. Z. Wang, M. Lei, B. Yao, Y. Cai, Y. Liang, Y. Yang, X. Yang, H. Li, and D. XiongCompact multi-band fluorescent microscope with an electrically tunable lens for auto-focusingBiomed. Opt. Express2015643534364
  21. N. R. Draper and H. SmithApplied Regression Analysis3rd Ed.John Wiley & SonsUS1998Chapter 25
  22. H. Xie, W. Rong, and L. SunConstruction and evaluation of a wavelet-based focus measure for microscopy imagingMicrosc. Res. Tech.200770987995
  23. S.-A. Lee, H.-S. Jang, and B.-G. LeeJitter elimination in shape recovery by using adaptive neural network filterSensors2019192566

Other (23)

T.-S. Choi and M. A. S. MalikVision and ShapeSejong Publishing CompanyKorea2008Chapter 1

M. Muhammad and T.-S. ChoiSampling for shape from focus in optical microscopyIEEE Trans. Patten. Anal. Mach. Intel.201234564573

S. Pertuz, D. Puig, and M. A. GarciaAnalysis of focus measure operators for shape-from-focusPattern Recognit.20134614151432

M. T. Mahmood, W. J. Choi, and T.-S. ChoiPCA-based method for 3D shape recovery of microscopic objects from image focus using discrete cosine transformMicrosc. Res. Tech.200871897907

M. J. Russell and T. S. DouglasEvaluation of autofocus algorithms for tuberculosis microscopyProc. 29th Annual International Conference of the IEEE Engineering in Medicine and Biology SocietyLyon, France2007Aug.34893492

Y. Sun, S. Duthaler, and B. J. NelsonAutofocusing in computer microscopy: selecting the optimal focus algorithmMicros. Res. Tech.200465139149

H.-J. Kim, M. T. Mahmood, and T.-S. ChoiAn efficient neural network for shape from focus with weight passing methodAppl. Sci.201881648

S.-O. Shim, W. Aziz, A. Banjar, A. Alamri, and M. AlqarniImproving depth computation from robust focus approximationIEEE Access201972014420149

W. Huang and Z. JingEvaluation of focus measures in multi-focus image fusionPattern Recognit. Lett.200728493500

A. M. Eskicioglu and P. S. FisherImage quality measures and their performanceIEEE Trans. Commun.19954329592965

M. T. Mahmood, S.-O. Shim, and T.-S. ChoiWavelet and PCA-based approach for 3D shape recovery from image focusProc. SPIE2008707370731S

C. Y. Wee and R. ParamesranImage sharpness measure using eigenvaluesProc. 9th International Conference on Signal ProcessingBeijing, China2008Oct.840843

S. K. Nayar and Y. NakagawaShape from focusIEEE Trans. Patten. Anal. Mach. Intel.199416824831

M. T. Mahmood, S.-O. Shim, A. Khan, and T.-S. ChoiAccurate Depth Approximation through Bezier-Bernstein Polynomial for 3D CamerasProc. Digest of Technical Papers International Conference on Consumer ElectronicsLas Vegas, USA2009Jan.12

M. S. Muhammad and T.-S. Choi3D shape recovery by image focus using Lorentzian-Cauchy functionProc. IEEE International Conference on Image ProcessingHong Kong, China2010Sep.40654068

M. S. Muhammad and T.-S. ChoiA novel method for shape from focus in microscopy using Bezier surface approximationMicrosc. Res. Technol.201073140151

A. S. Malik, T. L. Song, and T.-S. ChoiDepth map estimation based on linear regression using image focusInt. J. Imaging. Syst. Technol.201121241246

A. S. Malik and T.-S. ChoiA novel algorithm for estimation of depth map using image focus for 3D shape recovery in the presence of noisePattern Recognit.20084122002225

Y. Frommer, R. Ben-Ari, and N. KiryatiX. Xie, M. W. Jones, and G. K. L. TamShape from focus with adaptive focus measure and high order derivativesProc. British Machine Vision Conference (BMVC)BMVA PressUK2015134.1134.12

Z. Wang, M. Lei, B. Yao, Y. Cai, Y. Liang, Y. Yang, X. Yang, H. Li, and D. XiongCompact multi-band fluorescent microscope with an electrically tunable lens for auto-focusingBiomed. Opt. Express2015643534364

N. R. Draper and H. SmithApplied Regression Analysis3rd Ed.John Wiley & SonsUS1998Chapter 25

H. Xie, W. Rong, and L. SunConstruction and evaluation of a wavelet-based focus measure for microscopy imagingMicrosc. Res. Tech.200770987995

S.-A. Lee, H.-S. Jang, and B.-G. LeeJitter elimination in shape recovery by using adaptive neural network filterSensors2019192566

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.