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Robust Focus Measure Operator Using Adaptive Log-Polar Mapping for Three-Dimensional Shape Recovery

Published online by Cambridge University Press:  10 March 2015

Ik-Hyun Lee
Affiliation:
Media Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139USA
Muhammad Tariq Mahmood
Affiliation:
School of Computer Science and Engineering, Korea University of Technology and Education, 1600 Chungjeolno, Byeogchunmyun, Cheonan, Chungnam 330-708, Republic of Korea
Tae-Sun Choi*
Affiliation:
School of Mechatronics, Gwangju Institute of Science and Technology, 123 Cheomdan Gwagiro, Buk-Gu, Gwangju 500-712, Republic of Korea
*
*Corresponding author. tschoi@gist.ac.kr
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Abstract

Shape from focus (SFF) is a passive optical technique that reconstructs object shape from a sequence of image taken at different focus levels. In SFF techniques, computing focus measurement for each pixel in the image sequence, through a focus measure operator, is the fundamental step. Commonly used focus measure operators compute focus quality in Cartesian space and suffer from erroneous focus quality and lack in robustness. Thus, they provide erroneous depth maps. In this paper, we introduce a new focus measure operator that computes focus quality in log-polar transform (LPT) Properties of LPT, such as biological inspiration, data selection, and edge invariance, enable computation of better focus quality in the presence of noise. Moreover, instead of using a fixed patch of the image, we suggest the use of an adaptive window. The focus quality is assessed by computing variation in LPT. The effectiveness of the proposed technique is evaluated by conducting experiments using image sequences of different simulated and real objects. The comparative analysis shows that the proposed method is robust and effective in the presence of various types of noise.

Type
Materials Applications
Copyright
© Microscopy Society of America 2015 

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References

Ahmad, M. & Choi, T. (2007). Application of three dimensional shape from image focus in lcd/tft displays manufacturing. Consumer Electronics IEEE Trans 53(1), 14.CrossRefGoogle Scholar
Berton, F., Sandini, G. & Metta, G. (2006). Anthropomorphic visual sensors. Encyclopedia of Sensors 10, 116.Google Scholar
Bolduc, M. & Levine, M. (1998). A review of biologically motivated space-variant data reduction models for robotic vision* 1. Comput Vis Image Underst 69(2), 170184.Google Scholar
Braccini, C., Gambardella, G., Sandini, G. & Tagliasco, V. (1982). A model of the early stages of the human visual system: Functional and topological transformations performed in the peripheral visual field. Biol Cybern 44(1), 4758.Google Scholar
Brenner, J., Dew, B., Horton, J., King, T., Neurath, P. & Selles, W. (1976). An automated microscope for cytologic research a preliminary evaluation. J Histochem Cytochem 24(1), 100111.CrossRefGoogle ScholarPubMed
Bruno, O., de Oliveira Plotze, R., Falvo, M. & de Castro, M. (2008). Fractal dimension applied to plant identification. Inf Sci 178(12), 27222733.Google Scholar
Caviedes, J. & Oberti, F. (2004). A new sharpness metric based on local kurtosis, edge and energy information. Signal Process Image Commun 19(2), 147161.Google Scholar
Favaro, P., Soatto, S., Burger, M. & Osher, S. (2008). Shape from defocus via diffusion. IEEE Trans Pattern Anal Mach Intell 30(3), 518531.Google Scholar
Firestone, L., Cook, K., Culp, K., Talsania, N. & Preston, K. Jr (1991). Comparison of autofocus methods for automated microscopy. Cytometry 12(3), 195206.CrossRefGoogle ScholarPubMed
Fischl, B., Cohen, M. & Schwartz, E. (1998). Rapid anisotropic diffusion using space-variant vision. Int J Comput Vis 28(3), 199212.CrossRefGoogle Scholar
Gillespie, J. & King, R. (1989). The use of self-entropy as a focus measure in digital holography. Pattern Recognit Lett 9(1), 1925.Google Scholar
Grosso, E., Manzotti, R., Tiso, R. & Sandini, G. (1995). A space-variant approach to oculomotor control. In Proceedings of International Symposium on Computer Vision. pp. 509514, IEEE.CrossRefGoogle Scholar
Hasinoff, S.W. & Kutulakos, K.N. (2009). Confocal stereo. Int J Comput Vis 81(1), 82104.CrossRefGoogle Scholar
Jang, J., Park, K., Kim, J. & Lee, Y. (2008). New focus assessment method for iris recognition systems. Pattern Recognit Lett 29(13), 17591767.CrossRefGoogle Scholar
Javier Traver, V. & Bernardino, A. (2010). A review of log-polar imaging for visual perception in robotics. Rob Auton Syst 58(4), 378398.CrossRefGoogle Scholar
Jurie, F. (1999). A new log-polar mapping for space variant imaging: Application to face detection and tracking. Pattern Recognit 32(5), 865875.CrossRefGoogle Scholar
Kristan, M., Pers, J., Perse, M. & Kovacic, S. (2006). A bayes-spectral-entropy-based measure of camera focus using a discrete cosine transform. Pattern Recognit Lett 27(13), 14311439.CrossRefGoogle Scholar
Krüger, V. (1995). Optical flow estimation in the complex logarithmic plane. Master’s Thesis. University of Kiel, Germany.Google Scholar
Lee, I., Tariq Mahmood, M. & Choi, T.-S. (2013). Adaptive window selection for 3d shape recovery from image focus. Optics & Laser Technology 45, 2131 .CrossRefGoogle Scholar
Mahmood, M., Khan, A. & Choi, T. (2009). Approximating 3d shape through bezier curve and moments in discrete cosine transform. Int J Innov Comput Inf Control 5(10), 29472957.Google Scholar
Mahmood, M.T., Majid, A. & Choi, T. (2011). Optimal depth estimation by combining focus measures using genetic programming. Inf Sci 181(7), 12491263.Google Scholar
Majid, A., Mahmood, M. & Choi, T. (2010). Optimal composite depth function for 3D shape recovery of microscopic objects. Microsc Res Tech 73(7), 657661.CrossRefGoogle ScholarPubMed
Malik, A. & Choi, T. (2007). Application of passive techniques for three dimensional cameras. Consumer Electronics IEEE Trans 53(2), 258264.CrossRefGoogle Scholar
Malik, A. & Choi, T. (2009). Comparison of polymers: a new application of shape from focus. Syst Man Cybern C Appl Rev IEEE Trans 39(2), 246250.CrossRefGoogle Scholar
Nair, H. & Stewart, C. (1992). Robust focus ranging. In Proceedings CVPR’92., 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 309–314. IEEE.Google Scholar
Nayar, S. & Nakagawa, Y. (1994). Shape from focus. IEEE Trans Pattern Anal Mach Intell 16(8), 824831.Google Scholar
Papakostas, G., Boutalis, Y., Karras, D. & Mertzios, B. (2007). A new class of zernike moments for computer vision applications. Inf Sci 177(13), 28022819.CrossRefGoogle Scholar
Pentland, A. (1987). A new sense for depth of field. Pattern Anal Mach Intell IEEE Trans 4, 523531.Google Scholar
Rajagopalan, A. & Chaudhuri, S. (1999). An mrf model-based approach to simultaneous recovery of depth and restoration from defocused images. Pattern Anal Mach Intell IEEE Trans 21(7), 577589.CrossRefGoogle Scholar
Sandini, G. & Tagliasco, V. (1980). An anthropomorphic retina-like structure for scene analysis. Comput Graphics Image Process 14(4), 365372.Google Scholar
Santos, A., Ortiz de Solorzano, C., Vaquero, J.J., Pena, J.M., Malpica, N. & Del Pozo, F. (1997). Evaluation of autofocus functions in molecular cytogenetic analysis. J Microsc 188(3), 264272.Google Scholar
Sarkar, N. & Chaudhuri, B. (1994). An efficient differential box-counting approach to compute fractal dimension of image. Syst Man Cybernet IEEE Trans 24(1), 115120.CrossRefGoogle Scholar
Schwartz, E. (1977). Spatial mapping in the primate sensory projection: Analytic structure and relevance to perception. Biol Cybernet 25(4), 181194.Google Scholar
Shim, S. & Choi, T. (2010). A novel iterative shape from focus algorithm based on combinatorial optimization. Pattern Recognit 43(10), 33383347.CrossRefGoogle Scholar
Solari, F., Chessa, M. & Sabatini, S.P. (2012). Design strategies for direct multi-scale and multi-orientation feature extraction in the log-polar domain. Pattern Recognit Lett 33(1), 4151.Google Scholar
Subbarao, M. & Choi, T. (1995). Accurate recovery of three-dimensional shape from image focus. Pattern Anal Mach Intell IEEE Trans 17(3), 266274.CrossRefGoogle Scholar
Subbarao, M., Choi, T. & Nikzad, A. (1993). Focusing techniques (journal paper). Opt Eng 32(11), 28242836.CrossRefGoogle Scholar
Subbarao, M. & Lu, M. (1994). Image sensing model and computer simulation for ccd camera systems. Mach Vis Appl 7(4), 277289.Google Scholar
Subbarao, M. & Tyan, J. (1998). Selecting the optimal focus measure for autofocusing and depth-from-focus. Pattern Anal Mach Intell IEEE Trans 20(8), 864870.CrossRefGoogle Scholar
Sun, Y., Duthaler, S. & Nelson, B. (2004). Autofocusing in computer microscopy: Selecting the optimal focus algorithm. Microsc Res Tech 65(3), 139149.Google Scholar
Tenenbaum, J. (1970). Accommodation in computer vision. Doctoral Dissertation, Stanford University, CA, USAGoogle Scholar
Thelen, A., Frey, S., Hirsch, S. & Hering, P. (2009). Improvements in shape-from-focus for holographic reconstructions with regard to focus operators, neighborhood-size, and height value interpolation. Image Process IEEE Trans 18(1), 151157.CrossRefGoogle ScholarPubMed
Tian, J., Chen, L., Ma, L. & Yu, W. (2011). Multi-focus image fusion using a bilateral gradient-based sharpness criterion. Opt Commun 284(1), 8087.Google Scholar
Tian, Y., Shieh, K. & Wildsoet, C. (2007). Performance of focus measures in the presence of nondefocus aberrations. JOSA A 24(12), B165B173.Google Scholar
Tistarelli, M. & Sandini, G. (1993). On the advantages of polar and log-polar mapping for direct estimation of time-to-impact from flow. IEEE Trans Pattern Anal Mach Intell 15(4), 401410.Google Scholar
Traver, V. (2002). Motion estimation algorithms in log-polar images and application to monocular active tracking. PhD Thesis. Dep. Llenguatges.Google Scholar
Traver, V. & Pla, F. (2005). Similarity motion estimation and active tracking through spatial-domain projections on log-polar images. Comput Vis Image Underst 97(2), 209241.Google Scholar
Traver, V. & Pla, F. (2008). Log-polar mapping template design: From task-level requirements to geometry parameters. Image Vis Comput 26(10), 13541370.Google Scholar
Wallace, A. & McLaren, D. (2003). Gradient detection in discrete log-polar images. Pattern Recognit Lett 24(14), 24632470.Google Scholar
Wallace, R., Ong, P., Bederson, B. & Schwartz, E. (1994). Space variant image processing. Int J Comput Vis 13(1), 7190.CrossRefGoogle Scholar
Wee, C. & Paramesran, R. (2007). Measure of image sharpness using eigenvalues. Inf Sci 177(12), 25332552.Google Scholar
Weiman, C. & Chaikin, G. (1979). Logarithmic spiral grids for image processing and display. Comput Graphics Image Process 11(3), 197226.Google Scholar
Xie, H., Rong, W. & Sun, L. (2007). Construction and evaluation of a wavelet-based focus measure for microscopy imaging. Microsc Res Tech 70(11), 987995.CrossRefGoogle ScholarPubMed
Yap, P. & Raveendran, P. (2004). Image focus measure based on chebyshev moments. In Proceedings of IEE Vision, Image and Signal Processing, 151(2), pp. 128–136. IET.Google Scholar
Yun, J. & Choi, T. (1999). Accurate 3-d shape recovery using curved window focus measure. In Proceedings of International Conference on ICIP 1999 Image Processing, 3, pp. 910–914. IEEE.Google Scholar