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標題: 以調適性視窗聚焦量測法建立深度地圖並達成3D形狀重建
3D Shape Recovery Based on Depth Map Generation Using Focus Measures in Adaptive Sized Windows
作者: 吳聲遠
Wu, Sheng-Yuan
關鍵字: 3D shape recovery
focus measure
depth map
出版社: 資訊科學與工程學系所
引用: [1] Aamir S. Malik and Tae-Sun Choi, “Consideration of Illumination Effects and Optimization of Window Size for Accurate Calculation of Depth Map for 3D Shape Recovery,” Pattern Recognition, Vol. 40, No. 1, pp 154-170, Jan 2007. [2] E. Krotkov, “Focusing,” Int. J. Computer Vision, vol. 1, pp. 223-237,Oct. 1987. [3] Firestone L, Cook K, Culp K, Talsania N, Preston K. “Comparison of Autofocus Methods for Automated Microscopy,” Cytometry 1991; 12: 195-206. [4] Groen FCA, Young IT, Ligthart G. “A Comparison of Different Focus Functions for Use in Autofocus Algorithms,” Cytometry 1985; 6:81-91. [5] Johnson ET, Goforth LJ. “Metaphase Spread Detection and Focus Using Closed Circuit Television,” J Histochem Cytochem 1974;22:536-545. [6] Lockett SJ, Jacobson K, Herman B. “Application of 3D Digital Deconvolution to Optically Sectioned Images for Improving the Automatic Analysis of Fluorescent-Labeled Tumor Specimens,” Proc SPIE 1992; 1660:130-139. [7] Mason DC, Green DK. “Automatic Focusing of A Computer-Controlled Microscope,” IEEE Trans Biomed Eng 1975;22:312-317. [8] Mendelsohn ML, Mayall BH. “Computer-Oriented Analysis of Human Chromosomes-iii: Focus,” Comput Biol Med 1971;2:137-150. [9] Ligthart, G., Groen, F., 1982. “A Comparison of Different Autofocus Algorithms,” In: Proc. Int. Conf. on Pattern Recognition. pp. 597-600. [10] Papoulis A. “The Fourier Integral and Its Applications,” New York: McGraw-Hill; 1960. 318 p. [11] Boddeke FR, van Vliet LJ, Netten H, Young IT. “Autofocusing in Microscopy Based on The OTF And Sampling,” Bioimaging 1994;2:193- 203. [12] Subbarao and Tyan, 1998. M. Subbarao and J.K. Tyan , “Selecting the Optimal Focus Measure for Autofocusing And Depth-From-Focus,” IEEE Trans. Pattern Analysis and Machine Intelligence 20 (1998), pp. 864-870. [13] F.S. Helmli, S. Scherer, “Adaptive Shape From Focus with An error Estimation in Light Microscopy,” Second International Symposium on Image and Signal Processing and Analysis, 2001. [14] S.K. Nayar,Y. Nakagawa, “Shape From Focus: An Effective Approach for Rough Surfaces,” International conference on Robotics and Automation (1990) 218-225. [15] S.K. Nayar, Y. Nakagawa, “Shape From Focus,” IEEE Trans. Pattern Anal. Mach. Intell. 16 (8) (1994) 824-831. [16] W. Huang, Z.L. Jing, “Evaluation of Focus Measures In Multi-Focus Image Fusion,” Pattern Recognition Letters, vol. 28, pp. 493-500, 2007. [17] Eskicioglu, A.M., Fisher, P.S., 1995. “Image Quality Measures And Their Performance,” IEEE Trans. Commun. 43 (12), 2959-2965. [18] B. K. P. Horn, “Focusing,” MIT Artificial Intelligence Laboratory, Memo No. 10, May 1968. [19] Gunn S R 1999 “On The Discrete Presentation of The Laplacian of A Gaussian,” Pattern Recognit. 32 1463-72 [20] J. F. Schlag, A. C. Sanderson, C. P. Neumann, and F. C.Wimberly, “Implementation of Automatic Focusing Algorithms for A Computer Vision System with Camera Control,” Carnegie Mellon Univ., Pittsburgh, PA, CMU-RI-TR-83-14, Aug. 1983. [21] C. Yim and A. C. Bovik, “Range Segmentation Using Focus Cues,” in Proc. IEEE Int. Symp. Computer Vision, Nov. 1995, pp. 329-334. [22] T. Darrell and K. Wohn, “Pyramid Based Depth From Focus,” in Proc. CVPR, Jun. 1988, pp. 54-509. [23] Shree K. Nayar and Yasuo Nakagawa, “Shape From Focus,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, No. 8, 1994 [24] Murali Subbarao and Tae Choi, “Accurate Recovery of Three-Dimensional Shape from Image Focus,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 17, No. 3, 1995. [25] Tae S. Choi, Muhammad Asif, Joungil Yun, “Three-Dimensional Shape Recovery from Focused Image Surface,” IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3269-3272, 1999. [26] M. Asif, T.-S. Choi, “Shape From Focus Using Multilayer Feedforward Neural Network,” IEEE Trans. Image Process, 10 (11) pp. 1670-1675, 2001. [27] Muhammad Bilal Ahmad and Tae-Sun Choi, “A Heuristic Approach For Finding Best Focused Shape,” IEEE Trans. Circuits and Systems for Video Technology, vol. 15, No. 4, 2005.
摘要: 傳統的2D影像僅能顯示一物件之表面形狀及其平面方向之運行情況,而3D影像則多了Z軸座標來描述該物件與觀察者之間的距離,使其所觀測之物件具有一定的立體感。3D形狀重建(Shape Recovery)即是一個能將一物件由2D影像轉換為3D影像描述的方法,因此如何獲得一張準確的深度地圖一直是3D形狀重建的目標,其中對待測影像選取適當的聚焦量測法來建立深度地圖是一個很重要的部分,在過去文獻中也提出了許多方法,其中Sum Modified Laplcian (SML) 是用固定視窗大小作聚焦程度的衡量,若視窗過大,則建立的深度地圖會過度平滑;反之,若視窗過小,則建立的深度地圖受雜訊影響甚大,於是本論文以SML為基礎,提出了一個調適性視窗演算法。經實驗證明此演算法能改善視窗大小的影響因而獲得準確的深度地圖。
其他識別: U0005-1008200821131500
Appears in Collections:資訊科學與工程學系所



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