磁化曲線和磁滯回線測(cè)量

1、36 Kalle Karn, et c. Fingerprint Classification .Pattern Recognition, 1996, 29 (3:389-404.37 Ratha N K, Chen S Y, Jain A K. Adaptive flow orientation-based feature extraction in fingerprint images .Pattern Recognition,1995,28 (10:1657-1672.38 Hong L, Wan Y F, Jain A. Fingerprint image enhancement: a

2、lgorithm and performance evaluation.工EEE Trans on PAMI，1998, 20 (8:777-789.39 He Y,Kundu A.2D shape classification using hidden Markov models.IEEE Transactions on PAMI，1991, 13 (11:11721181.40 Liang K H, Tjahjadi T, Yang Y H. Roof edge detection using regularized cubic B一 spline fitting .Pattern Rec

3、ognition, 1997, 30(5:719-728. 41 Stockman G, Kopstein S, Benett S. Matching images to models for registration and detection via clustering.工EEE, Transactions on Pattern Analysis and Machine intelligence. 1982, 2 (3:229-211.42 Xudong Jiang, Weiyun Yau. Fingerprint Minutiae Matching Based on the Local

4、 and Global Structures.IEEE, 2000, 1038-1041.43 Conti V,Militello C, Sorbello R et al. Introducing pseudo-singularity points for efficient fingerprints classification and recognitionJ.CISIS,2010:368-375.44 B.Moayer and K.Fu. A tree system approach for fingerprint pattern recognitionJ. Pattern Recogn

5、ition. 1990, 23(8:893-904.45 B.Chatterjee, M.Verma and A.Majumdar. Edge detection in fingerprintsJ. Pattern Recognition. 1987, 20:513-523.46 Mehtre B.M, Muthy N.N, Kapoor S, et at. Segmentation of fingerprint image using the direction imageJ. Pattern Recognition. 1987, 20(4:429-435.一 1460. 48 Xudong

6、 Jiang, Weiyun Yau and Wee Ser. Minutiae extraction by adaptive tracing the gray level ridge of the fingerprint imageC. IEEE 6 International Conference on Image Processing, Japan. 1999: 852-856.49 J.D.Liu, Z. Huang, K.L.Chan. Minutiae extraction from gray-level fingerprint image by relationship exam

7、inationJ.IEEE.Image Processing, 2002, 2(9:10-13.50 O'Gonnan ， Nickerson. an Approach to Fingerprint Filter DesignJ. Pattern Recognition,1989, 22(1:29-38. 附錄1 配準(zhǔn)函數(shù)Matlab程序function output Greg = dftregistration(buf1ft,buf2ft,usfac% function output Greg = dftregistration(buf1ft,buf2ft,usfac;% Effic

8、ient subpixel image registration by crosscorrelation. This code% gives the same precision as the FFT upsampled cross correlation in a% small fraction of the computation time and with reduced memory % requirements. It obtains an initial estimate of the crosscorrelation peak% by an FFT and then refine

9、s the shift estimation by upsampling the DFT% only in a small neighborhood of that estimate by means of a % matrix-multiply DFT. With this procedure all the image points are used to % compute the upsampled crosscorrelation.% Manuel Guizar - Dec 13, 2007% Portions of this code were taken from code wr

10、itten by Ann M. Kowalczyk % J.R. Fienup and A.M. Kowalczyk, "Phase retrieval for a complex-valued % object by using a low-resolution image," J. Opt. Soc. Am. A 7, 450-458 % (1990.% Citation for this algorithm:% Manuel Guizar-Sicairos, Samuel T. Thurman, and James R. Fienup, % "Efficie

11、nt subpixel image registration algorithms," Opt. Lett. 33, % 156-158 (2008.% Inputs% buf1ft Fourier transform of reference image, % DC in (1,1 DO NOT FFTSHIFT% buf2ft Fourier transform of image to register, % DC in (1,1 DO NOT FFTSHIFT% usfac Upsampling factor (integer. Images will be registere

12、d to % within 1/usfac of a pixel. For example usfac = 20 means the% images will be registered within 1/20 of a pixel. (default = 1% Outputs% output = error,diffphase,net_row_shift,net_col_shift% error Translation invariant normalized RMS error between f and g% diffphase Global phase difference between the two images (should be% zero if images are non-negative.% net_row_shift net_col_shift Pixel shifts between images% Greg (Optional