Image Pattern Recognition and Its Applications

1. Image Pattern Recognition and Its Applications Chaur-Chin Chen (陳朝欽) Institute of Information Systems & Applications (Department of Computer Science) National Tsing Hua University HsinChu (新竹), Taiwan (台灣) cchen@cs.nthu.edu.tw May 3, 2013

2. Outline • Fundamental Image Processing • Fingerprint and Face Verification • Supervised vs. Unsupervised Learning • Watermarking and Steganography • Microarray Image Analysis • Some Other Application

3. Outline (Continuation) • Some Other Applications • Supervised vs. Unsupervised Learning • Data Description and Representation • 8OX and iris Data Sets • Dendrograms of Hierarchical Clustering • PCA vs. LDA • A Comparison of PCA and LDA

4. Fundamental Image Processing ♪ A Digital Image Processing System • Image Representation and Formats 1.Sensing, Sampling, Quantization 2. Gray level and Color Images 3. Raw, RGB, Tiff, BMP, JPG, GIF, (JP2) • Image Transform and Filtering • Histogram, Enhancement • Segmentation, Edge Detection, Thinning • Image Data Compression • Fingerprint and Face Recognition • Image Pattern Recognition • Watermarking and Steganography • Microarray Image Data Analysis [1] R.C. Gonzalez, R.E. Woods, S.L. Eddins, Digital Image Processing Using MATLAB, Pearson Prentice Hall, 2004 [2] R.C. Gonzalez and R.E. Woods, Digital Image Processing, Prentice-Hall, 2002+

5. Image Processing System • A 2D image is nothing but a mapping from a region to a matrix • A Digital Image Processing System consists of 1. Acquisition – scanners, digital camera, ultrasound, X-ray, MRI, PMT 2. Storage – HD (500GB, TeraBytes, PeraBytes, …), CD (700 MB), DVD (4.7 GB), Flash memory (2~32 GB) 3. Processing Unit – PC, Workstation (Sun Microsystems), PC-cluster 4. Communication – telephone lines, cable, wireless, Wi-Fi, LTE 5. Display – LCD monitor, laser printer, smart phone, i-Pad

6. Illustration of Image Processing System

7. Gray Level and Color Images

8. Pixels in a Gray Level Image

9. A Gray Level Image is a Matrix f(0,0) f(0,1) f(0,2) …. …. f(0,n-1) f(1,0) f(1,1) f(1,2) …. …. f(1,n-1) . . . . . . . . . f(m-1,0) f(m-1,1) f(m-1,2) … …. f(m-1,n-1) An image of m rows, n columns, f(i,j) is in [0,255]

10. Image Representation (Gray/Color) • A gray level image is usually represented by an M x N matrix whose elements are all integers in {0,1, …, 255} corresponding to brightness scales • A color image is usually represented by 3 M x N matrices whose elements are all integers in {0,1, …, 255} corresponding to 3 primary primitives of colors such as Red, Green, Blue

11. Gray and Color Image Data • 0, 64, 144, 196, 225, 169, 100, 36 (R, G, B) for a color pixel Red – (255, 0, 0) Green – ( 0, 255, 0) Blue – ( 0, 0, 255) Cyan – ( 0,255, 255) Magenta – (255, 0, 255) Yellow – (255, 255, 0) Gray – (128, 128, 128)

12. Red = FF0000 Green = 00FF00 Blue = 0000FF Cyan = 00FFFF Magenta= FF00FF Yellow = FFFF00 RGB Hex Triplet Color Chart

13. Koala and Its RGB Components

14. (R,G,B) Histograms of Koala

15. Sensing, Sampling, Quantization • A 2D digital image is formed by a sensor which maps a region to a matrix • Digitization of the spatial coordinates (x,y) in an image function f(x,y) is called Sampling • Digitization of the amplitude of an image function f(x,y) is called Quantization

16. Sampling and Quantization

17. The American National Standards Institute (ANSI) sets standards for voluntary use in US. One of the most popular computer standards set by ANSI is the American Standard Code for Information Interchange (ASCII) which guarantees all computers can exchange text in ASCII format BMP – Bitmap format from Microsoft uses Raster-based 1~24-bit colors (RGB) without compression or allows a run-length compression for 1~8-bit color depths GIF – Graphics Interchange Format from CompuServe Inc. is Raster-based which uses 1~8-bit colors with resolutions up to 64,000*64,000 LZW (Lempel-Ziv-Welch, 1984) lossless compression with the compression ratio up to 2:1 Image File Formats (1/2)

18. Some Image File Formats (2/2) • Raw – Raw image format uses a 8-bit unsigned character to store a pixel value of 0~255 for a Raster-scanned gray image without compression. An R by C raw image occupies R*C bytes or 8RC bits of storage space • TIFF – Tagged Image File Format from Aldus and Microsoft was designed for importing image into desktop publishing programs and quickly became accepted by a variety of software developers as a standard. Its built-in flexibility is both a blessing and a curse, because it can be customized in a variety of ways to fit a programmer’s needs. However, the flexibility of the format resulted in many versions of TIFF, some of which are so different that they are incompatible with each other • JPEG – Joint Photographic Experts Group format is the most popular lossy method of compression, and the current standard whose file name ends with “.jpg” which allows Raster-based 8-bit grayscale or 24-bit color images with the compression ratio more than 16:1 and preserves the fidelity of the reconstructed image • EPS – Encapsulated PostScript language format from Adulus Systems uses Metafile of 1~24-bit colors with compression • JP2 - JPEG 2000 based on 5/3 and 9/7 wavelet transforms

19. Image Transforms and Filtering • Feature Extraction – find all ellipses in an image • Bandwidth Reduction – eliminate the low contrast “coefficients” • Data Reduction – eliminate insignificant coefficients of Discrete Cosine Transform (DCT), Wavelet Transform (WT) • Smooth filtering can get rid of noisy signals

20. Discrete Cosine Transform Partition an image into nonoverlapping 8 by 8 blocks, and apply a 2d DCT on each block to get DC and AC coefficients. Most of the high frequency coefficients become insignificant, only the DC term and some low frequency AC coefficients are significant. Fundamental for JPEG Image Compression

21. Discrete Cosine Transform (DCT) X: a block of 8x8 pixels A=Q8: 8x8 DCT matrix as shown above Y=AXAt

22. Quantized DCT Coefficients on a 8x8 Block

23. Lenna Image vs. Compressed Lenna

24. Wavelet Transform • Haar, Daubechies’ Four, 9/7, 5/3 transforms • 9/7, 5/3 transforms was selected as the lossy and lossless coding standards for JPEG2000, respectively • A Comparison of JPEG and JPEG2000 shows that the latter is slightly better than the former, however, to replace image.jpg by image.jp2 needs time

25. 3-Scale Wavelet Transforms

26. X1 X2 X3 X4 X0 X5 X6 X7 X8 Replace the X0 by the mean of X0~X8 is called “mean filtering” X1 X2 X3 X4 X0 X5 X6 X7 X8 Replace the X0 by the median of X0~X8 is called “median filtering” Mean andMedian Filtering

27. Example of Median Filtering

28. Image and Its Histogram

29. Enhancement and Restoration • The goal of enhancementis to accentuate certain features for subsequent analysis or image display. The enhancement process is usually done interactively • The restoration is a process that attempts to reconstruct or recover an image that has been degraded by using some unknown phenomenon

30. Example of Image Enhancement • Support that A(i, j) is image gray level at pixel (i, j), μ and s2 are the mean and variance of gray levels of input image, and α=150, γ=95, γ must satisfy γ>s. The enhanced image B( i , j ) is obtained by a contrast stretching given below • B( i , j ) α + γ ＊ ([A ( i , j ) – μ]／s)

31. Result of Image Enhancement

32. Segmentation and Edge Detection • Segmentation is basically a process of pixel classification: the picture is segmented into subsets by assigning the individual pixels into classes • Edge Detection is to find the pixels whose gray values or colors being abruptly changed

33. Image Lenna and Its Histogram

34. Image Segmentation Algorithms • Otsu (1979) • Fisher (1936) • Kittler and Illingworth (1986) • Vincent and Soille (1991) • Besag, Chen and Dubes (1986, 1991)

35. A Simple Thresholding Algorithm(1)

36. Image, Histogram, Thresholding

37. Binarization by Thresholding

38. ICM Segmentation Algorithm 1. Given an image Y, initialize a labeling X 2. For t=1:mxn X(t)←g0 if Pr(X(t)=g0|XN(t),Y) > Pr(X(t)=g|XN(t),Y) for g,g0 3. Repeat step 2 until “convergence” (6 runs) 4. X is the required labeling Chaur-Chin Chen and Richard C. Dubes Environmental Studies and ICM Segmentation Algorithm, Journal of Information Science and Engineering, Vol. 6, 325-337, 1990.

39. Image Segmentation: ICM vs. Otsu

40. Image Segmentation: ICM vs. Otsu

41. Image Segmentation: ICM vs. Otsu

42. Edge Detection -1 -2 -1 0 0 0  X 1 2 1 -1 0 1 -2 0 2  Y -1 0 1 Large (|X|+|Y|)  Edge

43. Thinning and Contour Tracing • Thinning is to find the skeleton of an image which is commonly used for Optical Character Recognition (OCR) and Fingerprint matching • Contour tracing is usually used to locate the boundaries of an image which can be used in feature extraction for shape discrimination

44. Image Edge, Skeleton, Contour

45. Image Data Compression • The purpose is to save storage space and to reduce the transmission time of information. Note that it requires 6 mega bits to store a 24-bit color image of size 512 by 512. It takes 6 seconds to download such an image via an ADSL (Asymmetric Digital Subscriber Line) with the rate 1 mega bits per second and more than 12 seconds to upload the same image • Note that 1 byte = 8 bits, 3 bytes = 24 bits

46. Training Images for VQ

47. LBG Algorithm for Codebook Generation

48. Codebook and Decoded Images

49. Some Applications • Fingerprint and Face Recognition • Watermarking and Steganography • Image Pattern Recognition • Microarray Image Data Analysis

50. 美國啟用出入境指紋及人臉影像辨識系統 • 美國國土安全部基於安全考慮，自(2004)元月五日起，啟用數位化出入境身分辨識系統(US-VISIT)，大部分來美的14歲至79歲旅客，包括來自台灣、大陸、香港的留學生，於進入美國國際機場及港口時，都要接受拍照及留下指紋掃描紀錄以便辨識查核。(27個免簽證國公民之入境待遇略有不同，短期來美者，將受豁免。)，亦將需接受指紋掃描查核。