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An Introduction to Pattern Recognition 主講人：朱家德　

An Introduction to Pattern Recognition 主講人：朱家德　. 財團法人資訊工業策進會 (III) 網路多媒體研究所. 大綱. Introduction Image Processing Feature Extraction and Selection Methods Biometrics Recognition Research and Development Results. Introduction. 財團法人資訊工業策進會 (III) 網路多媒體研究所 (NMI).

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An Introduction to Pattern Recognition 主講人：朱家德　

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  1. An Introduction to Pattern Recognition主講人：朱家德　 • 財團法人資訊工業策進會(III) • 網路多媒體研究所

  2. 大綱 • Introduction • Image Processing • Feature Extraction and Selection Methods • Biometrics Recognition • Research and Development Results

  3. Introduction 財團法人資訊工業策進會(III) 網路多媒體研究所(NMI)

  4. What is Pattern Recognition • Definition: The act of taking in raw data and making an action based on the “category” of the pattern. • What is the pattern? The pattern is a picture, a string of characters, a set of symbols, a sequence of signal, etc.

  5. Pattern Recognition

  6. Pattern Recognition • Example The optical sensing is used to automate the process of sorting fish

  7. Machine Perception Face Recognition Character Recognition Computer Aid Diagnosis Speech Recognition

  8. Examples of Application • Handwritten: sorting letters by postal code, input device for PDA‘s. • Printed texts: reading machines for blind people, digitalization of text documents. • Optical Character Recognition (OCR) • Biometrics • Diagnostic systems • Military applications • Face recognition. • Fingerprints recognition. • Speech recognition. • Medical diagnosis: X-Ray, ECG analysis. • Machine diagnostics, waster detection. • Automated Target Recognition (ATR). • Image segmentation and analysis (recognition from aerial or satelite photographs).

  9. Image Processing 財團法人資訊工業策進會(III) 網路多媒體研究所(NMI)

  10. What is image processing • Two main application areas: 1.Improvement of pictorial information 2.Processing of image data for storage, transmission and feature extraction for machine perception.

  11. 2-D Image Model • The 2-D image has a two-dimensional light intensity function f(x,y), where x and y denote spatial coordinates and the value is proportional to the brightness of the image at that point.

  12. 2-D Image Model • In the black and white case, the brightness value are called gray levels(灰階). These values are integer, non-negative, and bounded. • The elements of f(x,y) are called pixels being commonly used abbreviations of “picture elements”. • A digital image is an image f(x,y) which has been discretized both in spatial coordinates and brightness. An image can be considered as a matrix whose row and column indices identify a pixel in the image and the corresponding matrix element value identifies the gray level at that pixel.

  13. 2-D Image Model

  14. Image type Color 彩色影像 • Gray 灰階影像 800x600x3=1440000 pixels 800x600=480000 pixels 每個pixel是由三個値組成 (R, G, B) 每個pixel是由一個値組成

  15. Image type

  16. Image Transform

  17. Image Enhancement • Contrast Enhancement(增強對比)

  18. Noise removing • 去除影像中在影像處理過程所造成的雜訊

  19. Image Smoothing • 去除影像中因不良取像或量化所造成的雜訊,同時也會使影像變模糊

  20. Image Sharpening • 強化影像中物體的邊緣效果

  21. Image Segmentation

  22. Image restoration

  23. Analogue signal

  24. Signal sampling

  25. Effects of sampling • Human visual and audio perception is insensitive to high frequency information. • Telephone system provides sound frequencies to 3 KHz. Human hearing goes up to 20 KHz.

  26. Effects of sampling 128x128 256 x 256 64x64 32x32

  27. Feature Extraction and Selection Methods 財團法人資訊工業策進會(III) 網路多媒體研究所(NMI)

  28. Feature Extraction and Selection Methods Task: to extract features which are good for classification. Good features: • Objects from the same class have similar feature values. • Objects from different classes have different values. “Good” features “Bad” features

  29. Feature Extraction and Selection Methods

  30. Feature Extraction and Selection Methods

  31. Feature Extraction and Selection Methods

  32. Feature Extraction and Selection Methods

  33. Feature Extraction and Selection Methods

  34. Feature Extraction and Selection Methods

  35. Feature Extraction and Selection Methods

  36. Feature Extraction and Selection Methods

  37. Feature Extraction and Selection Methods

  38. Feature Extraction and Selection Methods

  39. Feature Extraction and Selection Methods

  40. Feature Extraction and Selection Methods

  41. Feature Extraction and Selection Methods

  42. Feature Extraction and Selection Methods

  43. Feature Extraction and Selection Methods

  44. Biometric Recognition 財團法人資訊工業策進會(III) 網路多媒體研究所(NMI)

  45. Why Biometric?

  46. Why Biometric?

  47. Why Biometric?

  48. Password Limitations • 傳統身分認證方法 - key • license or card • password • and so on • 缺點 • Stolen • Lost • Forgotten(too many or hard to memorize) • Misplaced

  49. What is Biometric Recognition? • The biometrics recognition is the process of automatically differentiating people on the basis of individuality information from their physical or behavioral characteristics like fingerprint, iris, face, voice, and etc.

  50. What is Biometric Recognition?

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