01 introduction image processing analysis

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01 introduction image processing analysis

  1. 1. INTRODUCTION TO IMAGEPROCESSING AND ANALYSISTati R. Mengko
  2. 2. Sinyal dan Informasi central sulcus Transmisi sinyal ke otak motor control touch & pressure taste speech smell vision hearing ACTIONMorphonix LLC, Sausalito, CA
  3. 3. Sistem Visual Zrener E, Science, 295, 1022-1025, (2002), AAAS
  4. 4. A picture says more than a thousand wordsNational Geograhic Traveler Magazine, May/June 2004, Photograph by Justin Guariglia
  5. 5. Citra Cahaya Tampak (Visible Light)Warna Kandinsky: Almost Abstract Audrey Flack: A Super Realist Still LifeTeksturPersepsi Visual
  6. 6. Citra Cahaya tak Tampak (Non-visible Light)Citra inframerahCitra radarCitra sinar-X
  7. 7. Modalitas PencitraanCitra terbentuk ketika sensor menangkap radiasi yang sudah berinteraksi dengan obyek fisik.Informasi tentang obyek dalam pandangan direkam sebagai perubahan dalam intensitas dan warna dari radiasi yang dideteksi.
  8. 8. pencitraan tekstur kulit pencitraan reservoar panas bumi pencitraan objek astronomi pemetaan deposit minyak bumi tomografi struktur 3D pemeriksaan struktur anatomi interior bumi pencitraan iklim otomasi industri robot visionpencitraan untuk diagnosis Image Processing Application reverse engineering analisis pencemaran lingkungan nondestructive testing deteksi keretakan bangunan thermal imaging remote sensing biometrik pencitraan struktur material
  9. 9. Iris ScanDifferentiation of people uses unique patterns in the iris tissue known as thetrabecular meshworkAttacks and CountermeasuresUsability
  10. 10. the da Vinci System, picture from IntuitiveSurgical.com
  11. 11. IMAGE PROCESSING & ANALYSIS SCHEMEProblem Domain: Intermediate Level Image Processing Feature Extraction Pre - processing Recognition & Knowledge base Interpretation Result Image Acquisition Low Level Image Processing High Level Image Processing
  12. 12. INTRODUCTION Image ProcessingComputer Computer Vision Graphics
  13. 13. IMAGE Image IMAGE Processing Computer Computer Vision Graphics PERCEPTION Features Decision Mathematical model …. etc
  14. 14. Digital Image Processing Components CommunicationImage Acquisition Display Processing Storage
  15. 15. Part 2:Image Acquisition
  16. 16. IMAGE ACQUISITION
  17. 17. IMAGE ACQUISITION
  18. 18. Analog vs Digital ImageAnalog Image Digital Image
  19. 19. Sampling & Quantization Column of samples Pixel 255 BlackLine Line Spacing Gray 128 White 0 Sample Spacing Picture Sampling process Brightness Spacing Spatial resolution Quantization Process Brightness Resolution
  20. 20. Matrix Representation•In a (8-bit) greyscale image each picture element has an assigned intensitythat ranges from 0 to 255.•Each pixel has a value from 0 (black) to 255 (white).
  21. 21. Part 3
  22. 22. IMAGE PRE-PROCESSING Image Transform
  23. 23. Why Do Transforms?• Fast computation • e.g., convolution vs. multiplication• Conceptual insights for various image processing • e.g., spatial frequency info. (smooth, moderate change, fast change, etc)• Obtain transformed data as measurement • e.g., medical images • Need inverse transform
  24. 24. Image TransformsImage transforms a class of unitary matrices used for representing images. • Simple arithmetic operations on images or complex mathematical operations which convert images from one representation to another.Transform theory has played a key role in image processing for a number of years.2-D transforms are used for image enhancement, restoration, encoding, and description.
  25. 25. Image TransformsFoundations for image transforms • Matrices • Unitary transform (and orthogonal transfrom) • (K-L transform)Examples of unitary transforms • Discrete Fourier Transform (DFT) • Discrete Cosine Transform (DCT) • Haar transform • K-L transform
  26. 26. DFT Example50 50100 100150 150200 200 50250 250 50 100 150 200 250 100 50 100 150 200 250 Original image 150 Phase image 200 250 50 100 150 200 250 Log magnitude of DFT coefficient
  27. 27. DFT ExampleOriginal image Phase Magnitude Magnitude centered
  28. 28. Example of DCT50100150 50 100200 150250 50 100 150 200 250 50 Original image 200 100 250 50 100 150 200 250 150 DCT coefficient 200 250 50 100 150 200 250 log magnitude of DCT coefficient
  29. 29. Part 4
  30. 30. IMAGE PRE-PROCESSING Image Enhancement
  31. 31. Techniques
  32. 32. Image Enhancement CharacteristicsDefinition: accentuation, sharpening of image features (edge, boundaries, or contrast) to make a graphic display more useful for display and analysis.Characteristics: • does not increase the inherent information content in the data. • increases the dynamic range of the chosen features so that they can detected easily. • greatest difficulty: quantifying the criterion for enhancement.
  33. 33. Adjusting the Image Histogram to Improve Image Contrast Poor Contrast Adjusted Image Histogram
  34. 34. Median Filter Noisy Image Median Filtered Image(Salt & Pepper Noise)
  35. 35. Transform OperationSteps: 1. Convert image into a transform domain representation 2. Process the image in transform domain 3. Inverse-transform the processed image to obtain enhanced version
  36. 36. Transform Operation Example 1 Original Image Frequency Domain Image 2 3Band-reject Filter Filtered Image
  37. 37. Part 5
  38. 38. IMAGE RECONSTRUCTION
  39. 39. Image ReconstructionRadon Transform +∞+∞ g (s,θ ) ≡ R ( f ) = ∫ ∫ f (x, y )δ (x− ∞θ<+sy< ∞, 0 ≤)θ < π −∞−∞ cos sin θ − s dx dy s g(s,θ) y u θ x f(x,y)
  40. 40. Radon Transform citra phantom asal Has il trans form as i Radon invers e 50 50100 100 150150200 Radon Transform 200 of Head Phantom 250250 50 100 150 200 250 Using 90 50 100 150 200 250 Projections Original image has il trans form as i Radon dari c itra phantom Inverse Radon -150 60 Transforms of the -100 50 Shepp-Logan -50 Head Phantom 40 0 x′ 30 50 20 100 10 150 0 0 50 100 150 θ
  41. 41. Part 5
  42. 42. FEATURE EXTRACTION Image Segmentation
  43. 43. Segmentation MethodsBoundary-based • Edge detectionRegion-based • Thresholding 1 2
  44. 44. Edge Detection ExampleExample: Roberts Operator Original Image
  45. 45. Region-based ApproachGroups pixels based on similarity • Intensity similarity • Intensity variance similarity • etc.
  46. 46. FEATURE EXTRACTION Shape Analysis
  47. 47. y RECTANGULARITY βmax αmax θ Rectangularity (Bounding x Rectangle)αmin smallest rectangle that fits an βmin object according to its orientationα = x cosθ + y sin θ β = − x sin θ + y cosθLR = α max − α min AR = LR .WRWR = β max − β min
  48. 48. CIRCULARITY R Circularity • The smallest circle that encloses an object • Center of the circle = center of mass of the object • Radius = maximum distance between center and boundary P2C= A
  49. 49. SHAPE DESCRIPTORSPHERICITY• Ratio between smallest and biggest circle radius which are centered in the center of mass of the object.• 0 ≤ spher ≤ 1• Circle: spher = 1 Rc rinscribing ( Ri )spher = rcircumscribed ( Rc ) + Center of Mass Ri
  50. 50. FEATURE EXTRACTIONTexture Analysis
  51. 51. TextureTexture: A global pattern arising from the repetition, either deterministically or randomly, of local sub-patterns. Macrostructure Microstructure
  52. 52. 1st Order FeaturesBased on image histogram characteristics Statistical Features: • Mean • Variance • Skewness • Curtosis • Entropy
  53. 53. Texture Isolation with Gabor Filter Θ = 0o Θ = 30o 30Frequency=√2/ Θ = 60o 60 Θ = 90o 90 23
  54. 54. Part 6
  55. 55. CLASSIFICATION
  56. 56. Problem Sumo WrestlerWeight Runner Horse-rider Height
  57. 57. ApproachesStatistical Classification • Bayesian ClassifierSyntactical Classification • Rule-based ClassificationUnsupervised Learning Approach • Neural Network
  58. 58. ExamplesStatistical Classification ω1 = class-1 ω2 = class-2 x = classification object
  59. 59. ExamplesNeural Network Diagram Features Input Classification Output
  60. 60. ReferencesS. Webb Ed., The Physics of Medical Imaging, Medical Science SeriesZ-H Cho, JP Jones, & M. Singh, Foundations of Medical Imaging, WileyZ-P. Liang and Lauterbur, Principles of Magnetic Resonance Imaging: ASignal Processing Perspective, IEEE Press, 2000.AK Jain, Fundamentals of Digital Image Processing, PHIRC. Gonzalez & RE Woods, Digital Image Processing, Pearson EducationAny sources from the internet.

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