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01 introduction image processing analysis
- 1. INTRODUCTION TO IMAGE PROCESSINGAND ANALYSIS Tati R. Mengko
- 2. Sinyal dan Informasi central sulcus Transmisi sinyal ke otak motor control touch & pressure taste speech smell vision hearing ACTION Morphonix LLC, Sausalito, CA
- 3. Sistem Visual Zrener E, Science, 295, 1022-1025, (2002), AAAS
- 4. A picture saysmore than a thousand words National Geograhic Traveler Magazine, May/June 2004, Photograph by Justin Guariglia
- 5. Citra Cahaya Tampak(Visible Light) Warna Kandinsky: Almost Abstract Audrey Flack: A Super Realist Still Life Tekstur Persepsi Visual
- 6. Citra Cahaya takTampak (Non-visible Light) Citra inframerah Citra radar Citra sinar-X
- 7. Modalitas Pencitraan Citra terbentukketika 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. 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 vision pencitraan untuk diagnosis Image Processing Application reverse engineering analisis pencemaran lingkungan nondestructive testing deteksi keretakan bangunan thermal imaging remote sensing biometrik pencitraan struktur material
- 9. Iris Scan Differentiation ofpeople uses unique patterns in the iris tissue known as the trabecular meshwork Attacks and Countermeasures Usability
- 10. the da VinciSystem, picture from IntuitiveSurgical.com
- 11. IMAGE PROCESSING &ANALYSIS SCHEME Problem 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. INTRODUCTION Image Processing Computer Computer Vision Graphics
- 13. IMAGE Image IMAGE Processing Computer Computer Vision Graphics PERCEPTION Features Decision Mathematical model …. etc
- 14. Digital Image ProcessingComponents Communication Image Acquisition Display Processing Storage
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- 18. Analog vs DigitalImage Analog Image Digital Image
- 19. Sampling & Quantization Column of samples Pixel 255 Black Line Line Spacing Gray 128 White 0 Sample Spacing Picture Sampling process Brightness Spacing Spatial resolution Quantization Process Brightness Resolution
- 20. Matrix Representation •In a(8-bit) greyscale image each picture element has an assigned intensity that ranges from 0 to 255. •Each pixel has a value from 0 (black) to 255 (white).
- 21.
- 22. IMAGE PRE-PROCESSING Image Transform
- 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. Image Transforms Image 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. Image Transforms Foundations forimage 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. DFT Example 50 50 100 100 150 150 200 200 50 250 250 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. DFT Example Original image Phase Magnitude Magnitude centered
- 28. Example of DCT 50 100 150 50 100 200 150 250 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
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- 30. IMAGE PRE-PROCESSING Image Enhancement
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- 32. Image Enhancement Characteristics Definition: 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. Adjusting the ImageHistogram to Improve Image Contrast Poor Contrast Adjusted Image Histogram
- 34. Median Filter Noisy Image Median Filtered Image (Salt & Pepper Noise)
- 35. Transform Operation Steps: 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. Transform Operation Example 1 Original Image Frequency Domain Image 2 3 Band-reject Filter Filtered Image
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- 39. Image Reconstruction Radon 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. Radon Transform citra phantom asal Has il trans form as i Radon invers e 50 50 100 100 150 150 200 Radon Transform 200 of Head Phantom 250 250 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.
- 42. FEATURE EXTRACTION Image Segmentation
- 43. Segmentation Methods Boundary-based • Edge detection Region-based • Thresholding 1 2
- 44. Edge Detection Example Example:Roberts Operator Original Image
- 45. Region-based Approach Groups pixelsbased on similarity • Intensity similarity • Intensity variance similarity • etc.
- 46. FEATURE EXTRACTION Shape Analysis
- 47. y RECTANGULARITY βmax αmax θ Rectangularity (Bounding x Rectangle) αmin smallest rectangle that fits an βmin object according to its orientation α = x cosθ + y sin θ β = − x sin θ + y cosθ LR = α max − α min AR = LR .WR WR = β max − β min
- 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 P2 C= A
- 49. SHAPE DESCRIPTOR SPHERICITY • Ratiobetween 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
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- 51. Texture Texture: A globalpattern arising from the repetition, either deterministically or randomly, of local sub-patterns. Macrostructure Microstructure
- 52. 1st Order Features Basedon image histogram characteristics Statistical Features: • Mean • Variance • Skewness • Curtosis • Entropy
- 53. Texture Isolation withGabor Filter Θ = 0o Θ = 30o 30 Frequency=√2/ Θ = 60o 60 Θ = 90o 90 23
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- 56. Problem Sumo Wrestler Weight Runner Horse-rider Height
- 57. Approaches Statistical Classification • Bayesian Classifier Syntactical Classification • Rule-based Classification Unsupervised Learning Approach • Neural Network
- 58. Examples Statistical Classification ω1 = class-1 ω2 = class-2 x = classification object
- 59. Examples Neural Network Diagram Features Input Classification Output
- 60. References S. Webb Ed.,The Physics of Medical Imaging, Medical Science Series Z-H Cho, JP Jones, & M. Singh, Foundations of Medical Imaging, Wiley Z-P. Liang and Lauterbur, Principles of Magnetic Resonance Imaging: A Signal Processing Perspective, IEEE Press, 2000. AK Jain, Fundamentals of Digital Image Processing, PHI RC. Gonzalez & RE Woods, Digital Image Processing, Pearson Education Any sources from the internet.