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![Weeks 1 & 2 16
Representing Digital Images
► Discrete intensity interval [0, L-1], L=2k
► The number b of bits required to store a M × N
digitized image
b = M × N × k](https://image.slidesharecdn.com/seemadip-211031134349/75/Seema-dip-16-2048.jpg)
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Seema dip
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- 2. Contents This lecture willcover: • What is digital image processing? • History of digital image processing • State of the art • examples of digital image processing • Key stages in digital image processing Weeks 1 & 2 2
- 4. S. No. Contents 1. Imagefundamentals: A simple image formation model, sampling and quantization, connectivity and adjacency relationships between pixels 2. Spatial domain filtering: Basic intensity transformations: negative, log, power-law and piecewise linear transformations, bit-plane slicing, histogram equalization and matching, smoothing and sharpening filtering in spatial domain, unsharp masking and high-boost filtering 3. Frequency domain filtering: Fourier Series and Fourier transform, discrete and fast Fourier transform, sampling theorem, aliasing, filtering in frequency domain, lowpass and highpass filters, bandreject and bandpass filters, notch filters 4. Image restoration: Introduction to various noise models, restoration in presence of noise only, periodic noise reduction, linear and position invariant degradation, estimation of degradation function 5. Image reconstruction: Principles of computed tomography, projections and Radon transform, the Fourier slice theorem, reconstruction using parallel-beam and fan-beam by filtered backprojection methods 6. Mathematical morphology: Erosion and dilation, opening and closing, the Hit-or-Miss transformation, various morphological algorithms for binary images 7. Wavelets and multiresolution processing: Image pyramids, subband coding, multiresolution expansions, the Haar transform, wavelet transform in one and two dimensions, discrete wavelet transform
- 5. Gonzalez, R. C.and Woods, R. E., "Digital Image Processing", Prentice Hall, 3rd Ed. Jain, A. K., "Fundamentals of Digital Image Processing", PHI Learning, 1st Ed. Bernd, J., "Digital Image Processing", Springer, 6th Ed. Burger, W. and Burge, M. J., "Principles of Digital Image Processing", Springer Scherzer, O., " Handbook of Mathematical Methods in Imaging", Springer
- 7. Weeks 1 &2 7 Image Acquisition Process
- 8. Weeks 1 &2 8 Introduction ► What is Digital Image Processing? Digital Image — a two-dimensional function x and y are spatial coordinates The amplitude of f is called intensity or gray level at the point (x, y) Digital Image Processing — process digital images by means of computer, it covers low-, mid-, and high-level processes low-level: inputs and outputs are images mid-level: outputs are attributes extracted from input images high-level: an ensemble of recognition of individual objects Pixel — the elements of a digital image ( , ) f x y
- 9. 9 A Simple ImageFormation Model ( , ) ( , ) ( , ) ( , ): intensity at the point ( , ) ( , ): illumination at the point ( , ) (the amount of source illumination incident on the scene) ( , ): reflectance/transmissivity f x y i x y r x y f x y x y i x y x y r x y at the point ( , ) (the amount of illumination reflected/transmitted by the object) where 0 < ( , ) < and 0 < ( , ) < 1 x y i x y r x y
- 10. Weeks 1 &2 10 Some Typical Ranges of Reflectance ► Reflectance 0.01 for black velvet 0.65 for stainless steel 0.80 for flat-white wall paint 0.90 for silver-plated metal 0.93 for snow
- 11. Weeks 1 &2 11 Image Sampling and Quantization Digitizing the coordinate values Digitizing the amplitude values
- 12. Weeks 1 &2 12 Image Sampling and Quantization
- 13. Weeks 1 &2 13 Representing Digital Images ►The representation of an M×N numerical array as (0,0) (0,1) ... (0, 1) (1,0) (1,1) ... (1, 1) ( , ) ... ... ... ... ( 1,0) ( 1,1) ... ( 1, 1) f f f N f f f N f x y f M f M f M N
- 14. Weeks 1 &2 14 Representing Digital Images ►The representation of an M×N numerical array as 0,0 0,1 0, 1 1,0 1,1 1, 1 1,0 1,1 1, 1 ... ... ... ... ... ... ... N N M M M N a a a a a a A a a a
- 15. Weeks 1 &2 15 Representing Digital Images ►The representation of an M×N numerical array in MATLAB (1,1) (1,2) ... (1, ) (2,1) (2,2) ... (2, ) ( , ) ... ... ... ... ( ,1) ( ,2) ... ( , ) f f f N f f f N f x y f M f M f M N
- 16. Weeks 1 &2 16 Representing Digital Images ► Discrete intensity interval [0, L-1], L=2k ► The number b of bits required to store a M × N digitized image b = M × N × k
- 17. Weeks 1 &2 17 Representing Digital Images
- 21. What is aDigital Image? (cont…) ►Common image formats include: 1 sample per point (B&W or Grayscale) 3 samples per point (Red, Green, and Blue) 4 samples per point (Red, Green, Blue, and “Alpha”, a.k.a. Opacity) ►For most of this course we will focus on grey-scale
- 22. Image processing ► Animage processing operation typically defines a new image g in terms of an existing image f. ► We can transform either the range of f. ► Or the domain of f: ► What kinds of operations can each perform?
- 23. What is DIP?(cont…) ►The continuum from image processing to computer vision can be broken up into low-, mid- and high-level processes Low Level Process Input: Image Output: Image Examples: Noise removal, image sharpening Mid Level Process Input: Image Output: Attributes Examples: Object recognition, segmentation High Level Process Input: Attributes Output: Understanding Examples: Scene understanding, autonomous navigation In this course we will stop here
- 24. Key Stages inDigital Image Processing Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression
- 25. Key Stages inDigital Image Processing: Image Aquisition Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002)
- 26. Key Stages inDigital Image Processing: Image Enhancement Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002)
- 27. Key Stages inDigital Image Processing: Image Restoration Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002)
- 28. Key Stages inDigital Image Processing: Morphological Processing Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002)
- 29. Key Stages inDigital Image Processing: Segmentation Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002)
- 30. Key Stages inDigital Image Processing: Object Recognition Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002)
- 31. Key Stages inDigital Image Processing: Representation & Description Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002)
- 32. Key Stages inDigital Image Processing: Image Compression Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression
- 33. Key Stages inDigital Image Processing: Colour Image Processing Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression
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- 39. Fingerprint Identification Researchat UNR Minutiae Matching Delaunay Triangulation
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- 41. Object Recognition Research referenceview 1 reference view 2 novel view recognized
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- 44. Target Recognition ►Department ofDefense (Army, Airforce, Navy)
- 45. Interpretation of aerialphotography is a problem domain in both computer vision and registration. Interpretation of Aerial Photography
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- 54. Hand Gesture Recognition ►Smart Human-Computer User Interfaces ► Sign Language Recognition
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- 56. Medical Applications ► skincancer breast cancer
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- 58. Inserting Artificial Objectsinto a Scene
- 59. Companies In thisField In India ► Sarnoff Corporation ► Kritikal Solutions ► National Instruments ► GE Laboratories ► Ittiam, Bangalore ► Interra Systems, Noida ► Yahoo India (Multimedia Searching) ► nVidia Graphics, Pune (have high requirements) ► Microsoft research ► DRDO labs ► ISRO labs ► …