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CSE367 Lecture 1

Image Processing lectures

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CSE367 Lecture 1

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  1. CSE367 DIGITAL IMAGE PROCESSING LECTURE 1 INTRODUCTION & FUNDAMENTALS (PART 1) Dr. Fatma Newagy Associate Prof. of Communications Engineering Fatma_newagy@eng.asu.edu.eg

  2. Rules and Ethics • No more 10 minutes late • Use chat window for questions • Mute students when not participating • Good behavior and participation is a must • Turn on your camera if needed • Send your feedback by email after each week with your suggestions

  3. Assessment Weights and Schedule Assignments Weeks 3, 9 Weeks 4, 11 Quizzes Week 8 Mid-Term Examination Week 14 Project Any copied document (assignments, reports, projects,…) from internet/ student Will get negative mark End of semester Final Exam Attendance and Participation 5% 10% Assignments / project 10% Quizzes 25% Mid-Term Exam 10% Lab Reports 40% Final Exam 100% Total

  4. Article Reading and Project • Article Reading and Project • Medical image analysis • Face, fingerprint, and other object recognition • Image and/or video compression • Image segmentation and/or denoising • Digital image/video watermarking/steganography and detection • Whatever you’re interested…

  5. Evaluation of article reading and project • Evaluation of article reading and project • Report Article reading — Submit a survey of the articles you read and the list of the articles Project — Submit an article including introduction, methods, experiments, results, and conclusions — Submit the project code, the readme document, and some testing samples (images, videos, etc.) for validation • Presentation

  6. Journals & Conferences in Image Processing • Journals: — IEEE T IMAGE PROCESSING — IEEE T MEDICAL IMAGING — INTL J COMP. VISION — IEEE T PATTERN ANALYSIS MACHINE INTELLIGENCE — PATTERN RECOGNITION — COMP. VISION AND IMAGE UNDERSTANDING — IMAGE AND VISION COMPUTING … … • Conferences: — CVPR: Comp. Vision and Pattern Recognition — ICCV: Intl Conf on Computer Vision — ACM Multimedia — ICIP — SPIE — ECCV: European Conf on Computer Vision — CAIP: Intl Conf on Comp. Analysis of Images and Patterns … … • IEEE Communications Surveys & Tutorials • IEEE Multimedia • IEEE Signal Processing Magazine

  7. Text Books • Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, Prentice Hall, 2008, Third Edition. • Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing Using Matlab, Prentice Hall, 2009, Second Edition.

  8. Extra materials • https://web.stanford.edu/class/ee368/ • http://www.imageprocessingplace.com/ • ……

  9. This lecture will cover • Introduction • State of the art examples of digital image processing • Key stages in digital image processing • Digital Image Processing Fundamentals (Part-1)

  10. Introduction • Interest comes from two primary backgrounds Improvement of pictorial information for human perception • How can an image/video be made more aesthetically pleasing • How can an image/video be enhanced to facilitate extraction of useful information Processing of data for autonomous machine perception

  11. 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

  12. 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 Mid Level Process Input: Image Output: Attributes High Level Process Input: Attributes Output: Understanding Examples: Noise removal, image sharpening Examples: Object recognition, segmentation Examples: Scene understanding, autonomous navigation In this course we will stop here

  13. Image Sharpening (a) Original Image (b) After sharpening

  14. Removing Noise (a) Original Image (b) After removing noise

  15. Image Deblurring (a) Original Image (b) After removing the blur

  16. Image Segmentation

  17. History of DIP •1980s - Today: The use of digital image processing techniques has exploded and they are now used for all kinds of tasks in all kinds of areas • Image enhancement/restoration • Artistic effects • Medical visualisation • Industrial inspection • Law enforcement • Human computer interfaces

  18. Examples: Image Enhancement •One of the most common uses of DIP techniques: improve quality, remove noise etc

  19. Examples: The Hubble Telescope •Launched in 1990 the Hubble telescope can take images of very distant objects •However, an incorrect mirror made many images useless •Image processing were used to fix this of Hubble’s techniques

  20. Examples: Artistic Effects •Artistic effects are used to make images more visually appealing, to add special effects and to make composite images

  21. Examples: Medicine •Take slice from MRI scan of canine heart, and find boundaries between types of tissue • Image with gray levels representing tissue density • Use a suitable filter to highlight edges Original MRI Image of a Dog Heart Edge Detection Image

  22. Examples: GIS •Geographic Information Systems • Digital image processing techniques are used extensively to manipulate satellite imagery • Terrain classification • Meteorology

  23. Examples: GIS (cont…) •Night-Time Lights of the World data set • Global inventory of human settlement • Not hard to imagine the kind of analysis that might be done using this data

  24. Examples: Industrial Inspection •Human operators are expensive, slow and unreliable •Make machines do the job instead •Industrial vision systems are used in all kinds of industries

  25. Examples: Law Enforcement •Image processing techniques are used extensively by law enforcers • Number plate recognition for speed cameras/automated toll systems • Fingerprint recognition • Enhancement of CCTV images

  26. Key Stages in Digital Image Processing Image Restoration Morphological Processing Image Segmentation Enhancement Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

  27. Key Stages in Digital Image Processing: Image Aquisition Image Restoration Morphological Processing Image Segmentation Enhancement Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

  28. Key Stages in Digital Image Processing: Image Enhancement Image Restoration Morphological Processing Image Segmentation Enhancement Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

  29. Key Stages in Digital Image Processing: Image Restoration Image Restoration Morphological Processing Image Segmentation Enhancement Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

  30. Key Stages in Digital Image Processing: Morphological Processing Image Restoration Morphological Processing Image Segmentation Enhancement Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

  31. Key Stages in Digital Image Processing: Segmentation Image Restoration Morphological Processing Image Segmentation Enhancement Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

  32. Key Stages in Digital Image Processing: Object Recognition Image Restoration Morphological Processing Image Segmentation Enhancement Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

  33. Key Stages in Digital Image Processing: Representation & Description Image Restoration Morphological Processing Image Segmentation Enhancement Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

  34. Key Stages in Digital Image Processing: Image Compression Image Restoration Morphological Processing Image Segmentation Enhancement Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

  35. Key Stages in Digital Image Processing: Colour Image Processing Image Restoration Morphological Processing Image Segmentation Enhancement Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

  36. Computer Vision: Some Applications • Optical character recognition (OCR) • Face Detection • Smile Detection • Login without password using fingerprint scanners and face recognition systems • Object recognition in mobiles • Smart Cars • Vision in space

  37. Fundamentals in DIP

  38. Electromagnetic (EM) spectrum

  39. Electromagnetic (EM) energy spectrum Major uses Gamma-ray imaging: nuclear medicine and astronomical observations X-rays: medical diagnostics, industry, and astronomy, etc. Ultraviolet: lithography, industrial inspection, microscopy, lasers, biological imaging, and astronomical observations Visible and infrared bands: light microscopy, astronomy, remote sensing, industry, and law enforcement Microwave band: radar Radio band: medicine (such as MRI) and astronomy

  40. Light and EM Spectrum    , : Planck's constant. h  c E h

  41. Light and EM Spectrum ►The colors that humans perceive in an object are determined by the nature of the light reflected from the object. e.g. green objects reflect light with wavelengths primarily in the 500 to 570 nm range while absorbing most of the energy at other wavelength

  42. Light and EM Spectrum ► Monochromatic light: void of color Intensity is the only attribute, from black to white Monochromatic images are referred to as gray- scale images ► Chromatic light bands: 0.43 to 0.79 um The quality of a chromatic light source: Radiance: total amount of energy Luminance (lm): the amount of energy an observer perceives from a light source Brightness: a subjective descriptor of light perception that is impossible to measure. It embodies the achromatic notion of intensity and one of the key factors in describing color sensation.

  43. Image Acquisition Transform illumination energy into digital images

  44. Image Acquisition Using a Single Sensor

  45. Image Acquisition Using Sensor Strips

  46. Image Acquisition Process

  47. A Simple Image Formation Model  ( , ) f x y ( , ) ( , ) i x y r x y ( , ): intensity at the point ( , ) ( , ): illumination at the point ( , ) (the amount of source illumination incident on the scene) ( , ): reflectance/transmissivity r x y (the amount of illumination reflected/transmitted by the object) where 0 < ( , ) < and 0 < ( , ) < 1 i x y r x y  f x y i x y x y x y at the point ( , ) x y

  48. Some Typical Ranges of illumination • Illumination Lumen —A unit of light flow or luminous flux Lumen per square meter (lm/m2) —The metric unit of measure for illuminance of a surface • On a clear day, the sun may produce in excess of 90,000 lm/m2 of illumination on the surface of the Earth • On a cloudy day, the sun may produce less than 10,000 lm/m2 of illumination on the surface of the Earth • On a clear evening, the moon yields about 0.1 lm/m2 of illumination • The typical illumination level in a commercial office is about 1000 lm/m2

  49. Some Typical Ranges of Reflectance • Typical values of reflectance r(x,y) • 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

  50. Image Sampling and Quantization Digitizing the coordinate values Digitizing the amplitude values

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