Pertemuan 1. introduction to image processing

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Pertemuan 1. introduction to image processing

  1. 1. INTRODUCTION TOIMAGE PROCESSING Politeknik Kota Malang Aditya Kurniawan, S.ST © 2011
  2. 2. WHAT IS THE DIFFERENCE ?Image ProcessingComputer Vision Robot Vision 2 Aditya@poltekom.ac.id
  3. 3. IMAGE PROCESSINGA process to an image focusing on transforming, encoding and transmitting the image. IMAGE IMAGE 3 Aditya@poltekom.ac.id
  4. 4. COMPUTER VISIONComputer Vision => media to know the world visually supported byknowledge strength by computational instrument. Description / IMAGE humanized information 4 Aditya@poltekom.ac.id
  5. 5. ROBOT VISIONRobot Vision => a machine with ability to see his environment designed withworkflow algorithm, so it can make a decision and finish the job automatically. IMAGE Action 5 Aditya@poltekom.ac.id
  6. 6. BLOCK DIAGRAM ImageProsesing Computer + Vision Robot Artificial + VisionIntelligent IT guys Hardware 6 Aditya@poltekom.ac.id
  7. 7. BLOK DIAGRAM ImageProsesing Computer + Vision Robot Artificial + VisionIntelligent Hardware MECHATRONIC guys 7 Aditya@poltekom.ac.id
  8. 8. INTRODUCTION Modern digital technology has made it possible tomanipulate multi-dimensional signals with systems that rangefrom simple digital circuits to advanced parallel computers.The goal of this manipulation can be divided into threecategories:Image ProcessingImage in  image outImage AnalysisImage in  measurements outImage UnderstandingImage in  high-level description out 8 Aditya@poltekom.ac.id
  9. 9. INTRODUCTIONImage Processing 9 Aditya@poltekom.ac.id
  10. 10. INTRODUCTION Image Analysis10 Aditya@poltekom.ac.id
  11. 11. INTRODUCTION ImageUnderstanding 11 Aditya@poltekom.ac.id
  12. 12. INTRODUCTION We begin with certain basic definitions. An image defined in the “real world” isconsidered to be a function of two real variables, forexample, a(x,y) with a as the amplitude (e.g. brightness)of the image at the real coordinate position (x,y). 12 Aditya@poltekom.ac.id
  13. 13. INTRODUCTION An image may be considered to contain sub-images sometimes referred to as regions–of–interest, ROIs,or simply regions. This concept reflects the fact thatimages frequently contain collections of objects each ofwhich can be the basis for a region. 13 Aditya@poltekom.ac.id
  14. 14. INTRODUCTIONCoordinatePosition 14 Aditya@poltekom.ac.id
  15. 15. INTRODUCTION Y1 Y2Regions X1 OfInterest X2 15 Aditya@poltekom.ac.id
  16. 16. INTRODUCTION Y1 Y2 Y3 Y4 RegionsRegions Of Of InterestInterest X1 X2 16 Aditya@poltekom.ac.id
  17. 17. INTRODUCTION In a sophisticated image processing system it should be possible toapply specific image processing operations to selected regions. Thus one partof an image (region) might be processed to suppress motion blur whileanother part might be processed to improve color rendition. 17 Aditya@poltekom.ac.id
  18. 18. INTRODUCTION Brightnessenhancement Contrast enhancement 18 Aditya@poltekom.ac.id
  19. 19. DIGITAL IMAGE DEFINITIONS A digital image a[m,n] described in a 2D discrete space is derivedfrom an analog image a(x,y) in a 2D continuous space through a samplingprocess that is frequently referred to as digitization. For now we will look atsome basic definitions associated with the digital image. The effect ofdigitization is shown in Figure 1. 19 Aditya@poltekom.ac.id
  20. 20. DIGITAL IMAGE DEFINITIONS 0,2 nM 3,2 nMSample range of colour in reality world is an analog signal 20 Aditya@poltekom.ac.id
  21. 21. DIGITAL IMAGE DEFINITIONS0,2 nM 3,2 nM The idea of digitization Is taking sample of a range or an analog value 21 Aditya@poltekom.ac.id
  22. 22. DIGITAL IMAGE DEFINITIONS0,2 nM 3,2 nM 00 01 10 11 2 bit colour representation The idea of digitization Is taking sample of a range or an analog value 22 Aditya@poltekom.ac.id
  23. 23. DIGITAL IMAGE DEFINITIONS 23 Aditya@poltekom.ac.id
  24. 24. DIGITAL IMAGE DEFINITIONS The 2D continuous image a(x,y) is divided into Nrows and M columns. The intersection of a row and acolumn is termed a pixel (pixel comes from “picture element”).The value assigned to the integer coordinates [m,n] with{m=0,1,2,…,M–1} and {n=0,1,2,…,N–1} is a[m,n]. Infact, in most cases a(x,y) which we might consider to bethe physical signal that impinges on the face of a 2Dsensor is actually a function of many variables includingdepth (z), color (l), and time (t). Unless otherwise stated,we will consider the case of 2D, monochromatic, staticimages in this chapter. 24 Aditya@poltekom.ac.id
  25. 25. DIGITAL IMAGE DEFINITIONSA pixel contain these information : a (x, y, z, l, t) a = illumination / light exposure in a certain pixel X = horizontal coordinate Y = vertical coordinate Z = depth L = colour T = time frame 25 Aditya@poltekom.ac.id
  26. 26. DIGITAL IMAGE DEFINITIONS A pixel contain these information : a (x, y, z, l, t) a = illumination / light exposure in a certain pixel High Light Low exposure Lightexposure 26 Aditya@poltekom.ac.id
  27. 27. DIGITAL IMAGE DEFINITIONSA pixel contain these information : a (x, y, z, l, t) X, Y = 2 dimensional coordinate Y1 Y2 X1 X2 27 Aditya@poltekom.ac.id
  28. 28. DIGITAL IMAGE DEFINITIONS A pixel contain these information : a (x, y, z, l, t) Z = depth bottomsurface 28 Aditya@poltekom.ac.id
  29. 29. DIGITAL IMAGE DEFINITIONS A pixel contain these information : a (x, y, z, l, t) l = colour Yellow colour Redcolour 29 Aditya@poltekom.ac.id
  30. 30. DIGITAL IMAGE DEFINITIONSA pixel contain these information : a (x, y, z, l, t) Picture taken in a different time frame t = time frame t1 t2 t3 t4 30 Introduction to Image Processing Aditya@poltekom.ac.id
  31. 31. DIGITAL IMAGE DEFINITIONS The image shown in Figure 1 has been divided into N = 16 rows andM = 16 columns. The value assigned to every pixel is the average brightness inthe pixel rounded to the nearest integer value. The process of representingthe amplitude of the 2D signal at a given coordinate as an integer valuewith L different gray levels is usually referred to as amplitudequantization or simply quantization. 31 Aditya@poltekom.ac.id
  32. 32. COMMON VALUES There are standard values for the variousparameters encountered in digital image processing.These values can be caused by video standards, byalgorithmic requirements, or by the desire to keep digitalcircuitry simple. Table 1 gives some commonlyencountered values. 32 Aditya@poltekom.ac.id
  33. 33. COMMON VALUESQuite frequently we see cases of M=N=2K where {K =8,9,10}. This can be motivated by digital circuitry or bythe use of certain algorithms such as the (fast) Fouriertransform.The number of distinct gray levels is usually a power of2, that is, L=2B where B is the number of bits in thebinary representation of the brightness levels. WhenB>1 we speak of a gray-level image; when B=1 we speakof a binary image. In a binary image there are just twogray levels which can be referred to, for example, as“black” and “white” or “0” and “1”. 33 Aditya@poltekom.ac.id
  34. 34. CHARACTERISTIC OF IMAGE OPERATIONS There is a variety of ways to classify and characterize imageoperations. The reason for doing so is to understand what type of results wemight expect to achieve with a given type of operation or what might be thecomputational burden associated with a given operation. 34 Aditya@poltekom.ac.id
  35. 35. ADVANTAGES OF IMAGE PROCESSING Medical Sharpen X-Ray result, Analysis of MRI, etc Technology and CommunicationsReduce noise from satellite image, video streaming Game Shadow effect on water surface, light effect, etc Photography and FilmsContrast, brightness, illegal photo manipulations, etc 35 Aditya@poltekom.ac.id
  36. 36. MEDICAL APPLICATION 36 Aditya@poltekom.ac.id
  37. 37. TECHNOLOGY ANDCOMMUNICATIONS 37 Aditya@poltekom.ac.id
  38. 38. DIGITAL IMAGEACQUISITION PROCESS 38 Aditya@poltekom.ac.id

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