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1、Digitizing the coordinate values is called sampling.Image sampling and quantizationDigitizing the amplitude values is called quantization.Lecture 2:Lecture 2:Chapter 2:Digital Image Fundamentals Chapter 2:Digital Image FundamentalsConcept of false contouring:ridge-like structures caused by the use o
2、f an insufficient number of gray levels in smooth areas of a digital image.Rule of thumb:images of size 256256 pixels and 64 gray levels are about the smallest images that can be expected to be reasonably free of objectionable sampling checker-boards and false contouring.Lecture 2:Lecture 2:Chapter
3、2:Digital Image Fundamentals Chapter 2:Digital Image FundamentalsProblems associated with resolution reductionIllustration of sampling frequency less than signal frequencyIf the function is undersampled,then a phenomenon called aliasing corrupts the sampled image.Lecture 3:Lecture 3:Chapter 2:Digita
4、l Image Fundamentals Chapter 2:Digital Image FundamentalsA set of pixels all of which are 4-connected to each other is called a 4-component;if all the pixels are 8-connected the set is an 8-component.4-component4-componentOnly one 8-component but two 4-componentLecture 3:Lecture 3:Chapter 2:Digital
5、Image Fundamentals Chapter 2:Digital Image Fundamentals8-componentIllustration of different connected components0 0 0 0 0 00 1 1 0 0 00 1 1 0 0 00 0 0 1 1 00 0 0 1 1 0How many connected components for the following image?Two objects for 4-connected neighborhoodone object for 8-connected neighborhood
6、Lecture 3:Lecture 3:Chapter 2:Digital Image Fundamentals Chapter 2:Digital Image FundamentalsConnected components exercise1.The Euclidean distance between p and q is defined as:Different ways of measuring distanceUsing this method,the pixels having a distance less than or equal to some value r from(
7、x,y)are the points contained in a disk of radius r centered at(x,y).pqLecture 3:Lecture 3:Chapter 2:Digital Image Fundamentals Chapter 2:Digital Image Fundamentals2.The D4 distance(also called city block distance)between p and q is defined as:Using this method,the pixels having a D4 distance from(x,
8、y)less than or equal to some value r form a diamond centered at(x,y).For example,the pixels with D4 distance 2 from(x,y)form the following contours of constant distance:The pixels with D4=1 are the 4-neighbors of(x,y).2211221022212D4 distanceDifferent ways of measuring distance contLecture 3:Lecture
9、 3:Chapter 2:Digital Image Fundamentals Chapter 2:Digital Image Fundamentals3.The D8 distance(also called chessboard distance)between p and q is defined as:D8(p,q)=max(x-s,y-t)2222221112210122111222222D8 distanceDifferent ways of measuring distance contThe pixels with D8=1 are the 8-neighbors of(x,y
10、).Lecture 3:Lecture 3:Chapter 2:Digital Image Fundamentals Chapter 2:Digital Image Fundamentalsp2pp1p4p3Assume that p,p2,and p4 have value 1 and that p1 and p3 can have a value 0 or 1.For V=1,solve for Dm distance between p and p4.Solution:If p1 and p3 are 0,then Dm is 2.If p1 is 1,p3 are 0,then Dm
11、becomes 3.Similarly,if p3 is 1 and p1 is 0,Dm also is 3.Finally,if both p1 and p3 are 1,Dm is 4.Example to illustrate finding Dm distanceLecture 3:Lecture 3:Chapter 2:Digital Image Fundamentals Chapter 2:Digital Image FundamentalsCommon functions for enhancementLecture 4:Lecture 4:Chapter 3 Spatial
12、Domain Enhancement Chapter 3 Spatial Domain Enhancements=cr Where c and are positive constants.Power-law transformationLecture 4:Lecture 4:Chapter 3 Spatial Domain Enhancement Chapter 3 Spatial Domain EnhancementUse of values greater than 1 to reduce the light gray level.Lecture 4:Lecture 4:Chapter
13、3 Spatial Domain Enhancement Chapter 3 Spatial Domain EnhancementEnhancement of washed-out imagesIllustration of histogram equalization4x4 image Gray scale=0,9histogram0112233445566789No.of pixelsGray level2332424332352424Lecture 5:Lecture 5:Chapter 3 Spatial Domain Enhancement Chapter 3 Spatial Dom
14、ain Enhancement91609s x 9No.of pixelsGray Level(j)99998.486.163.330000161616161511600000145600876543210Perform histogram equalizationLecture 5:Lecture 5:Chapter 3 Spatial Domain Enhancement Chapter 3 Spatial Domain EnhancementOutput image Gray scale=0,9Equalized histogram0112233445566789No.of pixels
15、3663838663693838Results after histogram equalizationLecture 5:Lecture 5:Chapter 3 Spatial Domain Enhancement Chapter 3 Spatial Domain Enhancement 255 194 157 103 15 59 116 202 239 90 155 5 235 234 207 124 209 188 105 3 227 113 45 228 35255=11111111235=11101011188=10111100 155=10011011124=01111100 90
16、 =01011010Bit-plane 7 imageBit-plane 2 image111100111000Lecture 4:Lecture 4:Chapter 3 Spatial Domain Enhancement Chapter 3 Spatial Domain EnhancementA simple bit-plane example17241815235714164613202210121921311182529111121111910677101111131161113202210121921311182529Original imagemaskFiltered image10Lecture 7:Lecture 7:Chapter 3 Spatial Domain Enhancement Chapter 3 Spatial Domain EnhancementIllustration of a weighted average filterTechnique:Excessive blurring is generally used to eliminate small
