Chapter 4 Imaging Pre-Processing. Comunicação Visual Interactiva

Transcription

1 Chapter 4 maging Pre-Processing Comunicação Visual nteractiva

2 The need of pre-processing

3 The need of pre-processing n this chapter we are going to stud mage enhacement Realçamento de imagem mage restauration Restauração de imagem 3

4 Gra level transformation Enhancement Gamma correction f O O Omin ma min ma min min 4

5 Gamma correction f 1/ - Eample nput image f 0.1 f 0.3 Pseudo code for RGB color images f 0.5 f 0.7 gammacorrection = 1 / gamma colour = GetPielColour newred = 55 * Redcolour / 55 ^ gammacorrection newgreen = 55 * Greencolour / 55 ^ gammacorrection newblue = 55 * Bluecolour / 55 ^ gammacorrection PutPielColour = RGBnewRed newgreen newblue 5

6 Histogram equalization Enhancement 6

7 Histogram equalization Eample Value Count Value Count Value Count Value Count Value Count Given the histogram of the original image the transformation is accomplished b using a cumulative function that is PMF probabilit mass function CDF - cumulative distribution function 7

8 Histogram equalization Eample Value Count Value Count Value Count Value Count Value Count v Piel ntensit cdfv hv Equalized v

9 Histogram equalization Eample 9

10 Small regions removal Salt & pepper noise removal Removal of connected components with small area Algorithm: f the neighborhood of an image piel is equal to the given mask then the piel in the output image is replaced with the value of its neighbors 10

11 The need of smoothness operations Algorithm: Output_mage [ij] = average of some neighborhood of nput_mage [ij] 11

12 Smoothness low pass filter of an image Mean filter bo filter Gaussian filter mage smoothing 1 N j c i r c r O N N i N N j 1 d e g c c d N N i N N j j c i r j i g c r O 1

13 Median filtering Let A[ i] i0 n1 be an ordered list of real numbers. The median of the set A is the value A[n-1/] Eamples 13

14 Temporal Filtering with Median Filtragem de Mediana da Sequência Median filtering of the sequence 14

15 General Algorithm 15

16 Differential operators in 1D signals f ' i f i i f i1 i 1 Edge detection nput 1D mask centered 16

17 One Dimensional Masks 17

18 Differential operators Properties The mask coordinates must have opposite signs to obtain a maimum response output whenever eists intensit transitions contrast The sum of the values must be zero to obtain a zero response when the input region is constant. The first order derivative masks produce high absolute values in points with large contrast The second order derivative masks produce zero-crossing in points with large contrast Smoothness operators The mask elements are positive and sum one such that the output is equal to the input regions with constant intensit The smoothness and noise removal are proportional to the mask dimensions Abrupt transitions step edges are more blurred as the mask dimension increases. 18

19 Gradient of a function Differential Operators D f f f ] / / / 1 1 [ 3 1 f f ] / / / 1 1 [ 3 1 f f Eample: f f f / tan 1 f f 19

20 Corner Points Detectors 0

21 Detecting Edges with Cann Original Cann = 1 Cann = 4 Original Cann = 1 Roberts 0% 1

22 Gaussian filters 1 '' ' 1 4 g g g g e g r D case: r g h 1D case:

23 Edge Detection with Laplacian LOG Filter g g g L L Meican hat sombrero Mask 1111 = 3

24 Neural Networks and Mach band effect Retinal cells are sensible to light intensit level 1 ntegrated cells are sensible to intensit transitions level Bandas de Mach 4

25 Marr-Hildreth Theor The LOG filter helps to understand the Human Vision Sstem low level The main goal is to build the main sketch primal sketch: lines edges blobs Multi-resolution analsis LOG filtering with large allows the detection of the main structures in the image whilst the details are obtained b filtering with small. 5

26 Perceptual grouping - virtual lines Virtual line Output of the two LOG filters Virtual lines in real images 6