Image analysis and computer vision

1. Image analysis and computer vision

2. Image processing basic steps 1. Image Enhancement 2. Image Restoration 3. Image Analysis 4. Image compression 5. Image Synthesis

3. Goal in image analysis • Image analysis operations are used in applications, that require the measurement and classification of image information • Examples: • Cell recognition from tissue sample • Object recognition from conveyor belt • Zip code reading from envelope

5. Group discussion • List application possibilities for image analysis!

6. Image analysis • Basis is visual image, whose content should be interpreted • As a result mostly non-image data • As a goal is to understand images content classifying its content

7. Exaple: Robot vision

8. Example: Robotvehicle

9. Example: Traffic analysis

10. Image analysis operations

11. Image analysis operations • Segmentation • operation that highlights individual objects within an image. • Feature Extraction • after segmentation->measure the individual features of each object • Object Classification • classify the object to particular category segment feature classification space space space feature classification Segmentation extraction feature class image distinct objects

12. Segmentation operations • Image Preprocessing • Initial Object Discrimination • Image Morphological Operations segment feature classification space space space feature classification Segmentation extraction feature class image distinct objects

13. Image Preprocessing • In preprocessing eg. change images contrast, filter noise and remove distracting image background • Image enhancement operations is used

15. Initial Object Discrimination • Separates image objects into rough groups with like characteristics using image enhancement operations • Outlining and contrast enhancement often used

16. Initial object discrimination - example Original Binary contrast enhanced

17. Initial object discrimination - example • Sobel edge-enhancement • 1 Filter with horizontal mask • 2 Filter with vertical mask • 3 Add • -1 0 1 -1 -2 -1 • -2 0 2 0 0 0 • -1 0 1 1 2 1

18. Morphological processing • In preprocessed image the boundaries are very rough-> need to “clean up” • Morphological operations

19. Morphological operations • Binary operations • Erosion and dilation • Opening and Closing • Outlining • Skeletonization • Gray-scale operations • Top-Hat and Well transformations • Morphological gradient • Watershed edge detection

20. Binary morphology • Focus on two brightness values. black=0, white=255 • Technically same as spatial convolution • combines pixel brightness with a structuring element, looking for specific pattern • Array of logical values • (cut=AND, union=OR, complement=NOT)

22. Binary morphology - equation O(x,y) = 0 or 1 (predefined) if X =I (x,y) AND X0 =I (x+1,y) AND X1 =I (x+1,y-1) AND X2 =I (x,y-1) AND X3 =I (x-1,y-1) AND X4 =I (x-1,y) AND X5 =I (x-1,y+1) AND X6 =I (x,y+1) AND X7 =I (x+1,y+1) otherwise, O(x,y) = opposite state

23. Erosion • Reduces the size of the objects in relation to their background Mask 1 1 1 O(x,y) = 1 if “Hit” 1 1 1 1 1 1 = 0 if “Miss” 1 1 1

24. Example (1/2) Original Binary contrast enhanced Erosion image

25. Example (2/2) Binary contrast enhanced Eroded image

26. Dilation • Uniformly expands the size of object Mask 0 0 0 O(x,y) = 0 if “Hit” 0 0 0 = 1 if “Miss” 0 0 0

27. Example Binary contrast enhanced Dilated image

28. Opening • Erosion then Dilation • Removes one pixel mistakes like erosion • Object size remains Binary contrast enhanced Erosion Dilation

29. Closing • Dilation + erosion • Fills pixel wide holes • Object size remains Binary contrast enhanced Dilation Erosion

30. Cleaning Original binary contrast enhanced image Opening Cleaned = Opened and Closed

31. Outlining • Forms one-pixel-wide outlines and tends to be more immune to image noise than most edge enhancement operations • Implementation: • Eroded image subtract from original

32. Outlining Original Binary contrast enhanced

33. Outlining Eroded image Original - Eroded image

34. Skeletonizing • Make “wireframe” model from image • Uses different erosion masks • Analogy: fire, which burns object from each side

35. Gray-scale operations • Used when binary operations degrade an image • Gray-scale operation can be followed binary operation • Mask terms -255 ... 255 or “Don’t care”

37. Erosion and Dilation • Erosion reduces the size of objects by darken the bright areas in image • Dilation is inverse operation

38. Original Erosion Dilation Erosion and Dilation example

39. Opening and Closing • Opening = erosion + dilation • Opening reduces noise pixels • Closing = dilation + erosion • Closing fills one-pixel-wide holes

40. Opening example

41. Opening - example 2

42. Eroded Dilated Morphological gradient • Images outlines as a result • Make copy from image. Erosion to other image and dilation to other. Then images subtract from each other using a dual-image point process. Gradient Image Original Eroded - dilated =

43. Morphological gradient Original Erosion Dilation Gradient=Erosion-Dilation

44. Feature Extraction • Operation followed by segmentation • Choose essential features and measure them from objects • Goal is to find features, which help find out object’s class easier segment feature classification space space space feature classification Segmentation extraction feature class image distinct objects

45. Features • Brightness and color • Texture • Shape • Spatial moments • Edge shape

46. Pornographic image analysis

48. Feature: Brightness and color Histogram can show • Color (sorting by colors) • Brightness • average brightness • Standard deviation brightness • mode brightness • sum of all pixel brightnesses<-> energy (zero-order spatial moment)

49. Example: Fruit sorting Problem: • Boxes goes on conveyor belt, which has green apples (Granny Smith) and red apples (Red Delicious) and also oranges • Sort boxes to the correct follow on conveyor belts automatically

50. Example: Fruit sorting Solution: • Capture image with camera to RGB images • Convert RGB to HSL • Explore Hue color component which fruit it is