Chapter 8. Basic Image Process

1. Chapter 8. Basic Image Process The visual information can be recorded by a TV camera or a 2D array of CCD sensors. A digital image is formed.

3. Digital image formation • visual information recorded by a TV camera or a two dimensional array of CCD sensors • analog to digital (A/D) converter : digitizing the light intensity values. • black-white image: pixel matrix of digital grey level value • colour image: pixel matrix of R / G/ B -- optical information changed into electrical signals, finally turn out to be computer image files.

4. Color Terminology • Hue • Color wheel • Primary, secondary, tertiary • Chroma (saturation) • Intensity or purity • Distance from gray • Value (lightness or brightness)

5. eyedropper Many, many RGB/HSV/HSL Color Tools

6. Perceptual Organization of ColorNot “RGB” as in Colorimetry Lightness Colorfulness Hue

7. Chromatic Adaptation Original image Overall Purple Tint Tint Shirt Only

10. application of basic image processing in computer vision : • conditioning the image, either by highlighting its contrast or by filtering • devoted to eliminating non-relevant information from the scene • extracting characteristics, enable us to proceed to its classification, localization, manipulation and so on

11. Image Highlight • improve the contrast • carried out by an amplification of non-linear characteristic Texture Using color Using underlining A flying box leads attention Blinking momentarily attracts attention Blinking momentarily attracts attention Motion elicits an orienting response

12. very clear image: amplification of logarithmic characteristic • The dynamic range of an image can be compressed by replacing each pixel value with its logarithm. • Enhance the low intensity pixel values, but compress high intensity values into a relatively small pixel range. if an image contains some important high intensity information, applying the logarithmic operator might lead to loss of information. Original Image Logarithmic Image

13. Log Transformation • Large range – 0 to 1.5*106 • Brightest pixels dominate • Log transformation reduces the range to 0 to 6.2

14. dark images: amplification of exponential characteristic • Like the logarithmic transform, they are used to change the dynamic range of an image. • In contrast to the logarithmic operator, they enhance high intensity pixel values. • Basis number is used depends on the desired degree of compression of the dynamic range.

15. the dual of the logarithmic transform The basis for the exponential operator should be the same as it was for the logarithmic transform. Applying logarithmic operator Applying exponential operator

16. Original Image Applying an exponential transform with the base 1.005 Applying an exponential transform with the base 1.01

17. image containing areas with light and dark tones vary continuously the exponential-linear-logarithmic characteristic of the amplifier

18. 2. Filtering and Background Extraction • 1. Filtering • image may contain a high proportion of noise • filtering carried out • in the spatial domains: Each pixel is obtained from a weighted averaging with the values for luminosity of the pixels in its neighborhood • in the frequency domains: Fourier transform operator • in the temporal domain from a certain number of images: obtaining an intensity value for each point from the date for the corresponding points in previous images

19. Spatial filter: • Pixel gray value = a weighted averaging of its neighbors • Example: a 3x3 operator:

20. Functional diagram of a processor which carries out a spatial domains filter of an image making a serial/parallel conversion of the nn pixels considered and using a weighter

21. Low pass filters • Removes high frequency components • Better filter, weights centre pixel more • Moving average of time series smoothes • Average (up/down, left/right) • smoothes out sudden changes in pixel values • removes noise • introduces blurring • Classical 3x3 template

22. Example of Low Pass Gaussian, sigma=3.0 Original

23. Example of High Pass Laplacian Filter - 2nd derivative

24. More e.g.’s Horizontal Sobel Vertical Sobel 1st derivative

25. Temporal filter:frequency domain For random noise in continuous image Recursive filters: Non-recursive filters:

26. 2. Background Extraction • image with little contrast, difficultly distinguishing between the objects and the background • making a periodical subtraction between consecutive image.  result is of a sufficiently low value for all the pixel – background, stored it in memory  result stops giving null values in a high proportion of pixels – object appears • in real time, subtracting the image of object obtained from the digitizer from the image contained in memory.

27. Eliminating a large volume of non-relevant data.Help to distinguishing the objects from the background Periodical subtraction between consecutive image. Background region:  eliminate, black Object appear region:  Date remain in the memory

28. Functional diagram of a recursive algorithm to generate a reference image

29. + Background Extraction

30. 3. Obtain Contour Elements Contour of object: -- Points separating the regions -- Important information -- relevant to their identification Advantages of getting contour:-- considerable reduction in the volume of data -- relative stability under fluctuation of light

31. The contours are composed of the points separating the different regions in an image, a sharp variation in the grey level appears in these points. The partial derivation of I(x,y) with respect to x Del operator P(x,y) contour point Operator G(x,y)= f(Hx, Hy)– matrix type

32. characteristics to evaluate the degree of effectiveness of a contour extractor: • Sensitivity • Continuity of the contours obtained • Degree of contour thinning • Complexity-Calculation time

33. Edge coding & image compression Edge detection Edge decoding. Is it possible?

34. TV inpainting: applications in edge-based image decoding edge tube T No extra data are needed. Just inpaint!

35. “Paulina, now you are ready to turn around …” edge tube T No extra data are needed. Just inpaint!

36. 4. Contour Thinning and Feature Extraction • 1. Contour Thinning • eliminate the redundant pixels from the gradient image until contours of single pixel thickness are obtained • applies an algorithm of elimination of the redundant points of a contour • a horizontal sweep is made and the pixels selected to belong to the thinned contour are marked • a vertical sweep is made and the redundant pixels are eliminated

37. Thinning of the contour

38. 2. Feature Extraction •  geometrical or local features of the objects may be used for the formation of these vectors. • measurement of the area : adding the number of pixels defining the object • measurement of the perimeter: weighted sum of the number of pixels of the contour  analysis of local features, such as vertices, holes or marks.

39. Processor for detecting holes in real time

40. Morphing

41. Morphing

42. Sharpening before after

44. Thank you