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COLORCOLOR A SET OF CODES GENERATED BY THE BRAİN How do you quantify? How do you use?

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Presentation on theme: "COLORCOLOR A SET OF CODES GENERATED BY THE BRAİN How do you quantify? How do you use?"— Presentation transcript:

1 COLORCOLOR A SET OF CODES GENERATED BY THE BRAİN How do you quantify? How do you use?

2 Tiger in forest? cat on a rug?

3 Color perception depends on

4 What is blue? A blue object has a surface material that reflects wavelength corresponding blue, when illuminated by white light

5

6 İn the retina: Cones:6-7 Milion

7 RODES 80-100 Million for Shape perception

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9

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11 Ganglions take difference

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13 FMRI: At least 65percent of cortex is devoted to vision

14 14 Coding methods for humans RGB is an additive system (add colors to black) used for displays. CMY is a subtractive system for printing. HSI is a good perceptual space for art, psychology, and recognition. YIQ used for TV is good for compression.

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16 RGB COLOR SPACE

17 Need to quantize

18 RGB System is Additive

19 Color Normalization

20 Choromaticity: Normalize r+g+b=1 Plot g vs. b

21 Choromaticity: Normalize r+g+b=1 Plot g vs. b Choromaticity: Normalize r+g+b=1 Plot g vs. b

22 HSI Color coordinate system

23 HSI Color System

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25 HUE İN HSI

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28 28 Editing saturation of colors (Left) Image of food originating from a digital camera; (center) saturation value of each pixel decreased 20%; (right) saturation value of each pixel increased 40%.

29 Intensity carries the most of the info

30 Noise added

31 31 YIQ and YUV for TV signals Have better compression properties Have better compression properties Luminance Y encoded using more bits than chrominance values I and Q; humans more sensitive to Y than I,Q Luminance Y encoded using more bits than chrominance values I and Q; humans more sensitive to Y than I,Q Luminance used by black/white TVs Luminance used by black/white TVs All 3 values used by color TVs All 3 values used by color TVs YUV encoding used in some digital video and JPEG and MPEG compression YUV encoding used in some digital video and JPEG and MPEG compression

32 32 Conversion from RGB to YIQ We often use this for color to gray-tone conversion. An approximate linear transformation from RGB to YIQ:

33 How do you use color? Image retrieval Segmentation Recognition Detection Edge detection Connected component analysis

34 34 Color histograms can represent an image Histogram is fast and easy to compute. Size can easily be normalized so that different image histograms can be compared. Can match color histograms for database query or classification.

35 35 How to make a color histogram Make 3 histograms and concatenate them Use normalized color space and 2D histograms. Create a single pseudo color between 0 and 255 by using 2 bits of R, 2 bits of G and 2 bits of B (which bits?)

36 36 Histograms of two color images: Take 2 most significant bit in R,G,B obtain 2 6 =64 bins

37 Histogram Similarity Normalized histogram approximates the pdf – Look at cross entropy – Look at the sum of suare error Look at intersection and match

38 38 Retrieval from image database Top left image is query image. The others are retrieved by having similar color histogram

39 39 Recognition: Apples versus Oranges Separate HSI histograms for apples (left) and oranges (right) used by IBM’s VeggieVision for recognizing produce at the grocery store checkout station HSIHSI

40 40 Face detection in video frame: Train first (left) input video frame (center) pixels classified according to RGB space (right) largest connected component with aspect similar to a face (by Vera Bakic)

41 41 Skin color in RGB space (shown as normalized red vs normalized green) Purple region shows skin color samples from several people. Blue and yellow regions show skin in shadow or behind a beard.

42 recognition

43 43 Color Clustering by K-means Algorithm Form K-means clusters from a set of n-dimensional vectors 1. Set ic (iteration count) to 1 2. Choose randomly a set of K means m1(1), …, mK(1). 3. For each vector xi, compute D(xi,mk(ic)), k=1,…K and assign xi to the cluster Cj with nearest mean. 4. Increment ic by 1, update the means to get m1(ic),…,mK(ic). 5. Repeat steps 3 and 4 until Ck(ic) = Ck(ic+1) for all k.

44 44 K-means Clustering Example Original RGB ImageColor Clusters by K-Means

45 Color Edge Detection

46 Color edge detection

47 Color Edge detection

48

49 Chapter 6 Color Image Processing Chapter 6 Color Image Processing

50 Study Shading


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