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1 Color and Texture Ch 6 and 7 of Shapiro How do we quantify them? How do we use them to segment an image?

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Presentation on theme: "1 Color and Texture Ch 6 and 7 of Shapiro How do we quantify them? How do we use them to segment an image?"— Presentation transcript:

1 1 Color and Texture Ch 6 and 7 of Shapiro How do we quantify them? How do we use them to segment an image?

2 2 Color (Summary) Color (Summary) Used heavily in human vision Used heavily in human vision Color is a pixel property, making some recognition problems easy Color is a pixel property, making some recognition problems easy Visible spectrum for humans is 400 nm (blue) to 700 nm (red) Visible spectrum for humans is 400 nm (blue) to 700 nm (red) Machines can “see” much more; ex. X-rays, infrared, radio waves Machines can “see” much more; ex. X-rays, infrared, radio waves

3 3 Factors that Affect Perception Light: the spectrum of energy that illuminates the object surface Reflectance: ratio of reflected light to incoming light Specularity: highly specular (shiny) vs. matte surface Distance: distance to the light source Angle: angle between surface normal and light source Sensitivity how sensitive is the sensor

4 4 Difference Between Graphics and Vision In graphics we are given values for all these parameters, and we create a view of the surface. In graphics we are given values for all these parameters, and we create a view of the surface. In vision, we are given a view of the surface, and we have to figure out what’s going on. In vision, we are given a view of the surface, and we have to figure out what’s going on. What’s going on?

5 5 Some physics of color: Visible part of the electromagnetic spectrum White light is composed of all visible frequencies ( ) White light is composed of all visible frequencies ( ) Ultraviolet and X-rays are of much smaller wavelength Ultraviolet and X-rays are of much smaller wavelength Infrared and radio waves are of much longer wavelength Infrared and radio waves are of much longer wavelength

6 6 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.

7 7 RGB color cube R, G, B values normalized to (0, 1) interval human perceives gray for triples on the diagonal “Pure colors” on corners

8 8 Color palette and normalized RGB Intensity I = (R+G+B) / 3 Normalized red r = R/(R+G+B) Normalized green g = G/(R+G+B) Normalized blue b = B/(R+G+B) Color triangle for normalized RGB coordinates is a slice through the points [1,0,0], [0,1,0], and [0,0,1] of the RGB cube. The blue axis is perpendicular to the page. In this normalized representation, b = 1 – r –g, so we only need to look at r and g to characterize the color.

9 9 Color hexagon for HSI (HSV) Hue is encoded as an angle (0 to 2  ). Saturation is the distance to the vertical axis (0 to 1). Intensity is the height along the vertical axis (0 to 1). intensity saturation hue H=0 is red H=180 is cyan H=120 is green H=240 is blue I=0 I=1

10 10 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%.

11 11 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

12 12 Conversion from RGB to YIQ We often use this for color to gray-tone conversion. An approximate linear transformation from RGB to YIQ:

13 13 CIE, the color system we’ve been using in recent object recognition work Commission Internationale de l'Eclairage - this commission determines standards for color and lighting. It developed the Norm Color system (X,Y,Z) and the Lab Color System (also called the CIELAB Color System). Commission Internationale de l'Eclairage - this commission determines standards for color and lighting. It developed the Norm Color system (X,Y,Z) and the Lab Color System (also called the CIELAB Color System).

14 14 CIELAB, Lab, L*a*b One luminance channel (L) One luminance channel (L) and two color channels (a and b). and two color channels (a and b). In this model, the color differences which you perceive correspond to Euclidian distances in CIELab. In this model, the color differences which you perceive correspond to Euclidian distances in CIELab. The a axis extends from green (-a) to red (+a) and the b axis from blue (-b) to yellow (+b). The brightness (L) increases from the bottom to the top of the three-dimensional model. The a axis extends from green (-a) to red (+a) and the b axis from blue (-b) to yellow (+b). The brightness (L) increases from the bottom to the top of the three-dimensional model.

15 15 References The text and figures are from The text and figures are from ml ml CIELab Color Space CIELab Color Space Color Spaces Transformations Color Spaces Transformations 3D Visualization 3D Visualization aachen.de/Inhalt/Forschung/FarbbildRepro/Farbkoerper/Visual3D.html aachen.de/Inhalt/Forschung/FarbbildRepro/Farbkoerper/Visual3D.html

16 16 Colors can be used for image segmentation into regions Can cluster on color values and pixel locations Can cluster on color values and pixel locations Can use connected components and an approximate color criteria to find regions Can use connected components and an approximate color criteria to find regions Can train an algorithm to look for certain colored regions – for example, skin color Can train an algorithm to look for certain colored regions – for example, skin color

17 17 Color histograms can represent an image Histogram is fast and easy to compute. Histogram is fast and easy to compute. Size can easily be normalized so that different image histograms can be compared. Size can easily be normalized so that different image histograms can be compared. Can match color histograms for database query or classification. Can match color histograms for database query or classification.

18 18 Histograms of two color images

19 19 Retrieval from image database Top left image is query image. The others are retrieved by having similar color histogram (See Ch 8).

20 20 How to make a color histogram Make 3 histograms and concatenate them Make 3 histograms and concatenate them Create a single pseudo color between 0 and 255 by using 3 bits of R, 3 bits of G and 2 bits of B (which bits?) Create a single pseudo color between 0 and 255 by using 3 bits of R, 3 bits of G and 2 bits of B (which bits?) Use normalized color space and 2D histograms. Use normalized color space and 2D histograms.

21 21 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 (see Ch 16). HSIHSI

22 22 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.

23 23 Finding a face in video frame (left) input video frame (left) input video frame (center) pixels classified according to RGB space (center) pixels classified according to RGB space (right) largest connected component with aspect similar to a face (all work contributed by Vera Bakic) (right) largest connected component with aspect similar to a face (all work contributed by Vera Bakic)

24 24 Swain and Ballard’s Histogram Matching for Color Object Recognition (IJCV Vol 7, No. 1, 1991) Opponent Encoding: Histograms: 8 x 16 x 16 = 2048 bins Intersection of image histogram and model histogram: Match score is the normalized intersection: wb = R + G + B rg = R - G by = 2B - R - G intersection(h(I),h(M)) =  min{h(I)[j],h(M)[j]} match(h(I),h(M)) = intersection(h(I),h(M)) /  h(M)[j] j=1 numbins j=1 numbins

25 25 (from Swain and Ballard) cereal box image 3D color histogram

26 26 Four views of Snoopy Histograms

27 27 The 66 models objects Some test objects

28 28 More test objects used in occlusion experiments

29 29 Results Results were surprisingly good. At their highest resolution (128 x 90), average match percentile (with and without occlusion) was This translates to 29 objects matching best with their true models and 3 others matching second best with their true models. At resolution 16 X 11, they still got decent results (15 6 4) in one experiment; (23 5 3) in another.

30 30 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.

31 31 K-means Clustering Example Original RGB ImageColor Clusters by K-Means

32 Texture: Ch7 A description of the spatial arrangement of color or intensities in an image or a selected region of an image.

33 33

34 34 Aspects of texture Size or granularity (sand versus pebbles versus boulders) Size or granularity (sand versus pebbles versus boulders) Directionality (stripes versus sand) Directionality (stripes versus sand) Random or regular (sawdust versus woodgrain; stucko versus bricks) Random or regular (sawdust versus woodgrain; stucko versus bricks) Concept of texture elements (texel) and spatial arrangement of texels Concept of texture elements (texel) and spatial arrangement of texels

35 Two Approaches: Two Approaches: 1. Structural Approaches: Define texture primitives, texels and the spatial relationship among them. 2. Statistical Approaches: Use statistical properties of color distribution 2. Statistical Approaches: Use statistical properties of color distribution 35

36 1. Structural Approaches: Texels 36 Extract texels by thresholding convolıtion or morphological operations Extract texels by thresholding convolıtion or morphological operations

37 1. Structural Approaches Texel Based 37 1.Find the texels by simple procedures 2.Get the Voronoi tessalations: Define perpendicular bisector between the texels P and Q as

38 38 Problem with Structural Approach How do you decide what is a texel? Ideas?

39 39 2. Statistical Approaches Natural Textures from VisTex grassleaves What/Where are the texels?

40 Need a mesure for similarity 40

41 41 2. Statistical Approaches Segmenting out texels is difficult or impossible in real images. Numeric quantities or statistics that describe a texture can be computed from the gray tones (or colors) alone. This approach is less intuitive, but is computationally efficient. It can be used for both classification and segmentation.

42 42 Some Simple Statistical Texture Measures 1. Edge Density and Direction Use an edge detector as the first step in texture analysis. The number of edge pixels in a fixed-size region tells us how busy that region is. The directions of the edges also help characterize the texture

43 43 Two Edge-based Texture Measures 1. edgeness per unit area for a pixel p 2. edge magnitude and direction histograms F edgeness = |{ p | gradient_magnitude(p)  threshold}| / N where N is the size of the unit area F magdir = ( H magnitude, H direction ) where these are the normalized histograms of gradient magnitudes and gradient directions, respectively.

44 44 Original Image Frei-Chen Thresholded Edge Image Edge Image

45 45

46 46 Local Binary Pattern Measure For each pixel p, create an 8-bit number b 1 b 2 b 3 b 4 b 5 b 6 b 7 b 8, where b i = 0 if neighbor i has value less than or equal to p’s value and 1 otherwise. Represent the texture in the image (or a region) by the histogram of these numbers. Compute L1 distance between two histograms:

47 47 Fids (Flexible Image Database System) is retrieving images similar to the query image using LBP texture as the texture measure and comparing their LBP histograms

48 48 Low-level measures don’t always find semantically similar images.

49 49 Co-occurrence Matrix Features Define a spatial relationship specified by a vector d = (dr,dc) d(0,1) d(1,0) d(1,1) Compute the statistics of d within an image region C d (i,j) indicates how many times value i co-occurs with value j in a particular spatial relationship d.

50 j i 1 3 d = (3,1) C d gray-tone image co-occurrence matrix From C we can compute N, the normalized co-occurrence matrix, where each value is divided by the sum of all the values. d d

51 51 Co-occurrence Features sums. What do these measure? Energy measures uniformity of the normalized matrix.

52 52 But how do you choose d? This is actually a critical question with all the statistical texture methods. Are the “texels” tiny, medium, large, all three …? Not really a solved problem. Zucker and Terzopoulos suggested using a  statistical test to select the value(s) of d that have the most structure for a given class of images. 2

53 53 A classical texture measure: Autocorrelation function Autocorrelation function can detect repetitive patterns of texels Autocorrelation function can detect repetitive patterns of texels Also defines fineness/coarseness of the texture Also defines fineness/coarseness of the texture Compare the dot product (energy) of non shifted image with a shifted image Compare the dot product (energy) of non shifted image with a shifted image

54 54 Interpreting autocorrelation Coarse texture  function drops off slowly Coarse texture  function drops off slowly Fine texture  function drops off rapidly Fine texture  function drops off rapidly Can drop differently for r and c Can drop differently for r and c Regular textures  function will have peaks and valleys; peaks can repeat far away from [0, 0] Regular textures  function will have peaks and valleys; peaks can repeat far away from [0, 0] Random textures  only peak at [0, 0]; breadth of peak gives the size of the texture Random textures  only peak at [0, 0]; breadth of peak gives the size of the texture

55 SASI: A generic texture descriptor for image retrieval A. Çarkacıoğlu, F.T. Yarman Vural Base Clique Types Base Clique Types Clique Chains Clique Chains 55

56 Clique windows Clique windows 56

57 Algorithm of SASI Algorithm of SASI 57

58 58 Laws’ Texture Energy Features Design texture filters Convolute the input image using texture filters. Compute texture energy by summing the absolute value of filtering results in local neighborhoods around each pixel. Combine features to achieve rotational invariance.

59 59 Law’s texture masks (1)

60 60 Law’s texture masks (2) Creation of 2D Masks E5 L5 E5L5

61 61 9D feature vector for pixel Dot product 16 5x5 masks with neighborhood Dot product 16 5x5 masks with neighborhood 9 features defined as follows: 9 features defined as follows:

62 62 Features from sample images

63 63 water tiger fence flag grass small flowers big flowers Is there a neighborhood size problem with Laws?

64 64 Fourier power spectrum High frequency power  fine texture High frequency power  fine texture Concentrated power  regularity Concentrated power  regularity Directionality  directional texture Directionality  directional texture

65 65 Fourier power spectrum High frequency power  fine texture High frequency power  fine texture Concentrated power  regularity Concentrated power  regularity Directionality  directional texture Directionality  directional texture

66 66 Fourier example

67 67 Notes on Texture by FFT The power spectrum computed from the Fourier Transform reveals which waves represent the image energy.

68 68 Stripes of the zebra create high energy waves generally along the u-axis; grass pattern is fairly random causing scattered low frequency energy y x v u

69 69 More stripes Power spectrum x 64

70 70 Spectrum shows broad energy along u axis and less along the v-axis: the roads give more structure vertically and so does the regularity of the houses

71 71 Spartan stadium: the pattern of the seats is evident in the power spectrum – lots of energy in (u,v) along the direction of the seats.

72 72 Getting features from the spectrum FT can be applied to square image regions to extract texture features FT can be applied to square image regions to extract texture features set conditions on u-v transform image to compute features: f1 = sum of all pixels where R1 < || (u,v)|| < R2 (bandpass) set conditions on u-v transform image to compute features: f1 = sum of all pixels where R1 < || (u,v)|| < R2 (bandpass) f2 = sum of pixels (u,v) where u1 < u

73 73 Filtering or feature extraction using special regions of u-v F1 is all energy in small circle F4 is all energy in directional wedge F2 is all energy near origin (low pass) F3 is all energy outside circle (high pass)

74 74 What else? Gabor filters (we’ve used a lot) 3D textons (Leung and Malik) Polarity, anisotropy, and local contrast (Blobworld)


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