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Feature Extraction Statistical Feature Extraction(Texture) Geometry Feature Extraction(Fingerprint)

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Texture Feature Extraction Texture is a description of the spatial arrangement of color or intensities in an image or a selected region of an image.

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Natural Textures grassleaves

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Texture as Statistical Feature 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.

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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

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Two Edge-based Texture Measures 1. edgeness per unit area 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.

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Original Image Frei-Chen Thresholded Edge Image Edge Image Example

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Local Binary Pattern 100 101 103 40 50 80 50 60 90 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 ps value and 1 otherwise. Represent the texture in the image (or a region) by the histogram of these numbers. 1 1 1 1 1 1 0 0 1 2 3 4545 7 6 8

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Co-occurrence Matrix Features A co-occurrence matrix is a 2D array C in which Both the rows and columns represent a set of possible image values. C (i,j) indicates how many times value i co-occurs with value j in a particular spatial relationship d. The spatial relationship is specified by a vector d = (dr,dc). d

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1 1 0 0 0 0 2 2 j i 1 3 d = (3,1) 0 1 2 012012 1 0 3 2 0 2 0 0 1 CdCd gray-tone image co-occurrence matrix From C d we can compute N d, the normalized co-occurrence matrix, where each value is divided by the sum of all the values. Co-occurrence Matrix

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Co-occurrence Features

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Laws Texture Energy Features The Laws Algorithm : Filter 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.

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Laws texture masks (1)

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Laws texture masks (2) Creation of 2D Masks E5 L5 E5L5

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9D feature vector for pixel Subtract mean neighborhood intensity from (center) pixel Apply 16 5x5 masks to get 16 filtered images F k, k=1 to 16 Produce 16 texture energy maps using 15x15 windows E k [r,c] = |F k [i,j]| Replace each distinct pair with its average map: 9 features (9 filtered images) defined as follows:

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Laws Filters

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water tiger fence flag grass small flowers big flowers Example: Using Laws Features to Cluster

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Features from sample images

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

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Interpreting autocorrelation Coarse texture function drops off slowly Fine texture function drops off rapidly Can drop differently for r and c 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

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

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Feature Extraction Process Input: image Output: pixel features Color features Texture features Position features Algorithm: Select an appropriate scale for each pixel and extract features for that pixel at the selected scale Pixel Features Polarity Anisotropy Texture contrast feature extraction Original image

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Geometry Feature Extraction (Fingerprint)

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Image Enhancement - Gabor Filtering

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Image Processing (2.2) Raw ImageEnhanced Image Skeleton Image

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Ending PointBifurcation Point Fingerprint Feature : Minutia

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Minutia Extraction