嵌入式視覺 feature extraction statistical feature extraction(texture) geometry feature...

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

Author: frederica-dickerson

Post on 16-Dec-2015

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  • Slide 1
  • Feature Extraction Statistical Feature Extraction(Texture) Geometry Feature Extraction(Fingerprint)
  • Slide 2
  • 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.
  • Slide 3
  • Natural Textures grassleaves
  • Slide 4
  • 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.
  • Slide 5
  • 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
  • Slide 6
  • 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.
  • Slide 7
  • Original Image Frei-Chen Thresholded Edge Image Edge Image Example
  • Slide 8
  • 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
  • Slide 9
  • 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
  • Slide 10
  • 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
  • Slide 11
  • Co-occurrence Features
  • Slide 12
  • Slide 13
  • 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.
  • Slide 14
  • Laws texture masks (1)
  • Slide 15
  • Laws texture masks (2) Creation of 2D Masks E5 L5 E5L5
  • Slide 16
  • 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:
  • Slide 17
  • Laws Filters
  • Slide 18
  • Slide 19
  • water tiger fence flag grass small flowers big flowers Example: Using Laws Features to Cluster
  • Slide 20
  • Features from sample images
  • Slide 21
  • 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
  • Slide 22
  • 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
  • Slide 23
  • Fourier power spectrum High frequency power fine texture Concentrated power regularity Directionality directional texture
  • Slide 24
  • 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
  • Slide 25
  • Geometry Feature Extraction (Fingerprint)
  • Slide 26
  • Image Enhancement - Gabor Filtering
  • Slide 27
  • Image Processing (2.2) Raw ImageEnhanced Image Skeleton Image
  • Slide 28
  • Ending PointBifurcation Point Fingerprint Feature : Minutia
  • Slide 29
  • Minutia Extraction