Data Extraction using Image Similarity

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Data ExtractionusingImage Similarity

• CIS 601
• Image Processing
• Ajay Kumar Yadav

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Overview

• Segmentation.
• Edge Detection.
• Contour Mapping.
• Image Matching

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Segmentation

• Segmentation of data is a method by which large
  sets of data is grouped into clusters of smaller
  sets of similar data.
• Segmentation techniques are also referred as
  clustering techniques. It attempts to find
  natural groups of components (or data) based on
  some similarity.

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Common Metric

• The distance between two points is taken as a
  common metric to assess the similarity among the
  components of a population

Euclidean Metric
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Type of Algorithms

• Hierarchical methods Minimal Spanning Tree
  Method
• Nonhierarchical methods K-means Algorithm

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K-mean Clustering

• Number of segmentations are provided as input to
  the algorithm.
• Depending upon the input value, number of points
  are chosen which are mutually farthest apart.
• Each component of the population is assigned to
  the respected cluster depending upon the minimum
  distance.
• The centroid's position is recalculated on
  addition of each new component.

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Example 2 Segment Image
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3 Segmented Image
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Edge Detection

• Edge detection uses the difference in color
  between the background color and the foreground
  color. The end result is an outline of the
  borders.
• Edge detection techniques are broadly classified
  as
• Gradient -Detects the edges by looking for the
  maxima and minima of the first derivative.
• Laplacian -searches for zero-crossing in the
  second derivative.

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Gradient Method

• Sobel Performs 2D spatial gradient measurement.
• Canny Uses gradient to highlight regions with
  high spatial derivatives. If gradient of x is
  zero, depending upon the value of y edge
  direction would be either 0 or 90.
• Robert Uses 2x2 convolution matrix, one mask is
  simply rotated by 90. Respond maximally to edge
  running at 45 to pixel grid.
• Prewitt Similar to sobel. Strength of edge is
  defined by sum of square of two derivatives.

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Laplacian Method

• Calculates the second derivative of the image to
  find the zero crossing .
• It also calculates the local variance and compare
  it with the threshold value. If threshold exceeds
  declare it as edge.
• Its an lowest order orientation independent
  operator.

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Edge Detection of 2-segment image
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Edge Detection of 4-segment image
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Contour Mapping

• The basic idea is to trace closed contours at
  each slice of data and then connect between
  contours in adjacent slices using a mesh of
  triangles.

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Contoured Image
Two Level Contoured Image
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Image Matching

• Recognition of the similar image in the target
  image.
• The approach considered is the crude or brute
  force method. The assumption is taken that the
  images do not differ all that much. A point is
  mapped onto the second image and a window is
  slided over the neighboring area to determine the
  resemblance of the corners. If the value passes a
  certain threshold the match is accepted.

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Algorithm

• Find the three Euclidean distances for the source
  image and the target image Starting corner,
  Ending corner, Centroid.
• Normalize all the three Euclidean distances for
  both source and target.
• Calculate the correlation coefficient between the
  similar normalized vectors of source and target.
• If image is similar the plot of the output value
  should be linear and generate a peak.

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Image Correlation
Similar Image Different Segmentation
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Image Correaltion
Correlation of Starting data shows some
similarity
Complete and Partial Image
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Image Correlation
Starting and Ending are near to matching may be
because of the posture of the images
Dissimilar Images
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Contd
Dissimilar Image
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Conclusion

• Segmentation removes noise and cluster the
  useful data in one population.
• Edge Detection Differentiate between the
  foreground and background.
• Image similarity is performed on the basis
  Euclidean correlation between the different
  Indices value.

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Project Files

• im_clustering
• (input Enter number of desired clusters in
  command window for source image)
• Save the data of well defined edges.
• Go to step 1 run the im_clustering algorithm for
  the target image and save the data extracted from
  edge detection.
• a1,a2,a3 DistView(edge)
• (input saved data of the edge detection, output
  will be the Euclidean distance array for
  starting, ending and centroid)
• Repeat the same step for target data
• out1,out2,out3cor(s1,s2,s3,t1,t2,t3)
• (Input All the Euclidean distance vector for
  source and target, output would be the
  correlation coefficient between source and target
  for starting, ending and centroid)

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References

• Digital Image Processing (Rafael Gonzalez
  Richard Woods)
• The Analysis of Simple k-means Clustering
  Algorithm (University of Maryland, January 2000)
• Contour Detection Based on Non-classical
  Receptive Field Inhibition (IEEE, Vol. 12, No. 7,
  July 2003)
• Matching and Retrieval Based on the Vocabulary
  and Grammar of Color Patterns (IEEE, Vol. 9, No.
  1, January 2000)
• Lecture Slides.