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Grouping Using Factor Graph: An Approach for finding Text with a Camera Phone

This paper discusses an algorithm that utilizes a factor graph model to efficiently detect and group text in images captured with a camera phone. The algorithm employs feature selection techniques and simplifications to solve the problem non-iteratively, making it suitable for resource-constrained mobile devices.

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Grouping Using Factor Graph: An Approach for finding Text with a Camera Phone

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  1. Grouping Using Factor Graph: An Approach for finding Text with a Camera Phone Huiying Shen and James Coughlan Smith-Kettlewell Eye Research Institute San Francisco, USA

  2. Motivation

  3. Motivation “Webster”

  4. More Examples Find street signs, restaurants, addresses Find indoor signs as landmarks and info tags

  5. Why a Cell Phone • Everyone has one • A Smart phone is a capable computer • But, it has limited resources, compared to a desktop, • And, has NO float point processor • A simple and efficient algorithm is needed,……

  6. Why a Cell Phone • Everyone has one • A Smart phone is a capable computer • But, it has limited resources, compared to a desktop, • And, has NO float point processor • A simple and efficient algorithm is needed,……

  7. Why a Cell Phone • Everyone has one • A Smart phone is a capable computer • But, it has limited resources, compared to a desktop, • And, has NO float point processor • A simple and efficient algorithm is needed,……

  8. Why a Cell Phone • Everyone has one • A Smart phone is a capable computer • But, it has limited resources, compared to a desktop, • And, has NO float point processor • A simple and efficient algorithm is needed,……

  9. Why a Cell Phone • Everyone has one • A Smart phone is a capable computer • But, it has limited resources, compared to a desktop, • And, has NO float point processor • A simple and efficient algorithm is needed,……

  10. Our Algorithm: Feature Selection and Factor Graph • Feature Selection • Finding simple edges of four orientation • Build up the more complex features • Graph Model • Group the features into factors • Build the data driven graph model • Using a simplified version of graph model to solve it non-iteratively

  11. Our Algorithm: Feature Selection and Factor Graph • Feature Selection • Finding simple edges of four orientations • Build up the more complex features • Graph Model • Group the features into factors • Build the data driven graph model • Using a simplified version of graph model to solve it non-iteratively

  12. Our Algorithm: Feature Selection and Factor Graph • Feature Selection • Finding simple edges of four orientations • Build up the more complex features • Graph Model • Group the features into factors • Build the data driven graph model • Using a simplified version of graph model to solve it non-iteratively

  13. Feature Selection • Machine learning approaches • Filter banks • Sliding windows • Learn the filters statistically • Problems • Lots of filters, difficult to understand and improve • Require huge training data set • Possible over-learning • Possibly learning wrong things

  14. Feature Selection • Machine learning approaches • Filter banks • Sliding windows • Learn the filters statistically • Problems • Lots of filters, difficult to understand and improve • Require huge training data set • Possible over-learning • Possibly learning wrong things

  15. Feature Selection: bottom up

  16. Matched Vertical/Horizontal Edgelets

  17. Anchored Vertical Edgelets --- Many factors are found according to the tops and the bottoms of the edgelets

  18. A factor graph model --- Variables --- Factors

  19. The Equations

  20. Our Simplifications • The variables have only two states: x=0 for ground, and x=1 for figure • A factor potential function g(x) has only two values: g(x) = g1 x=1 for all x’s = g0, otherwise • g1 >= g0, i.e., “if you don’t have nice things to say, say something neutral”

  21. Our Simplifications • The variables have only two states: x=0 for ground, and x=1 for figure • A factor potential function g(x) has only two values: g(x) = g1 x=1 for all x’s = g0, otherwise • g1 >= g0, i.e., “if you don’t have nice things to say, say something neutral”

  22. Our Simplifications • The variables have only two states: x=0 for ground, and x=1 for figure • A factor potential function g(x) has only two values: g(x) = g1 x=1 for all x’s = g0, otherwise • g1 >= g0, i.e., “If you don’t have anything nice to say, don’t say anything”

  23. An Example Factor Graph fA(x1,x2,x5) – top of chars w/ ascender fB(x3,x4,x6,x7,x8) – top of w/o ascender fC(x3,x4,x5,x6,x7,x8) – bottoms of 1st 6 sticks fD(x4,x5,x6,x7,x8,x9) – bottoms of next 6 sticks

  24. An Example Factor Graph fA(x1,x2,x5) – top of chars w/ ascender fB(x3,x4,x6,x7,x8) – top of w/o ascender fC(x3,x4,x5,x6,x7,x8) – bottoms of 1st 6 sticks fD(x4,x5,x6,x7,x8,x9) – bottoms of next 6 sticks

  25. Our Non-Iterative Algorithm • Set mx->f(x=0) = 0 initially • Shift mf->x(x) after each iteration such that mf->x(x=0) = 0 • The Algorithm converges after first iteration • It can be implemented using fixed point operation only

  26. Our Non-Iterative Algorithm • Set mx->f(x=0) = 0 initially • Shift mf->x(x) after each iteration such that mf->x(x=0) = 0 • The Algorithm converges after first iteration • It can be implemented using fixed point operation only

  27. An example

  28. More examples

  29. Summary / Discussion • A simplified factor graph algorithm is developed for camera cell phones • Variables take only two states • Factor functions g(x) take only two values • “No bad mouth”: g1 >= g0 • Non-iterative • No floating point computation

  30. Summary / Discussion • A simplified factor graph algorithm is developed for camera cell phones • Variables take only two states • Factor functions g(x) take only two values • “No bad mouth”: g1 >= g0 • Non-iterative • No floating point computation

  31. Summary / Discussion • A simplified factor graph algorithm is developed for camera cell phones • Variables take only two states • Factor functions g(x) take only two values • “No bad mouth”: g1 >= g0 • Non-iterative • No floating point computation

  32. Summary / Discussion • A simplified factor graph algorithm is developed for camera cell phones • Variables take only two states • Factor functions g(x) take only two values • “No bad mouth”: g1 >= g0 • Non-iterative • No floating point computation

  33. Summary / Discussion • A simplified factor graph algorithm is developed for camera cell phones • Variables take only two states • Factor functions g(x) take only two values • “No bad mouth”: g1 >= g0 • Non-iterative • No floating point computation

  34. Summary / Discussion • A simplified factor graph algorithm is developed for camera cell phones • Variables take only two states • Factor functions g(x) take only two values • “No bad mouth”: g1 >= g0 • Non-iterative • No floating point computation

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