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Ridge and valley are the main components in an image, especially in fingerprints.

Ridge and valley are the main components in an image, especially in fingerprints. What is ridge and valley? How do we detect them?. Ridge and valley. Ridge and valley. Intuition: a sequence of pixels having intensity values higher(lower) than those neighboring sequence.

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Ridge and valley are the main components in an image, especially in fingerprints.

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  1. Ridge and valley are the main components in an image, especially in fingerprints. • What is ridge and valley? • How do we detect them?

  2. Ridge and valley

  3. Ridge and valley • Intuition: a sequence of pixels having intensity values higher(lower) than those neighboring sequence. • The meeting curve of two ascending (descending) surfaces.

  4. Intuitive methods • Intuitive method is to extract the zero-crossings of the first directional derivative taken in a direction which extremizes the second directional derivative. • First directional derivative: • For details see R. Haralick(1983)

  5. Height condition • Haralick’s idea can be extended to d dimensional images. • Let be a d-D images. is its gradient and is its Hessian matrix. • Let be the eigenvalues of and be their corresponding eigenvetors.

  6. Height condition(cont.) • A n-D( )ridge(valley) can be characterized as: and if ridge if valley See paper “Ridges for Image Analysis” (1994)

  7. Other methods • In Jain’s work[1997], ridges are obtained by convolution with two masks. • In O’Gorman and Nickerson’s work[1989], a k by k spatial filter mask is used for labeling the pixels as foreground or background. • Mehtre and Chatterjee[1989] described a method based on some statistics of the local orientations of the ridges of the original image.

  8. Combinatorial Classification • I mainly introduce this classification of pixels for ridge extraction in a gray-scale fingerprint image. • Two-fold process: a new combinatorial pixel classification scheme for ridge extraction and a binary thinning of the ridges.

  9. Three classes of pixels • Ridge, valley and slope.

  10. Directions and gradients • We can compute the directional gradients for every pixel based on their neighbor pixel values. • 4 directions • Sign of the gradients

  11. Classification • Based on the 4 directional gradients we classify every pixel by a look-up table. • Preliminary classification • Final classification based on average pixel value on the k-size neighborhood

  12. Thinning • The image after the above operation reduces to 2 or 3-pixel thick ridge line and valley line. • Ridges are treated as foreground and valleys and slopes are treated as background. • Standard thinning techniques on this binary image.

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