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BACKGROUD ESTIMATION

BACKGROUD ESTIMATION. PRESENTED BY VISWANATH HARSHA VARDHAN hviswana@kent.edu. INDEX. Problem Statement Proposed Methods Results Conclusion References. PROBLEM STATEMENT.

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BACKGROUD ESTIMATION

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  1. BACKGROUD ESTIMATION PRESENTED BY VISWANATH HARSHA VARDHAN hviswana@kent.edu

  2. INDEX • Problem Statement • Proposed Methods • Results • Conclusion • References

  3. PROBLEM STATEMENT • Given an area of an image, the color of that particular area is filled in such a way that it matches the background color.

  4. PROPOSED METHODS • Polynomial Smoothing. • Background Estimation.

  5. Polynomial Smoothing • Smoothing is a process by which signals are weighted within a local neighborhood window. • For a series of signals [s1,s2,...,sn], the new series of signals [f1, f2,..., fn] after the smoothing can be represented as follow fk = ∑(-n<i<n)wisk+I where wi denotes the weights n denotes the size of the local neighborhood window. Therefore, the smoothed signal fk is actually a weighted combination of the original signal sk and its neighbors within a neighborhood window.

  6. Background Estimation • First, we estimate the document background surface through one-dimensional polynomial smoothing that is usually much faster (up to ten times) and also more accurate than the two-dimensional polynomial smoothing. • Second, we perform the global polynomial smoothing, which fits a smoothing polynomial to the image pixels within each whole document row/column and therefore requires no pre- detection of the text regions.

  7. Cont… • Third, we perform the polynomial smoothing iteratively that updates the polynomial order and the data points adaptively after each round of smoothing.

  8. Cont.. • In the proposed polynomial smoothing, a set of equidis- tant pixels are first sampled from a document row/column. The signal at each sampling pixel is estimated by the median intensity of the document image pixels within a local one- dimensional neighborhood window. The initial smoothing setup can be specified as follows:

  9. … • xi = ks ×i Si = fmdn([I(x frnd(i−ks)), . . . ,I(x frnd(i+ks))]), i = 1,...,N where functions fmdn(·) and frnd(·) denote a median and a rounding functions, respectively. • xiand si refer to the position of the i-th sampling pixel and the sampled image intensity at that sampling pixel. • Parameter ks denotes the sampling step

  10. … • The background surface of the document row/column under study can thus be estimated through an iterative poly- nomial smoothing procedure specified in Algorithm • We set the initial polynomial order doat 6 based on the observation that the polynomial of order 6 in the initial iteration is usually sufficientto track the image variation within the document background. Furthermore, we increase the polynomial order adaptively (after each smoothing iteration)as follows to estimate the document background surface accurately: dn= do + frnd(kt·n)

  11. ALGORITHM • We implement the polynomial smoothing in a different way. First, we estimate the document background surface. Polynomial smoothing of one row/column of a image • Require: One row/column document image pixels • Ensure: A smoothing polynomial of the background of the document image • row/column under study • 1: Sample the image data from the document row/column under study as specified. • 2: Fit a smoothing polynomial of the initial order d0to the sampled image data. • 3: Evaluate the maximum fitting error between the sampled data and the fitted smoothing polynomial. Remove the sampling point with the maximum fitting error if the maximum fitting error is larger than a pre-defined threshold . • 4: Refit a smoothing polynomial of a higher order dnto the remaining data points; • 5: Repeat the previous two steps iteratively until the maximum fittingerror is smaller than the pre-defined threshold or the number of the remaining data points is smaller than dn. • 6: Return The final smoothing polynomial

  12. RESULTS

  13. Conclusion • The selected area is filled in such a way, that it matches the background. • The proposed methods are implemented using the MATLAB tool.

  14. REFERENCES • Leedham, G., Yan, C., Takru, K., Tan, J.H.N., Mian, L.:Comparisonof some thresholding algorithms for text/backgroundsegmentationin difficult document images. Int Conf Doc Anal.Recogn. 2, 859–864 (2003). • Lu, S., Tan, C.L.: Binarization of badly illuminated document images through shading estimation and compensation. Int. Conf.Doc. Anal. Recogn. 1, 312–316 (2007). • Kittler, J., Illingworth, J.: On threshold selection using clustering criteria. IEEE Trans. Syst. Man Cybern. 15, 652–655 (1985)

  15. . THANK YOU

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