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The first step is a preliminarily image classification at a fine segmentation level to generate vegetation and shadow information.
The second step models the directional relationship between building and shadow objects to extract building information at the optimal segmentation level.
The experimental study is performed on historical documents, both handwritten and printed, at word or line segmentation level and with a limited or extended set of queries.
Finally, when there are important word segmentation errors or when we are considering line segmentation level, Continuous Dynamic Programming (CDP) seems to be the best choice.
To demonstrate its performance, the proposed classification is compared at the optimal segmentation level with Maximum Likelihood Classification and Support Vector Machine classification.
We found that the segmentation level whose regression predictions had spatial dependence that most closely matched the spatial dependence of the field samples also had the strongest predicted-to-observed correlations.
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We developed and applied an object-based classification approach, working on two segmentation levels.
With the incorporation of a geostatistical interpolator to predict the value of regression residuals at unsampled locations, however, we achieved consistently strong correlations across many segmentation levels.
Therefore, this work focuses on integrating quality prediction for more efficient fusion at segmentation-level.
According to our knowledge, this paper is the first to present a quality-driven fusion at segmentation-level in iris recognition.
Finally, a conclusion of this work and outlook on future topics in segmentation-level iris fusion is given in Section 5.
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