ABSTRACT
This paper compares and analyzes different Segmentation techniques that are used in pattern analysis and machine intelligence. Comparing motion segmentation, script-independent text line segmentation in freestyle handwritten documents and combined top-down and bottom up segmentation based on density estimation and state of the art image segmentation technique. It presents experimental results and quantitative evaluation, demonstrating the resulting approach is effective for very challenging data. The main novel aspects of this work are the fragment learning phase, which efficiently learns the figure-ground labeling of segmentation fragments, even in training sets with high object and background variability; combining the resulting top-down segmentation with bottom-up criteria; and the use of segmentation to improve recognition.
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Index Terms
- Comparison and analysis of segmentation techniques in pattern analysis and machine intelligence
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