Back-to-front interference is a common problem in documents, printed on translucent pages with insufficient opacity and
is referred to as bleed through. The present state-of-art algorithms address bleed through based on entropy, entropic
correlation and discriminator analysis. However, a common drawback of such algorithms is their inefficient
processing of documents that are either sparse in terms of content or have a very dark background. Our proposed
algorithm, based on Otsu's binarization method and pixel level classification addresses these problems. Experiments
indicate that our algorithm performs comparable to state-of-the-art for most of the images and better than state-of-the-art
for the low contrast images.
When taking pictures, professional photographers apply photographic composition rules, e.g. rule of thirds. The rule of thirds says to place the main subject's center at one of four places: at 1/3 or 2/3 of the picture width from left edge, and 1/3 or 2/3 of the picture height from the top edge. This paper develops low-complexity unsupervised methods for digital still cameras to (1) segment the main subject and (2) realize the rule-of-thirds.
The main subject segmentation method uses the auto-focus filter, opens the shutter aperture fully, and segments the resulting image. These camera settings place the main subject in focus and blur the rest of the image by diffused light. The segmentation utilizes the difference in frequency content between the main subject and blurred background. The segmentation does not depend on prior knowledge of the indoor/outdoor setting or scene content.
The rule-of-thirds method moves the centroid of the main subject to the closest of the four rule-of-thirds locations. We first define an objective function that measures how close the main subject placement obeys the rule-of-thirds, and then reposition the main subject in order to optimize the objective function. For multiple main subjects, the proposed algorithm could be extended to use rule-of-triangles by adding an appropriate constraint.