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Y.: Quantitative analysis of pictorial color image difference (1998)

by R Chung, Shimamura
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Robust Image Segmentation in Low Depth Of Field Images

by Franz Graf, Hans-peter Kriegel, Michael Weiler , 2013
"... In photography, low depth of field (DOF) is an im-portant technique to emphasize the object of interest (OOI) within an image. Thus, low DOF images are widely used in the application area of macro, portrait or sports photography. When viewing a low DOF image, the viewer implicitly concentrates on th ..."
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In photography, low depth of field (DOF) is an im-portant technique to emphasize the object of interest (OOI) within an image. Thus, low DOF images are widely used in the application area of macro, portrait or sports photography. When viewing a low DOF image, the viewer implicitly concentrates on the re-gions that are sharper regions of the image and thus segments the image into regions of interest and non regions of interest which has a major impact on the perception of the image. Thus, a robust algorithm for the fully automatic detection of the OOI in low DOF images provides valuable information for subsequent image processing and image retrieval. In this paper we propose a robust and parameterless algorithm for the fully automatic segmentation of low DOF images. We compare our method with three similar methods and show the superior robustness even though our algorithm does not require any parameters to be set by hand. The experiments are conducted on a real world data set with high and low DOF images. 1
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...l color changes between I ′(x, y) and I(x, y) equally where ∆E > 16. This can be justified by human perception, which recognizes two colors u, v to as rather unsimilar to each other if ∆E∗(u, v) > 12 =-=[3]-=-. Thus it can be said, that a ∆E∗ > 16 indicates a significant color change which is also a strong indication for an edge. Afterwards, all edge pixels with a score value greater than the threshold Θsc...

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