Updated 17 Aug 2015
This package provides the implementation of the locally debiased region contrast saliency algorithm. Furthermore, it also provides the original region contrast algorithm and allows to set different optional center bias integration schemes (min, max, linear, product).
Locally debiased region contrast saliency has the advantage that it is not center biased, which makes it a perfect candidate as a salient object detection algorithm for, e.g., surveillance footage or robots (i.e., image data that was not collected/generated by a photographer). However, you can also bias the algorithm again, in which case the algorithm also provides state-of-the-art performance on Achanta's salient object detection dataset.
If you use any of this work in scientific research or as part of a larger software system, you are kindly requested to cite the use in any related publications or technical documentation. The work is based upon:
B. Schauerte, R. Stiefelhagen, "How the Distribution of Salient Objects
in Images Influences Salient Object Detection". In Proceedings of the
20th International Conference on Image Processing (ICIP), 2013.
Furthermore, it has been applied in:
B. Schauerte, R. Stiefelhagen, "Look at this! Learning to Guide Visual Saliency in Human-Robot Interaction". In Proceedings of the International Conference on Intelligent Robots and Systems (IROS), 2014.
Boris Schauerte (2020). Region Contrast Saliency (https://github.com/bschauerte/region_contrast_saliency), GitHub. Retrieved .
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