A detailed discussion of the ROI detection algorithm can be found here, with examples:
This is an implementation of the algorithm described in our paper . The input is any map generated by saliency detection algorithms like Itti-Koch  or GBVS . The algorithm outputs a binary mask without requiring a threshold for the saliency map. More details about it are described in our paper.
Please cite our paper if you find it useful.
 Bharath, Ramesh, et al. "Scalable scene understanding using saliency-guided object localization." Control and Automation (ICCA), 2013, 10th IEEE International Conference on. IEEE, 2013.
 Itti, Laurent, Christof Koch, and Ernst Niebur. "A model of saliency-based visual attention for rapid scene analysis." Pattern Analysis and Machine Intelligence, IEEE Transactions on 20.11 (1998): 1254-1259.
 Harel, Jonathan, Christof Koch, and Pietro Perona. "Graph-based visual saliency." Advances in neural information processing systems. 2006.
Bharath Ramesh (2021). ROI selection for saliency maps (https://www.mathworks.com/matlabcentral/fileexchange/43558-roi-selection-for-saliency-maps), MATLAB Central File Exchange. Retrieved .
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