Segmentation of Dark Foreground Objects by Maximum Non–Extensive Entropy Partitioning Susan Seba1,*, Singh Rahul2, Kumar Amit3, Kumar Abhishek4, Kumar Ashwani5 1Associate Professor, Department of Information Technology, Delhi Technological University, New Delhi–110042, India 2Undergraduate B.Tech., Department of Information Technology, Delhi Technological University, New Delhi–110042, India 3Undergraduate B.Tech., Department of Information Technology, Delhi Technological University, New Delhi–110042, India 4Undergraduate B.Tech., Department of Information Technology, Delhi Technological University, New Delhi–110042, India 5Undergraduate B.Tech., Department of Information Technology, Delhi Technological University, New Delhi–110042, India *Corresponding author email id: seba_406@yahoo.in
Abstract In this paper, we investigate the segmentation of dark foreground objects in a relatively bright background clutter by maximum non-extensive entropy partitioning of the image histogram. The non-linear, non-extensive entropy with Gaussian gain, which is a regularity indicator, is embedded in the maximum entropy thresholding framework, in the background of the recent work in Susan et al. (2016. Sadhana 41: 1393) on segregating facial intensities related to emotions by iterative maximum entropy partitioning. The non-linearity of this entropy ensures that out of the two partitions of the image histogram, the darkest shades in the image approximate very finely a uniform probability distribution and are separated out effectively from the brighter shades in the image that coarsely approximate a uniform distribution. This definition of the point of maximum entropy brought about by the non-linearity of the Gaussian curve targets and segments out the darkest shades pertaining to the foreground object in a more efficient manner than other thresholding techniques. Comparisons with the existing entropic thresholding schemes on test instances from a benchmark object segmentation dataset confirm the utility and efficiency of our method. Top Keywords Image thresholding, Maximum entropy partitioning, Non-extensive entropy with Gaussian gain, Weighted sum of non-extensive entropies, Entropy-based image thresholding, Maximum non-extensive entropy partitioning, Foreground object detection. Top |