Abstract
Image segmentation is the area of research to study the number of homogenous regions present in the image and to analyze the objects present in the image. The set of pixels belong to each object present in the image can be assigned same gray level to visualize certain characteristics. In this article, Particle Swarm Optimizer(PSO) based context sensitive thresholding technique has been presented to detect optimal thresholds present in the image automatically. The main objective behind utilization of the PSO is to demonstrate its effectiveness when applied to context sensitive thresholding technique to determine optimal thresholds of the image to be segmented. Further the results are compared with the two state-of-art thresholding techniques for image segmentation cited in literature. The achieved improvements are validated in terms of quantitative and qualitative parameters on the large dataset of images.
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Singla, A., Patra, S. (2017). PSO Based Context Sensitive Thresholding Technique for Automatic Image Segmentation. In: Deep, K., et al. Proceedings of Sixth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 547. Springer, Singapore. https://doi.org/10.1007/978-981-10-3325-4_15
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DOI: https://doi.org/10.1007/978-981-10-3325-4_15
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