Abstract
The aim of this paper is to segment an object from a texture-cluttered image. Segmentation is achieved by extracting the local information of image and embedding it with active contour model based on region. Images with inhomogenous intensity can be segmented using this model by extracting the local information of image. The level set function [1] can be smoothened by introducing the Gaussian filtering to the current model and the need for resetting the contour for every iteration can be eliminated. Evaluation results showed that the results obtained from the proposed method is similar to the results obtained from LBF [2] (local binary fitting) energy model, but the proposed method is found to be more efficient in terms of computational aspect. Moreover, the method maintains the sub-pixel reliability and boundary fixing properties. The approach is presented with metrics of visual similarity and could be further extended with quantitative metrics.
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Sreelakshmi, S., Vijai, A., Senthilkumar, T. (2016). Detection and Segmentation of Cluttered Objects from Texture Cluttered Scene. In: Suresh, L., Panigrahi, B. (eds) Proceedings of the International Conference on Soft Computing Systems. Advances in Intelligent Systems and Computing, vol 398. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2674-1_25
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DOI: https://doi.org/10.1007/978-81-322-2674-1_25
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