Enhancing Image Segmentation: A Novel Grow Cut Algorithm with Advanced Cellular Automata

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Raman Gaur
Toofan Mukherjee
Akash Dadhich


Image segmentation is a fundamental technique pivotal in a myriad of vision-related applications, yet the field lacks a universally accepted methodology for selecting and comparing segmentation algorithms. This absence of standardization can lead to inaccurate interpretations and unexpected results, underscoring the inherent challenges of image segmentation, which lacks a definitive meaning. In computer graphics, segmentation refers to the division of a pixel collection into subsets, a concept that aligns with other scholarly interpretations, albeit with debated criteria. This process echoes human cognitive behaviour, specifically pattern recognition, amplifying the complexity of segmentation challenges. Various methodologies characterize the landscape of image segmentation, where one prevalent approach involves text retrieval to generate localized feature sets. Object recognition in computer science, which entails the automatic classification of objects, intertwines deeply with image segmentation, enhancing the understanding of objects within images. The chosen segmentation algorithm critically influences the overall outcome, necessitating meticulous selection tailored to specific frameworks. Despite the availability of numerous segmentation techniques, their complexity often deters practical research applications. This research delves into an enhanced graph cut method for distinguishing foreground and background elements through image labelling and segmentation. This approach is scrutinized against performance metrics to evaluate the efficacy of the proposed algorithm in image segmentation. By methodically comparing results, this study aims to provide insights into the algorithm's effectiveness, contributing to the broader discourse on segmentation techniques and their applicability in various vision-related fields.


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Gaur, R. ., Mukherjee, T. ., & Dadhich, A. . (2023). Enhancing Image Segmentation: A Novel Grow Cut Algorithm with Advanced Cellular Automata. International Journal of New Practices in Management and Engineering, 12(3), 07–21. Retrieved from https://www.ijnpme.org/index.php/IJNPME/article/view/211


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