Using Genetic Algorithms to Segment Images: A Review
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Abstract
The genetic algorithm plays a pivotal role in image processing, particularly in the critical stage of image segmentation. The process of segmenting photographs is an essential method in the field. Identifying objects, extracting features for object recognition, and classifying are integral components of image processing. However, the effectiveness of these activities relies on the quality of the operations performed. The work at hand in the domain of image processing is notably arduous and intricate. The segmentation of photos cannot be consistently achieved through the utilization of a singular approach. Nevertheless, it is not possible to consistently classify photos into extensive categories. The complexity inherent in the image segmentation task necessitates careful consideration when determining a suitable set of parameters to employ. The arduous task of selecting picture parameters the picture segmentation problem encompasses various factors that contribute to the complexity of the selection process. An optimization problem is employed to efficiently locate the global maximum inside a given search space, with the problem being formulated as a Genetic Algorithm. Subsequently, the task of determining the most suitable segmentation criteria for an image is successfully overcome. The primary objective of this study was to investigate the viability of employing genetic algorithms within the domain of image segmentation.
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