Enhanced Global Image Segmentation: Addressing Pixel Inhomogeneity and Noise with Average Convolution and Entropy-Based Local Factor
Journal Title: International Journal of Knowledge and Innovation Studies - Year 2023, Vol 1, Issue 2
Abstract
In the field of computer vision and digital image processing, the division of images into meaningful segments is a pivotal task. This paper introduces an innovative global image segmentation model, distinguished for its ability to segment pixels with intensity inhomogeneity and robustly handle noise. The proposed model leverages a combination of randomness measurement and spatial techniques to accurately segment regions within and outside contours in challenging conditions. Its efficacy is demonstrated through rigorous testing with images from the Berkeley image database. The results significantly surpass existing methods, particularly in the context of noisy and intensity inhomogeneous images. The model's proficiency lies in its unique ability to differentiate between minute, yet crucial, details and outliers, thus enhancing the precision of global segmentation in complex scenarios. This advancement is particularly relevant for images plagued by unknown noise distributions, overcoming limitations such as the inadequate handling of convex images at local minima and the segmentation of images corrupted by additive and multiplicative noise. The model's design integrates a region-based active contour method, refined through the incorporation of a local similarity factor, level set method, partial differential equations, and entropy considerations. This approach not only addresses the technical challenges posed by image segmentation but also sets a new benchmark for accuracy and reliability in the field.
Authors and Affiliations
Ibrar Hussain, Jan Muhammad, Rifaqat Ali
Keywords
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- EP ID EP732607
- DOI https://doi.org/10.56578/ijkis010204
- Views 112
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