Noise-Resilient Region Extraction in Images Through Dynamic Pixel Classification

Abstract

Region extraction is a fundamental task in image analysis, serving as a prerequisite for higher-level processes such as object recognition, tracking, and scene understanding. However, the presence of noise, illumination variations, and complex backgrounds significantly degrades the performance of traditional segmentation techniques. Static pixel classification methods often rely on fixed thresholds or global criteria, making them highly sensitive to noise and intensity fluctuations. To address these challenges, this paper proposes a noise-resilient region extraction framework based on dynamic pixel classification. The proposed approach adaptively classifies pixels by incorporating local intensity statistics, spatial context, and neighborhood consistency, enabling robust region extraction even in noisy environments. Noise suppression is integrated into the classification process rather than treated as a separate preprocessing step. The dynamic nature of pixel classification allows the method to adjust decision boundaries in response to local image characteristics. Experimental evaluations on benchmark image datasets demonstrate that the proposed method achieves superior robustness to noise and improved region extraction accuracy compared to conventional thresholding and clustering-based segmentation techniques. The results confirm the effectiveness of dynamic pixel classification for reliable image region extraction under challenging conditions.