AI RESEARCH
Image Thresholding: Understanding Bias of Evaluation Metrics towards Specific Evaluation Functions
arXiv CS.CV
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ArXi:2605.27132v1 Announce Type: new Multilevel image thresholding is widely used for segmentation in applications ranging from medical imaging to remote sensing. Classical objective functions, such as Otsu's between-class variance and Kapur's entropy, are often optimized using metaheuristic algorithms, with performance evaluated via metrics like Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR). These evaluations implicitly assume that SSIM and PSNR provide unbiased measures of segmentation quality.