Optimizing computer vision through adaptive thresholdingbased image segmentation

Abstract

Image segmentation separates foreground structures from background so that later analysis can use a simpler representation. A single global threshold is efficient, but uneven lighting and low contrast can make one cutoff unsuitable across an image. This focused review examines adaptive thresholding, which estimates a threshold from a local neighborhood for each pixel. It compares mean- and Gaussian-based approaches with variance-aware and integral-image variants; describes preprocessing, window and offset selection, and post-processing; and sets out an evaluation protocol for document, biomedical, and inspection images. The source paper did not supply a dataset, ground-truth masks, measured results, or a reproducible experiment. Accordingly, this review reports methodological comparisons and expected trade-offs rather than claiming numerical performance. Adaptive thresholding is most useful when foreground/background separation is primarily an intensity problem with spatially varying illumination; it is less reliable when texture, noise, and foreground intensities overlap locally.