DOI: 10.11648/j.ajcst.20260902.15 ISSN: 2640-012X

Texture-guided Adaptive HSV Segmentation for Constrained-environment Object Detection: A Classical Pipeline with Per-image Saturation Threshold Modulation

Emmanuel Obite, Anasuodei Moko, Kizzy Elliot
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Object detection and segmentation in constrained visual environments remains a difficult problem for classical computer vision pipelines. Fixed-parameter thresholds lose accuracy under variable illumination, a problem acute in environments like caves, where lighting is uneven, shadows are deep, and surface textures shift unpredictably. This paper introduces a texture-guided adaptive saturation thresholding framework for HSV-based binary segmentation, tested on a 51-image dataset of parachute canopy detection in a cave environment. The baseline is a seven-stage pipeline combining dual-channel HSV thresholding, morphological refinement, and largest connected component selection. It achieves a mean Dice Similarity Coefficient (DSC) of 0.901 with a standard deviation of 0.024, a strong result for constrained-scene segmentation. The core contribution is a per-image adaptive saturation threshold, which uses Local Binary Pattern (LBP) uniformity and Gray-Level Co-occurrence Matrix (GLCM) energy to read each image's texture before setting the threshold. LBP captures local micro-patterns while GLCM measures pixel-level co-occurrence statistics. Pairing them means the threshold adapts to image content rather than applying fixed assumptions across all frames, addressing the shadow-induced saturation reduction that the baseline fails to handle. A stage-by-stage ablation study isolates each pipeline component's contribution, making performance gains traceable rather than assumed. The adaptive formulation projects a mean DSC of 0.914 with a standard deviation of 0.019 on the full test set, improving both accuracy and consistency over the baseline. Full MATLAB implementation is publicly available. This matters because constrained-environment datasets are rare, and reproducibility gives other researchers a shared starting point for binary segmentation comparisons.

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