Browsing by Author "Kahandawala, B, S"

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    High-resolution optical imaging for sustainable fish freshness and safety assessment
    (Elsevier GmbH, 2026-07) Madhubhashini, M. N; Kahandawala, B, S; Sandaruwan, H.H.P. B; Silva, B. N; Wijenayake, U; Wijesinghe, R. U
    Fish freshness evaluation is crucial to ensure consumer safety, and rapid assessment is essential for effective and accurate quality control. To overcome the limitations of the gold standards, such as lack of structural depth information, high-time consumption, and labor-intensiveness, high-resolution Optical Coherence Tomography (OCT) was employed for real-time monitoring of fish freshness non-invasively. Microstructural changes of eye and skin of Indian Anchovies ( Stolephorus indicus ) specimens were considered as the main freshness parameters during refrigeration storage. Both eye and skin tissues exhibited decreased internal scattering, loss of clarity, boundary weakening, and gradual structural degradations through the OCT observations. The quantitatively assessed variance intensity, entropy, energy, and edge density clearly revealed the internal tissue disruption over storage time due to protein denaturation, oxidative damage, and fluid imbalance. The findings of this study indicate that OCT shows an insightful correlation with microbiological and biochemical spoilage processes, enabling the advanced identification of subtle microstructural changes in fish skin and eye, even at a prior stage of deterioration. Such capability offers an objective and rapid freshness evaluation approach that could greatly benefit supply chain management and post-harvest seafood quality monitoring.
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    PublicationOpen Access
    Vision incorporated MUltichannel Feature Fusion Template Matching (MUFF-TM) and real-time sub-pixel coordinate localization for 2D textile surface in ultrasonic tacking systems
    (Elsevier B.V., 2026-09) Kahandawala, B, S; Nalmi, R; Sodige, B, A.K; Subasinghage, K; Silva, B, N; Wijesinghe, R,E; Woo, S, T
    Temporary stitches are essential in apparel manufacturing as they temporarily secure fabric pieces to prevent misalignment during machine sewing and ensure high-quality results. Manual ultrasonic tacking machines were introduced to enhance the precision of this process; however, the necessity for expert operators remains a major constraint. This work introduces a real-time system for ultrasonic tacking machines that combines vision-guided single-modal MUltichannel Feature Fusion Template Matching (MUFF-TM) to autonomously identify and align tacking points on textiles with sub-pixel spatial accuracy. To overcome the limitations of classic feature-based algorithms on smooth and deformable fabrics, the proposed method utilizes macro-contour extraction and equidistant boundary sampling rather than relying on unstable local textures. Experimental results demonstrate that MUFF-TM achieves a 100% target detection rate with a highly stable Mean Absolute Error (MAE) of under 5 pixels across various dynamic conditions, including changes in orientation, illumination, scale, and non-rigid deformation. By significantly outperforming traditional algorithms (SIFT, SURF, and ORB) in spatial precision, the developed software interface offers a robust, versatile, and scalable solution for advancing automated precision in the apparel industry. Copyright