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Hybrid image processing model: a base for smart emergency applications

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Abstract

Image processing has led its applications to scale to almost all areas encompassing the emergent interdisciplinary fields of computers, electronics, mechanical, civil, and more. There are several discrete models in image processing to identify characteristic of an object under surveillance. In smart emergency applications, accuracy and precision on attributes of these objects are paramount. Hence there is a need to enhance the image processing algorithms used to measure an object’s distance, size, and color from any altitude. The paper demonstrates Hybrid Image Processing Model (HIPM) using Triangle similarity, Pixel Per Metric (PPM), CIELAB color space, and Douglas-Peucker algorithm to compute the distance of an object from the camera, the size, color, and shape of an object from the image, respectively. This work emphasises on leveraging image processing techniques for assisting emergency aircraft landing. Results were obtained with five real-time image sets, each consisting of 50 images, and proved HIPM is efficient and reliable, with an accuracy of 99.84% and a mean error rate of 0.08. This work also discusses the model’s capability to function in accordance with the need of autonomous vehicles and military events.

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Notes

  1. https://solutions4ga.com/runway-lights-at-airport-colors-and-meaning-explained/.

  2. Appendix-A.

  3. https://www.pexels.com/.

  4. https://www.bosch-pt.co.in/in/en/products/glm-500-0601072HF0.

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  1. Department of Computer Science, Indian Institute of Information Technology Kottayam (IIITK), Kottayam, Kerala, India

    Gunish Gunish, Sheema Madhusudhanan & Arun Cyril Jose

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Gunish Gunish - Ideation, and algorithimic analysis. Sheema Madhusudhanan - data analysis, experimentation, and manuscript writing. Arun Cyril Jose - guidance and review.

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Correspondence to Sheema Madhusudhanan.

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Gunish, G., Madhusudhanan, S. & Jose, A.C. Hybrid image processing model: a base for smart emergency applications. J Supercomput 79, 13119–13141 (2023). https://doi.org/10.1007/s11227-023-05174-7

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