Mere za estimaciju klatera na slici i performanse akvizicije cilja
Sažetak
Uvod/cilj: Određivanje performansi akvizicije cilja ima bitnu ulogu u vojnim primenama u kojima je čovek operater. Ovaj rad predstavlja prošireno istraživanje o primeni metrika klatera slike za analizu performansi akvizicije cilja, kako bi se primenom objektivnih mera izvršila predikcija verovatnoće detekcije, verovatnoće lažnog alarma i srednjeg vremena traženja cilja na slici.
Metode: Za određivanje stepena klatera korišćena su jednostavna obeležja na globalnom (nivo kompletne slike) i lokalnom nivou (u okolini cilja), metrike klatera zasnovane na kontrastu, veličina cilja i objektivne mere izvedene iz mera za procenu kvaliteta slike. Pored standardnih obeležja, korišćena su i obeležja izvedena iz raspodele MSCN (mean subtracted contrast normalized coefficients) koeficijenata. Za poređenje rezultata objektivnih skorova i eksperimentalnih rezultata dobijenih na javno dostupnoj Search_2 bazi, korišćeni su regresioni zakoni prihvaćeni u literaturi. Kao kvantitativne mere slaganja korišćene su linearna korelacija i korelacije rangova.
Rezultati: Pokazano je da se primenom metrika klatera, izvedenih iz mera procene kvaliteta slike, dobija najbolje slaganje sa pokazateljima akvizicije cilja. Korelacija sa rezultatima subjektivnih testova iznosi do 90%, što ukazuje na potrebu za daljim istraživanjima. Poseban doprinos rada predstavlja detaljna analiza predikcije performansi akvizicije cilja primenom jednostavnih obeležja na globalnom i lokalnom nivou, pri čemu je pokazano da se određivanjem obeležja u okolini cilja mogu poboljšati performanse predikcije. Takođe, rezultati subjektivnih testova pokazuju da se sa verovatnoćom većom od 90% na osnovu srednjeg vremena traženja cilja na slici može proceniti verovatnoća lažnog alarma i verovatnoća detekcije cilja.
Zaključak: Pored toga što je dobijen visok stepen slaganja objektivnih metrika klatera i rezultata subjektivnih testova (do 90%), postoji potreba za unapređenjem postojećih i razvojem novih metrika, kao i za sprovođenjem novih subjektivnih testova.
Reference
Bondžulić, B., Pavlović, B., Stojanović, N. & Petrović, V. 2022. Picture-wise just noticeable difference prediction model for JPEG image quality assessment. Vojnotehnički glasnik / Military Technical Courier, 70(1), pp.62-86. Available at: https://doi.org/10.5937/vojtehg70-34739.
Canny, J. 1986. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6), pp.679-698. Available at: https://doi.org/10.1109/TPAMI.1986.4767851.
Chang, H. & Zhang, J. 2006a. New metrics for clutter affecting human target acquisition. IEEE Transactions on Aerospace and Electronic Systems, 42(1), pp.361-368. Available at: https://doi.org/10.1109/TAES.2006.1603429.
Chang, H. & Zhang, J. 2006b. Evaluation of human detection performance using target structure similarity clutter metrics. Optical Engineering, 45(9), art.number:096404. Available at: https://doi.org/10.1117/1.2353848.
Chang, H., Zhang, J. & Liu, D. 2007. Modeling human false alarms using clutter metrics. In: Proceedings of International Symposium on Multispectral Image Processing and Pattern Recognition, MIPR 2007; Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, 67863N, Wuhan, China, 6786, pp.1-8, November 15-17. Available at: https://doi.org/10.1117/12.750260.
Chang, H., Zhang, J., Liu, X., Yang, C. & Li, Q. 2010. Color image clutter metrics for predicting human target acquisition performance. In: 2010 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM), Chengdu, China, pp.1-4, September 23-25. Available at: https://doi.org/10.1109/WICOM.2010.5600622.
Cheng, X. & Li, Z. 2021. Predicting the lossless compression ratio of remote sensing images with configurational entropy. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, pp.11936-11953. Available at: https://doi.org/10.1109/JSTARS.2021.3123650.
Chu, X., Yang, C. & Li. Q. 2012. Contrast-sensitivity-function-based clutter metric. Optical Engineering, 51(6), art.number:067003. Available at: https://doi.org/10.1117/1.OE.51.6.067003.
Corchs, S.E., Ciocca, G., Bricolo, E. & Gasparini, F. 2016. Predicting complexity perception of real world images. PLoS ONE, 11(6), e0157986. Available at: https://doi.org/10.1371/journal.pone.0157986.
Culpepper, J.B. 2015. Texture metric that predicts target detection performance. Optical Engineering, 54(12), art.number:123101. Available at: https://doi.org/10.1117/1.OE.54.12.123101.
Gavrovska, A. & Samčović, A. 2018. Challenges in modeling of visual human map attention. In: Proceedings of the 36th Symposium on Novel Technologies in Postal and Telecommunication Traffic PosTel 2018, Belgrade, Serbia, December 4-5, pp.256-264 [online]. Available at: https://postel.sf.bg.ac.rs/simpozijumi/POSTEL2018/RADOVI%20PDF/Telekomunikacioni%20saobracaj,%20mreze%20i%20servisi/13.GavrovskaSamcovic.pdf (in Serbian) [Accessed: 20 April 2023].
Hasler, D. & Suesstrunk, S.E. 2003. Measuring colourfulness in natural images. In: Proceedings of Electronic Imaging 2003; Human Vision and Electronic Imaging VIII, Santa Clara, CA, USA, 5007, pp.87-95. Available at: https://doi.org/10.1117/12.477378.
Itti, L., Gold, C. & Koch, C. 2001. Visual attention and target detection in cluttered natural scenes. Optical Engineering, 40(9), pp.1784-1793. Available at: https://doi.org/10.1117/1.1389063.
Li, Q., Yang, C. & Zhang, J.-Q. 2012. Target acquisition performance in a cluttered environment. Applied Optics, 51(31), pp.7668-7673. Available at: https://doi.org/10.1364/AO.51.007668.
Lukin, V., Bataeva, E. & Abramov, S. 2023. Saliency map in image visual quality assessment and processing. Radioelectronic and Computer Systems, 1(105), pp.112-121. Available at: https://doi.org/10.32620/reks.2023.1.09.
Meehan, A.J. & Culpepper, J.B. 2016. Clutter estimation and perception. Optical Engineering, 55(11), art.number:113106. Available at: https://doi.org/10.1117/1.OE.55.11.113106.
Mittal, A., Moorty, A.K. & Bovik, A.C. 2012. No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing, 21(12), pp.4695-4708. Available at: https://doi.org/10.1109/TIP.2012.2214050.
Mondal, A. 2022. Camouflage design, assessment and breaking techniques: a survey. Multimedia Systems, 28, pp.141-160. https://doi.org/10.1007/s00530-021-00813-6.
Rotman, S.R., Cohen-Nov, A., Shamay, D., Hsu, D. & Kowalczyk, M.L. 1996. Textural metrics for clutter affecting human target acquisition. In: Proceedings of Aerospace/Defense Sensing and Controls; Infrared Imaging Systems: Design, Analysis, Modeling, and Testing VII, Orlando, FL, USA, 2743, pp.99-112. Available at: https://doi.org/10.1117/12.241951.
Schmieder, D.E. & Weathersby, M.R. 1983. Detection performance in clutter with variable resolution. IEEE Transactions on Aerospace and Electronic Systems, AES-19(4), pp.622-630. Available at: https://doi.org/10.1109/TAES.1983.309351.
Tan, W., Zhou, H.-x., Yu, Y., Du, J., Qin, H., Ma, Z. & Zheng, R. 2017. Multi-focus image fusion using spatial frequency and discrete wavelet transform. In: Proceedings of Applied Optics and Photonics China (AOPC2017); Optical Sensing and Imaging Technology and Applications, 104624K, Beijing, China, 10462, pp.1-11. Available at: https://doi.org/10.1117/12.2285561.
Toet, A., Bijl, P. & Valeton, J.M. 2001. Image dataset for testing search and detection models. Optical Engineering, 40(9), pp.1760-1767. Available at: https://doi.org/10.1117/1.1388608.
Toet, A. 2010. Structural similarity determines search time and detection probability. Infrared Physics & Technology, 53(6), pp.464-468. Available at: https://doi.org/10.1016/j.infrared.2010.09.003.
Toet, A. & Hogervorst, M.A. 2020. Review of camouflage assessment techniques. In: Proceedings of SPIE Security + Defence; Target and Background Signatures VI; 1153604, Online Only, 11536, pp.1-29. Available at: https://doi.org/10.1117/12.2566183.
Wang, Z., Bovik, A.C., Sheikh, H.R. & Simoncelli, E.P. 2004. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), pp.600-612. Available at: https://doi.org/10.1109/TIP.2003.819861.
Wilson, D. 2001. Image-based contrast-to-clutter modeling of detection. Optical Engineering, 40(9), pp.1852-1857. Available at: https://doi.org/10.1117/1.1389502.
Xiao, C.-m., Shi, Z.-l. & Liu, Y.-p. 2015a. Metrics of image clutter by introducing gradient features. Optics and Precision Engineering, 12, pp.3472-3479 [online]. Available at: http://caod.oriprobe.com/articles/47854327/Metrics_of_image_background_clutter_by_introducing_gradient_features.htm [Accessed: 20 April 2023].
Xiao, B., Duan, J., Zhu, Y., Chen, Y. & Li, G. 2015b. Survey of evaluation methods in image complexity of target and background. In: Proceedings of Applied Optics and Photonics China (AOPC2015); Image Processing and Analysis; 96751Q, Beijing, China, 9675, pp.1-6. Available at: https://doi.org/10.1117/12.2199533.
Xu, D. & Shi, Z. 2012. FD: A feature difference based image clutter metric for targeting performance. Infrared Physics & Technology, 55(6), pp.499-504. Available at: https://doi.org/10.1016/j.infrared.2012.08.001.
Xu, D., Shi, Z. & Luo, H. 2013. A structural difference based image clutter metric with brain cognitive model constraints. Infrared Physics & Technology, 57, pp.28-35. Available at: https://doi.org/10.1016/j.infrared.2012.11.005.
Xu, D. & Shi, Z. 2013. DSIM: A dissimilarity-based image clutter metric for targeting performance. IEEE Transactions on Image Processing, 22(10), pp.4108-4122. Available at: https://doi.org/10.1109/TIP.2013.2270112.
Yang, C., Zhang. J.-Q., Xu, X., Chang, H.-H. & He, G.-J. 2007. Quaternion phase-correlation-based clutter metric for color images. Optical Engineering, 46(12), art.number:127008. Available at: https://doi.org/10.1117/1.2823489.
Yang, C., Wu, J., Li, Q. & Zhang, J.-Q. 2011. Sparse-representation-based clutter metric. Applied Optics, 50(11), pp.1601-1605. Available at: https://doi.org/10.1364/AO.50.001601.
Yu, H. & Winkler, S. 2013. Image complexity and spatial information. In: 2013 Fifth International Workshop on Quality of Multimedia Experience (QoMEX), Klagenfurt am Worthersee, Austria, pp.12-17, July 3-5. Available at: https://doi.org/10.1109/QoMEX.2013.6603194.
Zhao, Y., Song, Y., Sulaman, M., Li, X., Guo, Z., Yang, X. & Wang, F. 2019. A multidirectional-difference-Hash-based image clutter metric for targeting performance. IEEE Photonics Journal, 11(4), art.number:7801110, pp.1-10. Available at: https://doi.org/10.1109/JPHOT.2019.2922967.
Zheng, B., Wang, X.-D., Huang, J.-T., Wang, J. & Jiang, Y. 2016. Selective visual attention based clutter metric with human visual system adaptability. Applied Optics, 55(27), pp.7700-7706. Available at: https://doi.org/10.1364/AO.55.007700.
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