Abstract
Anomaly detection is useful in diverse domains including fault detection system, health monitoring, intrusion detection, fraud detection, emotion recognition, cancer detection, animal rescue, detecting ecosystem disturbances, and event detection in sensor networks. Thermal image is a widely used night vision technology. Anomaly detection using thermal image features has been proposed in this work. Three major classes of features, namely textural features, color features, and shape features, have been extracted. Correlation model has been used for detecting anomalies. Thermal image of perishable objects has been analyzed, and the evaluation result confirms the hypothesis. It is found that using a set of features while using correlation as similarity measure the achieved average recall is 76.06%.
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References
Ansari S, Salankar S (2017) An overview on thermal image processing. In: Proc. 2nd International conference research in intelligent and computing in engineering, 117–120
Bhartee AK, Srivastava KM, Sharma T, Scholar UG, GCET GN (2017) Object identification using thermal image processing. Int J Eng Sci 11400
Mohamed MB, Makhlouf AM, Fakhfakh A (2018) Correlation for efficient anomaly detection in medical environment. In: 2018 14th International wireless communications & mobile computing conference (IWCMC), IEEE, 548–553
Selvathi D, Hannah Nithilla I, Akshaya N (2019) Image processing techniques for defect detection in metals using thermal images. In: 2019 3rd International conference on trends in electronics and informatics (ICOEI), IEEE, 939–944
Soliman OO, Sweilam NH, Shawky DM (2018) Automatic breast cancer detection using digital thermal images. In: 2018 9th Cairo International biomedical engineering conference (CIBEC), IEEE, 110–113
Pedraza ILA, Diaz JFA, Pinto RM, Becker M, Tronco ML (2019) Sweet citrus fruit detection in thermal images using fuzzy image processing. In: IEEE Colombian conference on applications in computational intelligence, Springer, Cham, 182–193
Guo T, Huynh CP, Solh M (2019) Domain-adaptive pedestrian detection in thermal images. In: 2019 IEEE International conference on image processing (ICIP), IEEE, 1660–1664
Sudha BG, Umadevi V, Shivaram JM (2017) Thermal image acquisition and segmentation of human foot. In: 2017 4th International conference on signal processing and integrated networks (SPIN), IEEE, 80–85
Vandone A (2011) Algorithms for infrared image processing. 9–29
Budzan S (2015) Human detection in thermal images using low-level features measurement automation monitoring. 61
Thirumurthy B, Parameswaran L, Vaiapury K (2018) Visual-based change detection in scene regions using statistical-based approaches. J Electron Imaging 27(5):051217
Huang Y, Han X, Tian X, Zhao Z, Zhao J, Hao D (2010) Texture analysis of ultrasonic liver images based on spatial domain methods. In: 2010 3rd International congress on image and signal processing, vol 2. IEEE, pp 562–565
Çevik T, Alshaykha AMA, Çevik N (2016) Performance analysis of GLCM-based classification on Wavelet Transform-compressed fingerprint images. In: 2016 Sixth international conference on digital information and communication technology and its applications (DICTAP), 131–135
Gonzales RC, Richard E (2002) Woods. Digital image process
Mudrova M, Procházka A (2005) Principal component analysis in image processing. In: Proceedings of the MATLAB technical computing conference, Prague
Afifi AJ, Ashour WM (2012) Image retrieval based on content using color feature. Int Scholarly Res Not
YenYen K, Yen EK, Johnston RG (1996) The ineffectiveness of the correlation coefficient for image comparisons
Parameswaran L (2013) A hybrid method for object identification and event detection in video. In: 2013 Fourth national conference on computer vision, pattern recognition, Image Processing and Graphics (NCVPRIPG), IEEE, 1–4
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Mishra, C., Bagyammal, T., Parameswaran, L. (2021). An Algorithm Design for Anomaly Detection in Thermal Images. In: Favorskaya, M.N., Mekhilef, S., Pandey, R.K., Singh, N. (eds) Innovations in Electrical and Electronic Engineering. Lecture Notes in Electrical Engineering, vol 661. Springer, Singapore. https://doi.org/10.1007/978-981-15-4692-1_49
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DOI: https://doi.org/10.1007/978-981-15-4692-1_49
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