Trends in Opto-electro & Optical Communication Original Research
Self-similarities in Hilbert Envelope Energy Regions on Motion Waveforms Application in Detecting Motion Irregularities in Video Frames
Abstract
This paper introduces self-similar Hilbert envelope energy regions that are commonly found in oscillating spectral curves. The time-varying amplitudes and peak value frequencies in typical spectral curves reflect the response of a system to external stimuli. A Hilbert envelope is a smooth curve connected between spectral curve peak values. Each peak value on an envelope curve at time identifies an envelope-bounded region with interior area (called Hilbert envelope energy region (denoted by)) which provides a measure of the size of a system response to a time-varying stimulus. A planar curve tangent to the peak values of an oscillating waveform is called a Hilbert envelope. Such an envelope is a series of pathways that extend between a nonlinear waveform's maximum values. In other words, a Hilbert envelope provides a ‘Fingerprint’ of the spectral flow of an oscillating waveform in as much as an envelope is tangent to every maximal value of the waveform. This paper includes an application of Hilbert energy envelope regions in tracking the self-similarities of either a walker or runner motion recorded in infrared videos.
Keywords
References (17)
- Kaiser JF. On a simple algorithm to calculate the 'energy' of a signal. International Conference on Acoustics, Speech, and Signal Processing. 381-384. doi:10.1109/icassp.1990.115702
- Cui E, Peters JF. Self-similarities in optical flows. Chaos, Solitons & Fractals. 2022;164:112722. doi:10.1016/j.chaos.2022.112722
- ANDRADE A, KYBERD P, NASUTO S. The application of the Hilbert spectrum to the analysis of electromyographic signals. Information Sciences. 2008;178(9):2176-2193. doi:10.1016/j.ins.2007.12.013
- Feldman M, Braun S. Nonlinear vibrating system identification via Hilbert decomposition. Mechanical Systems and Signal Processing. 2017;84:65-96. doi:10.1016/j.ymssp.2016.03.015
- Feldman M. Hilbert Transform Applications in Mechanical Vibration. 2011. doi:10.1002/9781119991656
- Al-Ahmari A, Sinha JK, Asnaashari E. A Comparison Between Hilbert Transform and a New Method for Signal Enveloping. Mechanisms and Machine Science. 2014:163-172. doi:10.1007/978-3-319-09918-7_14
- Kim AH, Kim IH. Essential spectra of quasisimilar (p,k)-quasihyponormal operators. Journal of Inequalities and Applications. 2006;2006:1-7. doi:10.1155/jia/2006/72641
- Krantz S, Rosen KH, Zwillinger D. Standard Mathematical Tables and Formulae. Boca Raton (FL): Chapman & Hall/CRC Press; 2003.
- Oppenheim AV, Willsky AS, Nawab SH. Signals & Systems. 2nd ed. Upper Saddle River (NJ): Prentice Hall; 2003.
- Kido K. Digital Fourier Analysis: Advanced Techniques. 2015. doi:10.1007/978-1-4939-1127-1
- Boudraa AO, Salzenstein F. Teager–Kaiser energy methods for signal and image analysis: A review. Digital Signal Processing. 2018;78:338-375. doi:10.1016/j.dsp.2018.03.010
- Råde L, Westergren B. Mathematics Handbook for Science and Engineering. 2004. doi:10.1007/978-3-662-08549-3
- Lathi BP, Green RA. Linear systems and signals. New York: Oxford University Press; 2005.
- Haider MS, Peters JF. Temporal proximities: Self-similar temporally close shapes. Chaos, Solitons & Fractals. 2021;151:111237. doi:10.1016/j.chaos.2021.111237
- Deng H, Liu J, Li H. EMD Based Infrared Image Target Detection Method. Journal of Infrared, Millimeter, and Terahertz Waves. 2009;30(11):1205-1215. doi:10.1007/s10762-009-9548-9
- Ulrich T. Envelope calculation from the Hilbert transform. Los Alamos National Laboratory, Los Alamos, NM, USA; 2006.
- Weisstein EW. Self-similarity. MathWorld--A Wolfram Web Resource. 2024. Available from: https://mathworld.wolfram.com/Self-Similarity.html.