Search Results - (( motion extraction method algorithm ) OR ( motion estimation methods algorithm ))

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  1. 1
    Fast adaptive motion estimation search algorithm for H.264 encoder

    Fast adaptive motion estimation search algorithm for H.264 encoder by Patwary, Md Anwarul Kaium

    Published 2012
    “…Motion estimation is a technique of video compression and video processing applications; it extracts motion information from the video sequence. …”
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  2. 2
    Robust remote heart rate estimation from multiple asynchronous noisy channels using autoregressive model with Kalman filter

    Robust remote heart rate estimation from multiple asynchronous noisy channels using autoregressive model with Kalman filter by Nooralishahi, Parham, Loo, Chu Kiong, Shiung, Liew Wei

    Published 2019
    “…The results of three experiments demonstrate that our algorithm substantially outperforms all previous methods. …”
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  3. 3
    A new search and extraction technique for motion capture data

    A new search and extraction technique for motion capture data by Mohamad, Rafidei

    Published 2008
    “…Results from the experiments show that matching motion files were successfully extracted from the motion capture library using the new algorithm based on different human body segments.…”
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  4. 4
    Detection and Classification of Moving Objects for an Automated Surveillance System

    Detection and Classification of Moving Objects for an Automated Surveillance System by Md. Tomari, Mohd Razali

    Published 2006
    “…Moving object is detected by using combination of two frame differencing and adaptive image averaging with selectivity. Technically, this method estimate the motion area before updates the background by taking a weighted average of non-motion area of the current background altogether with non-motion area of the current frame of the video sequence. …”
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  5. 5
    Detection and classification of moving objects for an automated surveillance system

    Detection and classification of moving objects for an automated surveillance system by Md Tomari, Mohd Razali

    Published 2006
    “…Moving object is detected by using combination of two frame differencing and adaptive image averaging with selectivity. Technically, this method estimate the motion area before updates the background by taking a weighted average of non-motion area of the current background altogether with non-motion area of the current frame of the video sequence. …”
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  6. 6
    Detection and classification of moving objects for an automated surveillance system

    Detection and classification of moving objects for an automated surveillance system by Md Tomari, Mohd Razali

    Published 2006
    “…Moving object is detected by using combination of two frame differencing and adaptive image averaging with selectivity. Technically, this method estimate the motion area before updates the background by taking a weighted average of non-motion area of the current background altogether with non-motion area of the current frame of the video sequence. …”
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  7. 7
    Fuzzy qualitative approach to address uncertainty in human motion analysis / Lim Chern Hong

    Fuzzy qualitative approach to address uncertainty in human motion analysis / Lim Chern Hong by Lim, Chern Hong

    Published 2015
    “…In addition, dynamic fuzzy qualitative rank classifier is proposed as the extension to overcome the heuristic method in the learning step. In summary, the collective impact of the above contributions will constitute to achieve a more practical and feasible framework towards the human motion analysis applications. …”
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  8. 8
    Human upper body pose region estimation

    Human upper body pose region estimation by Bilal, Sara Mohammed Osman Saleh, Akmeliawati, Rini, Shafie, Amir Akramin, Salami, Momoh Jimoh Eyiomika

    Published 2013
    “…(b) Scaling the extracted parts during body orientation was attained using partial estimation of face size. …”
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  9. 9
    Adaptive template matching of photoplethysmogram pulses to detect motion artefact

    Adaptive template matching of photoplethysmogram pulses to detect motion artefact by Lim, Pooi Khoon, Ng, Siew Cheok, Lovell, Nigel Hamilton, Yu, Yong Poh, Tan, Maw Pin, McCombie, Devin, Lim, Einly, Redmond, Stephen James

    Published 2018
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  10. 10
    Model of Bayesian tangent eye shape for eye capture

    Model of Bayesian tangent eye shape for eye capture by Nsaef, Asama Kuder, Jaafar, Azizah, Sliman, Layth, Sulaiman, Riza, O. K. Rahmat, Rahmita Wirza

    Published 2014
    “…To overcome these difficulties one can enhance the image acquisition process. Obtaining a method in extracting quality of eye images automatically from the video stream is the main area of interest in this study. …”
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  11. 11
    The classification of wink-based eeg signals by means of transfer learning models

    The classification of wink-based eeg signals by means of transfer learning models by Jothi Letchumy, Mahendra Kumar

    Published 2021
    “…This study aimed to explore the performance of different pre-processing methods, namely Fast Fourier Transform, Short-Time Fourier Transform, Discrete Wavelet Transform, and Continuous Wavelet Transform (CWT) that could allow TL models to extract features from the images generated and classify through selected classical ML algorithms . …”
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  12. 12
    Multi-sensor fusion based on multiple classifier systems for human activity identification

    Multi-sensor fusion based on multiple classifier systems for human activity identification by Nweke, Henry Friday, Teh, Ying Wah, Mujtaba, Ghulam, Alo, Uzoma Rita, Al-garadi, Mohammed Ali

    Published 2019
    “…The study proposes a multi-view ensemble algorithm to integrate predicted values of different motion sensors. …”
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  13. 13
    Spectral Estimation And Supervised Classification Technique For Real Time Electromyography Pattern Recognition

    Spectral Estimation And Supervised Classification Technique For Real Time Electromyography Pattern Recognition by Burhan, Nuradebah

    Published 2018
    “…Electromyography (EMG) signal is a biomedical signal which measures physical activity of human muscle.It has been acknowledged to be widely used in rehabilitation or recovery application system assisting physiotherapist to monitor a patient’s physical strength,function,motion and overall well-being by addressing the underlying physical issues.In application system associated with rehabilitation,a signal processing and classification techniques are implemented to classify EMG signal obtained.For real time application in the rehabilitation, the classification is crucial issue.The success of the signal classification depends on the selection of the features that represent a raw EMG signal in the signal processing.Therefore,a robust and resilient denoising method and spectral estimation technique have been acknowledged as necessary to distinguish and detect the EMG pattern.The present study was undertaken to determine the characteristic of EMG features using denoising method and spectral estimation technique for assessing the EMG pattern based on a supervised classification algorithm.In the study,the combination of time-frequency domain (TFD) and time domain (TD) were identified as the preferred denoising method and spectral estimation techniques.In the first part of study, the recorded EMG signal filtered the contaminated noise by using wavelet transform (WT) approach which implemented discrete wavelet transform (DWT) method of the wavelet-denoising signal. …”
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  14. 14
    A vision-based deep learning approach for non-contact vibration measurement using (2+1)D CNN and optical flow

    A vision-based deep learning approach for non-contact vibration measurement using (2+1)D CNN and optical flow by Harold Harrison, Mazlina Mamat, Farah Wong, Hoe Tung Yew, Racheal Lim, Wan Mimi Diyana Wan Zaki

    Published 2025
    “…An optical flow-based preprocessing algorithm synchronized motion features in recorded video inputs with measured vibration labels, improving measurement accuracy. …”
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  15. 15
    Artifact identification for blood pressure and photoplethysmography signals in an unsupervised environment / Lim Pooi Khoon

    Artifact identification for blood pressure and photoplethysmography signals in an unsupervised environment / Lim Pooi Khoon by Lim , Pooi Khoon

    Published 2020
    “…Upon using the artifact detection method followed by BP estimation, the SBP and DBP were improved in BHS grades from D to A. …”
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  16. 16
    An Adaptive Motion Estimation Algorithm for Irregular Motion of Objects

    An Adaptive Motion Estimation Algorithm for Irregular Motion of Objects by Kajo, Ibrahim, Malik, Aamir Saeed, Kamel , Nidal

    Published 2014
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  17. 17
    ADAPTIVE MOTION ESTIMATION ALGORITHM FOR CROWDED SCENES

    ADAPTIVE MOTION ESTIMATION ALGORITHM FOR CROWDED SCENES by KAJO, IBRAHIM

    Published 2015
    “…Lots of crowd motion estimation algorithms have been presented in the last years comprising pedestrian motion. …”
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  18. 18
    Block based motion vector estimation using fuhs16 uhds16 and uhds8 algorithms for video sequence

    Block based motion vector estimation using fuhs16 uhds16 and uhds8 algorithms for video sequence by S. S. S. , Ranjit

    Published 2011
    “…Block-matching algorithm is the most common technique applied in block-based motion estimation technique. …”
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  19. 19
    Variable block based motion estimation using hexagon diamond full search algorithm (HDFSA) via block subtraction technique

    Variable block based motion estimation using hexagon diamond full search algorithm (HDFSA) via block subtraction technique by Hardev Singh, Jitvinder Dev Singh

    Published 2015
    “…There are many types of motion estimation method but the most used method is the block matching method which is the fixed block matching and the variable block matching. …”
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  20. 20
    New Fast Block Matching Algorithm Using New Hybrid Search Pattern And Strategy To Improve Motion Estimation Process In Video Coding Technique

    New Fast Block Matching Algorithm Using New Hybrid Search Pattern And Strategy To Improve Motion Estimation Process In Video Coding Technique by Hamid, Nurul 'Atiqah

    Published 2016
    “…These 6 algorithms are divided into 3 main methods namely Method A, Method B, and Method C depending on their search patterns and strategies. …”
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