Search Results - (( using function method algorithm ) OR ( motion extraction method algorithm ))

Adaptive Initial Contour and Partly-Normalization Algorithm for Iris Segmentation of Blurry Iris Images by Jamaludin, Shahrizan, Mohamad Ayob, Ahmad Faisal, Mohd Norzeli, Syamimi, Mohamed, Saiful Bahri

“…Finally, the partly -normalization method for normalization and feature extraction was designed by selecting the most prominent iris features. …”
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The classification of wink-based eeg signals by means of transfer learning models by Jothi Letchumy, Mahendra Kumar

“…The implementation of pre-processing algorithms has been demonstrated to be able to mitigate the signal noises that arises from the winking signals without the need for the use signal filtering algorithms. …”
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Human action recognition using slow feature analysis / Bardia Yousefi by Bardia, Yousefi

“…These pathways are designed to analyze not only motion information (optical flow) but also the ventral processing stream in the brain that proceeds with form features, in which Gabor wavelet is widely used. …”
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Fast adaptive motion estimation search algorithm for H.264 encoder by Patwary, Md Anwarul Kaium

“…Motion estimation is a technique of video compression and video processing applications; it extracts motion information from the video sequence. …”
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Electrocardiogram based heart disease diagnosis using artificial intelligence by Hussain Kareem, Khleaf

“…Accordingly, the first step is an image segmentation method using proposed thresholding algorithms has been used to locate objects and boundaries of the ECG signal and background grid lines in the ECG images. …”
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Spectral Estimation And Supervised Classification Technique For Real Time Electromyography Pattern Recognition by Burhan, Nuradebah

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

“…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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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

“…The method employs the RADICAL technique to extract independent subcomponents. …”
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Motion detection using Horn-Schunck optical flow by Wan Nur Azhani, W. Samsudin, Kamarul Hawari, Ghazali

“…This system is design to detect motion in a crowd using one of the optical flow algorithms, Horn-Schunck method. …”
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Particle swarm optimization with deep learning for human action recognition by Usmani, U.A., Watada, J., Jaafar, J., Aziz, I.A., Roy, A.

“…This decreases the detection efficiency and degrades the target tracking output. Also, the current motion target detection algorithms extract features from the relevant object only if the moving object has complex texture features. …”
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A method for motion tracking of ventricular endocardial surface by O. K. Rahmat, Rahmita Wirza, Dawood, Faten Abed Ali, Dimon, Mohd Zamrin, Kadiman, Suhaini, Abdullah, Lili Nurliyana

“…The present invention relates to a method for automatic motion tracking of ventricular endocardial surface in three dimensional (3D) echocardiography, characterized by the steps of extracting a plurality of ventricular endocardial contours over a complete cardiac cycle; identifying a plurality of landmarks on each ventricular endocardial contour; measuring displacement vector flow (DVF) for each landmark by comparing a pair of consecutive ventricular endocardial contours; measuring velocity vector flow (VVF) for each landmark from end-diastolic (ED) to end-systolic (ES) and vice versa; identifying at least four landmarks from the plurality of landmarks on each ventricular endocardial contour to represent anatomical landmarks of left lateral surface, right lateral surface, inferior wall and anterior wall by using geometrical distance calculation (GDC) algorithm; analysing ventricular endocardial motion direction using a fuzzy logic analyzer (FLA) for the four landmarks identified; updating values of the displacement vector flow (DVF) and velocity vector flow (VVF) based on the ventricular endocardial motion direction; and generating graphical curves of time versus values of the displacement vector flow (DVF) and velocity vector flow (VVF) for the four landmarks identified.…”
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Design and performance analysis of artificial neural network for hand motion detection from EMG signals by Ibrahimy, Muhammad Ibn, Ahsan, Md. Rezwanul, Khalifa, Othman Omran

“…The conventional and most effective time and timefrequency based features are extracted and normalized. The neural network has been trained with the normalized feature set with supervised learning method. …”
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Detection and Classification of Moving Objects for an Automated Surveillance System by Md. Tomari, Mohd Razali

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

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

“…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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Automated threshold detection for object segmentation in colour image by Akhtaruzzaman, Md., Shafie, Amir Akramin, Khan, Md. Raisuddin

“…In the next stage, Line Fill (LF) algorithm is applied for smoothing the edges of object and finally background is subtracted to extract the targeted object. …”
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AR oriented pose matching mechanism from motion capture data by Iqbal J., Sidhu M.S., Ariff M.B.M.

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

“…Human modelling is the enabling step in the human motion analysis system where the identified person from a video camera will be projected and represented in a better model to ease the latter processes such as feature extraction. …”
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Multi-sensor fusion and deep learning framework for automatic human activity detection and health monitoring using motion sensor data / Henry Friday Nweke by Henry Friday , Nweke

“…First, to investigate existing multi-sensor and automatic feature extraction methods for human activity detection and health monitoring using motion sensor. …”
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Feature extraction: hand shape, hand position and hand trajectory path by Bilal, Sara Mohammed Osman Saleh, Akmeliawati, Rini

“…Algorithms have been developed for extracting these features after segmenting the head and the two hands. …”
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