Search Results - (( frames extraction method algorithm ) OR ( data extraction method algorithm ))

A fast feature extraction algorithm for image and video processing by Abdulhussain, Sadiq H., Ramli, Abdul Rahman, Mahmmod, Basheera M., Saripan, M. Iqbal, Syed Mohamed, Syed Abdul Rahman Al-Haddad, Baker, Thar, Flayyih, Wameedh Nazar, Jassim, Wissam A.

“…These features are used to represent the local visual content of images and video frames. We compared the proposed method with the traditional approach of feature extraction using a standard image technique. …”
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An improved method in speech signal input representation based on DTW technique for NN speech recognition system by Sudirman, Rubita, Salleh, Sh. Hussain, Salleh, Shaharuddin

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AR oriented pose matching mechanism from motion capture data by Iqbal J., Sidhu M.S., Ariff M.B.M.

“…In order to extract the exact matched pose, the frame sequence is divided into pose feature frame and skeletal data frame by the use of pose matching dance training movement recognition algorithm (PMDTMR). …”

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

“…Most common solution of the task is the uses of threshold strategy based on trial and error method. As the method is not automated, it is time consuming and sometimes a single threshold value does not work for a series of image frames of video data. …”
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Image classification using two dimensional wavelet coefficients with parallel computing by Ong, Yew Fai

“…Wavelet is a mathematical function that decomposes any given data signals and enabling the extraction of discontinuities and sharp spikes permeated in the signal. …”
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Time normalization of LPC feature using warping method by Sudirman, Rubita, Salleh, Sh. Hussain, Khalid, Puspa Inayat, Ahmad, Abd. Hamid

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Improving performance of automated coronary arterial tree center-line extraction, stent localization and tracking by Boroujeni, Farsad Zamani

“…This problem is addressed by proposing an accurate and robust centerline extraction method. Starting at each detected seed point, the centerline extraction method utilizes eigenvalues and eigenvectors of Hessian matrix for the pixels located on a semi-circular scanning profile for robust estimation of the next centerline point. …”
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Image Stitching Of Aerial Footage by Ng, Wei Haen

“…In this project, an image stitching framework is proposed to take aerial footage as input data. The proposed algorithm extracts the frames of the aerial footage and undistorts the bird-eye-effect of the images to remove the noises. …”
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Development Of A Robust Blind Digital Video Watermarking Algorithm Using Discrete Wavelet Transform by Al-Deen, Ahmed A. Baha'a

“…On the other hand, a random key is used to choose the frames to be watermarked to increase the security level of the algorithm and discourage piracy. …”
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Enhanced mechanism to handle missing data of Hadith classifier by Aldhlan, Kawther A., Zeki, Ahmed M., Zeki, Akram M.

“…This research produced a mechanism to deal with missing data in Hadith database, 999 Hadiths from Sahih Al-Bukhari, Jami'u Al-Termithi and Selseelt AlaHadith Aldae'ifah w' Almadu'h were framed the sample of this study, the attributes of the hadith database were gained according to the validate methods of Hadith science, the experiment applied C4.5 algorithm to extract the rules of classification. …”
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Autism spectrum self-stimulatory behaviours classification using explainable temporal coherency deep networks and SVM classifier / Liang Shuaibing by Liang , Shuaibing

“…Firstly, the extracted features are classified by the k-means method to demonstrate the classification of self-stimulatory behaviours in a completely unsupervised way. …”
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An integrated deep learning deepfakes detection method (IDL-DDM) by Yassin, Warusia, Johan, Azwan, Abas, Zuraida Abal, Baharon, Mohd Rizuan, Bejuri, Wan, Ismail, Anuar

“…In addition, the Long Short-Term Memory (LSTM) approach is applied consecutively after CNN in order to grant sequential processing of data and overcome learning dependencies. Using this learning algorithm, several facial region characteristics such as eyes, nose, and mouth are extracted and further transformed into numerical form with the intention to identify video frames more precisely. …”
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Detection and Classification of Moving Objects for an Automated Surveillance System by Md. Tomari, Mohd Razali

“…All the methods have been tested on video data and the experimental results have demonstrated a fast and robust system …”
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Detection and classification of moving objects for an automated surveillance system by Md Tomari, Mohd Razali

“…All the methods have been tested on video data and the experimental results have demonstrated a fast and robust system…”
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Frontal View Gait Recognition With Fusion of Depth Features From a Time of Flight Camera by Tengku Mohd Afendi, Zulcaffle, Fatih, Kurugollu, Crookes, Danny, Ahmed, Bouridane, Mohsen, Farid

“…The four-part method includes: a new human silhouette extraction algorithm that reduces the multiple reflection problem experienced by ToF cameras; a frame selection method based on a new gait cycle detection algorithm; four new gait image representations; and a novel fusion classifier. …”
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Detection and classification of moving objects for an automated surveillance system by Md Tomari, Mohd Razali

“…All the methods have been tested on video data and the experimental results have demonstrated a fast and robust system.…”
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Frontal view gait recognition with fusion of depth features from a time of flight camera by Tengku Mohd Afendi, Zulcaffle, Kurugollu, F., Crookes, D., Bouridane, A., Farid, M.

“…The four-part method includes: A new human silhouette extraction algorithm that reduces the multiple reflection problem experienced by ToF cameras; a frame selection method based on a new gait cycle detection algorithm; four new gait image representations; and a novel fusion classifier. …”
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Identifying and detecting unlawful behavior in video images using genetic algorithm / Shahirah Mohamed Hatim by Mohamed Hatim, Shahirah

“…Each video is of less than 30 seconds length. The data undergo the pre-processed phase which consists of edge detection, adaptive thresholding segmentation and MATLAB regionprops function for feature extraction. …”
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Deep learning object detector using a combination of Convolutional Neural Network (CNN) architecture (MiniVGGNet) and classic object detection algorithm by Ismail, Asmida, Ahmad, Siti Anom, Che Soh, Azura, Hassan, Mohd Khair, Harith, Hazreen Haizi

“…Based on the experiment result, the percentage of classification accuracy of the network is 80% to 90% and the time for the system to detect the object is less than 15sec/frame. Experimental results show that there are reasonable and efficient to combine classic object detection method with a deep learning classification approach. …”
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Correlation-based subset evaluation of feature selection for dynamic Malaysian sign language by Sutarman, .

“…Thus by adding processes before classification methods such as feature selection methods can provide better data input in the classification process, it is expected to improve the performance of the method of classification. …”
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