Search Results - candidate ((detection algorithm) OR (pollination algorithm))

3D face candidate region detection using background subtraction / Zulfikri Paidi and Nurzaid Muhd Zain by Paidi, Zulfikri, Muhd Zain, Nurzaid

“…Our focus is to solve the first challenge in face registration, which is to detect and identify face region. From the experiment, it shows some promising results related to using background subtraction in face candidate region detection algorithm. …”
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Automated face localization and facial features detection using geometric information by Chai, Tong Yuen, Mohd Rizon, Mohamed Juhari, Prof. Dr., Karthigayan, M., Sugisaka, M., Hazry, Desa, Dr., Ibrahim, Z.

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Real-time detection of face and iris by Chai, Tong Yuen, Mohamed Rizon, Mohamed Juhari, Prof. Dr., Muhammad Shazri

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Temporal video segmentation using squared form of Krawtchouk-Tchebichef moments by Abdulhussain, Sadiq H.

“…The fade transitions are detected based on the smoothed moments energy and the moments of gradients correlation for the candidate segments. …”
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Skin detection using HSV color component subtraction and texture information / Rizal Mat Jusoh and Norhazimi Hamzah by Mat Jusoh, Rizal, Hamzah, Norhazimi

“…This thesis presents skin detection algorithm for detecting human skin regions in color images. …”
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Automatic detection of face and facial features by Chai, Tong Yuen, Mohd Rizon, Mohamed Juhari, Prof. Dr., Woo, San San, Sugisaka, M.

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Improvement real-time detection of moving vehicle in a dynamic scene using shadow removal method / Khairul Azman Ahmad, Mohd Halim Mohd Noor,Mohamad Adha Mohamad Idin by Ahmad, Khairul Azman, Mohd Noor, Mohd Halim, Mohamad Idin, Mohamad Adha

“…Real-time processing is still feasible as these sophisticated algorithms are applied only a small number of candidates foreground pixels. …”
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Improved Malware detection model with Apriori Association rule and particle swarm optimization by Adebayo, Olawale Surajudeen, Abdul Aziz, Normaziah

“…These rule models are used together with extraction algorithm to classify and detect malicious android application. …”
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Android Malware classification using static code analysis and Apriori algorithm improved with particle swarm optimization by Adebayo, Olawale Surajudeen, Abdul Aziz, Normaziah

“…In this method, features were extracted from Android applications byte-code through static code analysis, selected and were used to train supervised classifiers. Using a number of candidate detectors, the true positive rate of detecting malicious code is maximized, while the false positive rate of wrongful detection is minimized. …”
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Static code analysis of permission-based features for android malware classification using apriori algorithm with particle swarm optimization by Adebayo, Olawale Surajudeen, Abdul Aziz, Normaziah

“…This paper presents a classification approach on android malware using candidate detectors generated from an unsupervised association rule of Apriori Algorithm. …”
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Fraud detection in telecommunication industry using Gaussian mixed model by Mohd Yusoff, Mohd Izhan, Mohamed, Ibrahim, Abu Bakar, Mohd Rizam

“…Using data obtained from one of the leading telecommunication companies in Malaysia, we show that the proposed algorithm has successfully not only detected fraud calls as suspected by the company, but also to identify suspicious calls which can be candidates of fraud call. …”
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Integrated face and facial components detection by Ho, Lip Chin, Hanafi, Marsyita, Salka, Tanko Danial

“…The system detects face, nose and mouth using three different classifiers, which were created based on the Viola-Jones method [1] and the eyes were detected using an Eye Detection method that consists of resolution reduction, identification of the eye candidates using eye filter [2] and eyes localization based on mean comparison. …”
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Improving the efficiency of clustering algorithm for duplicates detection by Emran, Nurul Akmar, Abdul Rahim, Abdulrazzak Ali Mohamed, Kamal Baharin, Safiza Suhana, Othman, Zahriah, Salem, Awsan Thabet, Abd Aziz, Maslita, Md. Bohari, Nor Mas Aina, Abdullah, Noraswaliza

“…Clustering method is a technique used for comparisons reduction between the candidates records in the duplicate detection process. …”
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Dingle's Model-based EEG Peak Detection using a Rule-based Classifier by Asrul, Adam, Norrima, Mokhtar, Marizan, Mubin, Zuwairie, Ibrahim, Mohd Ibrahim, Shapiai

“…In this study, the performances of four different peak models of time domain approach which are Dumpala's, Acir's, Liu's, and Dingle's peak models are evaluated for electroencephalogram (EEG) signal peak detection algorithm. The algorithm is developed into three stages: peak candidate detection, feature extraction, and classification. …”
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Fast shot boundary detection based on separable moments and support vector machine by Idan, Zinah N., Abdulhussain, Sadiq H., Mahmmod, Basheera M., Al-Utaibi, Khaled A., Syed Abdul Rahman Al-, Syed Abdul Rahman Al-Hadad, Sait, Sadiq M.

“…Moreover, for the proposed SBD algorithm, a comparative study is performed with state-of-the-art algorithms. …”
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A Fast Vertical Edge Detection Algorithm for Car License Plate Detection by Ali Al-Ghaili, Abbas Mohammed

“…This thesis aims to propose a fast vertical edge detector using Vertical Edge Detection Algorithm (VEDA) and to build a Car License Plate Detection (CLPD) method. …”
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Vertical-edge-based car-license-plate detection method by Al-Ghaili, Abbas Mohammed Ali, Mashohor, Syamsiah, Ramli, Abdul Rahman, Ismail, Alyani

“…Finally, an LP is detected. The third contribution is that we compare the VEDA to the Sobel operator in terms of accuracy, algorithm complexity, and processing time. …”
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A feature-based approach for segmenting faces by Zaqout, I., Zainuddin, R., Baba, S.

“…The algorithm detects feature points from the image and groups them into face candidates using geometric and grey level constraints. …”
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Evaluation of different peak models of eye blink EEG for signal peak detection using artificial neural network by Adam, A., Ibrahim, Z., Mokhtar, N., Shapiai, M.I., Mubin, M.

“…Therefore, the purpose of peak detection algorithm is to distinguish an actual peak location from a list of peak candidates. …”
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Facial features for template matching based face recognition by Chai, Tong Yuen, Mohd Rizon, Mohamed Juhari, Prof. Dr., Woo, San San, Tan, Ching Seong, Dr.

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