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Journal articles on the topic 'Speeded-Up Robust Features'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Bay, Herbert, Andreas Ess, Tinne Tuytelaars, and Luc Van Gool. "Speeded-Up Robust Features (SURF)." Computer Vision and Image Understanding 110, no. 3 (2008): 346–59. http://dx.doi.org/10.1016/j.cviu.2007.09.014.

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2

Zhang, Jianguang, Yongxia Li, An Tai, Xianbin Wen, and Jianmin Jiang. "Motion Video Recognition in Speeded-Up Robust Features Tracking." Electronics 11, no. 18 (2022): 2959. http://dx.doi.org/10.3390/electronics11182959.

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Motion video recognition has been well explored in applications of computer vision. In this paper, we propose a novel video representation, which enhances motion recognition in videos based on SURF (Speeded-Up Robust Features) and two filters. Firstly, the detector scheme of SURF is used to detect the candidate points of the video because it is an efficient faster local feature detector. Secondly, by using the optical flow field and trajectory, the feature points can be filtered from the candidate points, which enables a robust and efficient extraction of motion feature points. Additionally, w

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3

Jeevan, Musale*, and P. Mane A. "INDIAN SIGN LANGUAGE RECOGNITION WITH IMPLEMENTATION OF SPEEDED UP ROBUST FEATURES ALGORITHM." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 3 (2016): 517–22. https://doi.org/10.5281/zenodo.47635.

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In This paper automatic gesture recognition is used for Indian Sign Language (ISL). In Indian sign language for representing alphabet we use both hands. We suggest an a method used in dealing with  addresses local-global ambiguity which increases the range of possible interpretations of natural language, and a computer has to find a way to deal with this identification, inter-class variability increases for each hand gesture. Hand parts are firstly separated and then detected by skin color model reference i.e. HSI. In this system the well-built points are removing out for recognition proc

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4

KONG Jun, JIANG Min, and Alimujiang Yiming. "Research on speeded-up robust features with RANSAC." International Journal of Advancements in Computing Technology 5, no. 8 (2013): 777–84. http://dx.doi.org/10.4156/ijact.vol5.issue8.88.

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Ali, Hanaa S., Asmaa I. Ismail, Fathi A. Farag, and Fathi E. Abd El-Samie. "Speeded up robust features for efficient iris recognition." Signal, Image and Video Processing 10, no. 8 (2016): 1385–91. http://dx.doi.org/10.1007/s11760-016-0903-8.

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6

LIAO, Chao, Guijin WANG, Quan MIAO, Zhiguo WANG, Chenbo SHI, and Xinggang LIN. "DSP-Based Parallel Implementation of Speeded-Up Robust Features." IEICE Transactions on Information and Systems E94-D, no. 4 (2011): 930–33. http://dx.doi.org/10.1587/transinf.e94.d.930.

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LONG Si-yuan, 龙思源, 张. 葆. ZHANG Bao, 宋. 策. SONG Ce, and 孙保基 SUN Bao-ji. "Object detection based on improved speeded-up robust features." Chinese Optics 10, no. 6 (2017): 719–25. http://dx.doi.org/10.3788/co.20171006.0719.

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8

Li, Jingyu, Yulei Wang, and Yanjie Wang. "Visual tracking and learning using speeded up robust features." Pattern Recognition Letters 33, no. 16 (2012): 2094–101. http://dx.doi.org/10.1016/j.patrec.2012.08.002.

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9

Hsu, Wei-Yen, and Yan-Chieh Lee. "Rat Brain Registration Using Improved Speeded Up Robust Features." Journal of Medical and Biological Engineering 37, no. 1 (2017): 45–52. http://dx.doi.org/10.1007/s40846-016-0204-2.

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10

Wang, Yin Tien, Chen Tung Chi, and Ying Chieh Feng. "Robot Simultaneous Localization and Mapping Using Speeded-Up Robust Features." Applied Mechanics and Materials 284-287 (January 2013): 2142–46. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.2142.

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An algorithm for robot mapping is proposed in this paper using the method of speeded-up robust features (SURF). Since SURFs are scale- and orientation-invariant features, they have higher repeatability than that of the features obtained by other detection methods. Even in the cases of using moving camera, the SURF method can robustly extract the features from image sequences. Therefore, SURFs are suitable to be utilized as the map features in visual simultaneous localization and mapping (SLAM). In this article, the procedures of detection and matching of the SURF method are modified to improve

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11

Seo, Jung-Jin, and Kyoung-Ro Yoona. "Modified Speeded Up Robust Features(SURF) for Performance Enhancement of Mobile Visual Search System." Journal of Broadcast Engineering 17, no. 2 (2012): 388–99. http://dx.doi.org/10.5909/jeb.2012.17.2.388.

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12

Prajakta, H. Umale Chanchal H. Sahani Aboli S. Patil Anisha A. Gedam Kajal V. Kawale Prof. Aditya Turankar. "Planer Object Detection Using Sift and Surf in Image Processing." International Journal of Research in Computer & Information Technology 7, no. 2 (2022): 31–34. https://doi.org/10.5281/zenodo.6676111.

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Object Detection refers to the capability of computers and software to locate objects in an image/scene and identify each object. Object detection is a computer vision technique that works to identify and locate objects within an image or video. In this study, we compare and analyze Scale-invariant feature transform (SIFT) and speeded-up robust features (SURF) and propose various geometric transformations. To increase the accuracy, the proposed system firstly performs the separation of the image by reducing the pixel size, using the Scale-invariant feature transform (SIFT). Then the key points

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13

Jing Zhao, Jing Zhao. "Sports Motion Feature Extraction and Recognition Based on a Modified Histogram of Oriented Gradients with Speeded Up Robust Features." 電腦學刊 33, no. 1 (2022): 063–70. http://dx.doi.org/10.53106/199115992022023301007.

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<p>Traditional motion recognition methods can extract global features, but ignore the local features. And the obscured motion cannot be recognized. Therefore, this paper proposes a modified Histogram of oriented gradients (HOG) combining speeded up robust features (SURF) for sports motion feature extraction and recognition. This new method can fully extract the local and global features of the sports motion recognition. The new algorithm first adopts background subtraction to obtain the motion region. Direction controllable filter can effectively describe the motion edge features. The HO

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14

Agrawal, Pooja, Teena Sharma, and Nishchal K. Verma. "Supervised approach for object identification using speeded up robust features." International Journal of Advanced Intelligence Paradigms 15, no. 2 (2020): 165. http://dx.doi.org/10.1504/ijaip.2020.10026727.

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Agrawal, Pooja, Teena Sharma, and Nishchal K. Verma. "Supervised approach for object identification using speeded up robust features." International Journal of Advanced Intelligence Paradigms 15, no. 2 (2020): 165. http://dx.doi.org/10.1504/ijaip.2020.105142.

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16

Long, Xu Lin, Qiang Chen, and Jun Wei Bao. "Improvement of Image Mosaic Algorithm Based on SURF." Applied Mechanics and Materials 427-429 (September 2013): 1625–30. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1625.

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The present study concerns about feature matching in image mosaic. In order to solve the problems of low accuracy and poor applicability in the traditional speeded up robust features algorithm, this paper presents an improved algorithm. Clustering algorithm based on density instead of random sample consensus method is used to eliminate mismatching pairs. The initial matching pairs are mapped onto a plane coordinate system, which can be regarded as points, by calculating the density of each point to extract the final matching pairs. The results show that this algorithm overcomes the limitations

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17

Srujana, S., K. Sripal Reddy, and D. Praveen Kumar. "Speeded Up Robust Features Registration based Efficient Multi Row Panorama Generation." Revista Gestão Inovação e Tecnologias 11, no. 2 (2021): 596–606. http://dx.doi.org/10.47059/revistageintec.v11i2.1696.

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Most of the current panorama generation tools need an input to be provided along a single axis, which means that only a small portion of the scene is recorded. To achieve a wide area of viewers, this paper suggests a multi-row panoramic technique (multi-panorama method). A pan/tilt camera allows the automatic or manual scanning to occur over large horizontal and vertical perspectives. Frame pictures in horizontal and vertical perspectives to adjust to their coordinates and projections will need separate projection marks. And the picture should be continually updated over long time periods but

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18

Edwin, Dr Anusha. "Identification of Cattle using Fuzzy Speeded up Robust Features (F-SURF)." International Journal of Research in Advent Technology 7, no. 4 (2019): 581–87. http://dx.doi.org/10.32622/ijrat.742019209.

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19

Bhosle, Udhav, and Dipti Jadhav. "Video summarisation based on motion estimation using speeded up robust features." International Journal of Computational Vision and Robotics 9, no. 6 (2019): 569. http://dx.doi.org/10.1504/ijcvr.2019.10025718.

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20

Jadhav, Dipti, and Udhav Bhosle. "Video summarisation based on motion estimation using speeded up robust features." International Journal of Computational Vision and Robotics 9, no. 6 (2019): 569. http://dx.doi.org/10.1504/ijcvr.2019.104039.

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21

Lavín-Delgado, J. E., J. E. Solís-Pérez, J. F. Gómez-Aguilar, and R. F. Escobar-Jiménez. "Fractional speeded up robust features detector with the Caputo-Fabrizio derivative." Multimedia Tools and Applications 79, no. 43-44 (2020): 32957–72. http://dx.doi.org/10.1007/s11042-020-09547-5.

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22

Sharma, Manoj Kumar, and Vijay Pal Dhaka. "Offline scripting-free author identification based on speeded-up robust features." International Journal on Document Analysis and Recognition (IJDAR) 18, no. 4 (2015): 303–16. http://dx.doi.org/10.1007/s10032-015-0252-0.

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23

Di, Guohui, Fulin Su, Hongxin Yang, and Shuang Fu. "ISAR image scattering center association based on speeded-up robust features." Multimedia Tools and Applications 79, no. 7-8 (2018): 5065–82. http://dx.doi.org/10.1007/s11042-018-6291-z.

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24

Kelen, Yoseph P. K., and Budiman Baso. "Klasifikasi Tenun Timor Menggunakan Metode SVM Berdasarkan Speeded Up Robust Features." Jurnal Teknologi Informasi dan Ilmu Komputer 10, no. 6 (2023): 1353–60. http://dx.doi.org/10.25126/jtiik.1067625.

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Penelitian ini dilakukan sebagai upaya untuk melestarikan kain tenun Timor di bidang teknologi informasi, kususnya bidang pengolahan citra digital, yaitu pengenalan pola yang merupakan solusi untuk mengenali citra tenun secara otomatis. Dalam penelitian ini, klasifikasi citra tenun Timor mengaplikasikan metode SURF (Speeded Up Robust Feature) sebagai ekstraksi fitur dengan representasi BoVW (Bag of Visual Words) sedangkan SVM (Support Vector Machine) digunakan sebagai metode classifier. Agar kinerja BoVW lebih baik, digunakan pendekatan untuk menentukan jumlah cluster yang tepat untuk mengelom

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25

Kelen, Yoseph P. K., and Budiman Baso. "Klasifikasi Tenun Timor Menggunakan Metode SVM Berdasarkan Speeded Up Robust Features." Jurnal Teknologi Informasi dan Ilmu Komputer 10, no. 6 (2023): 1353–60. https://doi.org/10.25126/jtiik.2023107625.

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Penelitian ini dilakukan sebagai upaya untuk melestarikan kain tenun Timor di bidang teknologi informasi, kususnya bidang pengolahan citra digital, yaitu pengenalan pola yang merupakan solusi untuk mengenali citra tenun secara otomatis. Dalam penelitian ini, klasifikasi citra tenun Timor mengaplikasikan metode SURF (Speeded Up Robust Feature) sebagai ekstraksi fitur dengan representasi BoVW (Bag of Visual Words) sedangkan SVM (Support Vector Machine) digunakan sebagai metode classifier. Agar kinerja BoVW lebih baik, digunakan pendekatan untuk menentukan jumlah cluster yang tepat untuk mengelom

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26

Zhou, Mu, Xia Hong, Zengshan Tian, Huining Dong, Mingchun Wang, and Kunjie Xu. "Maximum Entropy Threshold Segmentation for Target Matching Using Speeded-Up Robust Features." Journal of Electrical and Computer Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/768519.

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This paper proposes a 2-dimensional (2D) maximum entropy threshold segmentation (2DMETS) based speeded-up robust features (SURF) approach for image target matching. First of all, based on the gray level of each pixel and the average gray level of its neighboring pixels, we construct a 2D gray histogram. Second, by the target and background segmentation, we localize the feature points at the interest points which have the local extremum of box filter responses. Third, from the 2D Haar wavelet responses, we generate the 64-dimensional (64D) feature point descriptor vectors. Finally, we perform t

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27

Umale, Prajakta, Aboli Patil, Chanchal Sahani, Anisha Gedam, and Kajal Kawale. "PLANER OBJECT DETECTION USING SURF AND SIFT METHOD." International Journal of Engineering Applied Sciences and Technology 6, no. 11 (2022): 36–39. http://dx.doi.org/10.33564/ijeast.2022.v06i11.008.

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Object Detection refers to the capability of computer and software to locate objects in an image/scene and identify each object. Object detection is a computer vision technique works to identify and locate objects within an image or video. In this study, we compare and analyze Scale-invariant feature transform (SIFT) and speeded up robust features (SURF) and propose a various geometric transformation. To increase the accuracy, the proposed system firstly performs the separation of the image by reducing the pixel size, using the Scale-invariant feature transform (SIFT). Then the key points are

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28

Wang, Yin-Tien, and Guan-Yu Lin. "Improvement of speeded-up robust features for robot visual simultaneous localization and mapping." Robotica 32, no. 4 (2013): 533–49. http://dx.doi.org/10.1017/s0263574713000830.

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SUMMARYA robot mapping procedure using a modified speeded-up robust feature (SURF) is proposed for building persistent maps with visual landmarks in robot simultaneous localization and mapping (SLAM). SURFs are scale-invariant features that automatically recover the scale and orientation of image features in different scenes. However, the SURF method is not originally designed for applications in dynamic environments. The repeatability of the detected SURFs will be reduced owing to the dynamic effect. This study investigated and modified SURF algorithms to improve robustness in representing vi

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29

Zhuang, Zhihong, and Hongbo Wang. "A novel nonuniformity correction algorithm based on speeded up robust features extraction." Infrared Physics & Technology 73 (November 2015): 281–85. http://dx.doi.org/10.1016/j.infrared.2015.10.006.

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30

Qiao, Guohui, Guanghua Zong, Jianzhong Wang, and Minglei Sun. "Automatic toxic granulation detection and grading based on speeded up robust features." Cytometry Part A 79A, no. 11 (2011): 887–90. http://dx.doi.org/10.1002/cyto.a.21113.

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31

Hariprasath., S., S.M GiriRajkumar., Yahya. A. Mohamed, Krishna. M. Hari, and Kumaran. K. Krishna. "Object Detection using SURF features." International Journal of Multidisciplinary Research Transactions 5, no. 7 (2023): 110–16. https://doi.org/10.5281/zenodo.7933324.

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A common method for locating items in photos is object detection utilising the Speeded-Up Robust Features (SURF) algorithm. In order to identify the existence of a certain object, this method pulls important details from an image and compares them to a learned collection of features. The algorithm used in this method can identify items even when they are rotated or partially obscured. The SURF technique is particularly helpful in computer vision applications where object detection is crucial, such as facial recognition and autonomous vehicles. An overview of the SURF algorithm and its use in o

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Yuk, Eun, Seung Park, Cheong-Sool Park, and Jun-Geol Baek. "Feature-Learning-Based Printed Circuit Board Inspection via Speeded-Up Robust Features and Random Forest." Applied Sciences 8, no. 6 (2018): 932. http://dx.doi.org/10.3390/app8060932.

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33

Devi, Chingakham Neeta, Debaprasad Das, and Haobam Mamata Devi. "A Meetei Mayek Handwritten Digit Recognition System." Journal of Computational and Theoretical Nanoscience 17, no. 1 (2020): 334–39. http://dx.doi.org/10.1166/jctn.2020.8671.

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Optical Character Recognition is an appealing field of work for research where an image containing text is given as input and text in the image is translated into an editable format. This paper proposes Meetei/Meitei Mayek Handwritten Digit Recognition System where an Isolated Handwritten Meetei Mayek Digit Database consisting of 10000 plus digits has been developed. This proposed Handwritten Meetei Mayek Digit Recognition System is an important component of Manipuri Meetei Mayek Optical Character Recognition system which is under development. For feature extraction, we have used State-of-Art

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34

Norhisham Razali, Mohd, Noridayu Manshor, Alfian Abdul Halin, Razali Yaakob, and Norwati Mustapha. "Recognition of Food with Monotonous Appearance using Speeded-Up Robust Feature (SURF)." International Journal of Engineering & Technology 7, no. 4.31 (2018): 204–8. http://dx.doi.org/10.14419/ijet.v7i4.31.23368.

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Food has become one of the most photographed objects since the inceptions of smart phones and social media services. Recently, the analysis of food images using object recognition techniques have been investigated to recognize food categories. It is a part of a framework to accomplish the tasks of estimating food nutrition and calories for health-care purposes. The initial stage of food recognition pipeline is to extract the features in order to capture the food characteristics. A local feature by using SURF is among the efficient image detector and descriptor. It is using fast hessian detecto

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35

Cizek, Petr, and Jan Faigl. "Real-Time FPGA-Based Detection of Speeded-Up Robust Features Using Separable Convolution." IEEE Transactions on Industrial Informatics 14, no. 3 (2018): 1155–63. http://dx.doi.org/10.1109/tii.2017.2764485.

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36

Idris, Mohd Yamani Idna, Hamzah Arof, Noorzaily Mohamed Noor, and Ainuddin Wahid Abdul Wahab. "Memory Access Reduction for Field Programmable Gate Array-Speeded Up Robust Features Implementation." Advanced Science Letters 19, no. 2 (2013): 646–50. http://dx.doi.org/10.1166/asl.2013.4759.

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37

Zhang, Nan. "Computing Optimised Parallel Speeded-Up Robust Features (P-SURF) on Multi-Core Processors." International Journal of Parallel Programming 38, no. 2 (2009): 138–58. http://dx.doi.org/10.1007/s10766-009-0122-9.

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38

WANG, Wenzhao, Jun TANG, Chong SHEN, and Jun LIU. "A brain-inspired SLAM navigation based on speeded up robust features matching algorithm." Journal of Measurement Science and Instrumentation 14, no. 2 (2023): 137–47. http://dx.doi.org/10.62756/jmsi.1674-8042.2023016.

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39

Teke, Mustafa. "High-resolution multispectral satellite image matching using scale invariant feature transform and speeded up robust features." Journal of Applied Remote Sensing 5, no. 1 (2011): 053553. http://dx.doi.org/10.1117/1.3643693.

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40

Ayushi, Jadia, and Chawla M.P.S. "Image Classification and Detection of Insulators using Bag of Visual Words and Speeded up Robust Features." International Journal of Innovative Science and Modern Engineering (IJISME) 6, no. 10 (2020): 7–13. https://doi.org/10.35940/ijisme.J1260.0961020.

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Electrical substation online monitoring in computer vision technology is based on image processing algorithm to perform visual analysis. This paper presents classification of ceramicand glass insulators through Bag of Visual Words and detection of these insulators by Point Feature Matching. The training image datasets are used for categorization by forming a visual vocabulary while a new unlabeled image from testing image dataset is classify using nearest neighbor classification method for features descriptor. For detection we use Speeded up Robust Features for detecting position of insulator

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41

Hutama, Samuel Alvin, Saptadi Nugroho, and Darmawan Utomo. "Features Deletion on Multiple Objects Recognition using Speeded-Up Robust Features, Scale Invariant Feature Transform and Randomized KD-Tree." TELKOMNIKA (Telecommunication Computing Electronics and Control) 14, no. 2 (2016): 692. http://dx.doi.org/10.12928/telkomnika.v14i2.3461.

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Zhang, Bao Feng, Ying Kui Jiao, Zhi Jun Ma, Yong Chen Li, and Jun Chao Zhu. "A Method of Features Extraction Based on Fisheye Image." Applied Mechanics and Materials 668-669 (October 2014): 1029–32. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.1029.

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In this paper, feature extraction algorithm based on spherical perspective projection model for the matching fisheye image is proposed. The fisheye image is mapped to the image plane through spherical mapping. Then the diffusion equation is formed by convolution of the image projection and spherical Gaussian function. The feature points of image are extracted based on the SIFT at the scale of spherical correlation function. Compared with SURF(Speeded Up Robust Features), more feature points in a shorter time are obtained.

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Dewanti, Farida, and Raden Sumiharto. "Purwarupa Sistem Penggabungan Foto Udara Pada UAV Menggunakan Algoritma Surf (Speeded-Up Robust Features)." IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) 5, no. 2 (2015): 165. http://dx.doi.org/10.22146/ijeis.7640.

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Abstrak Purwarupa penggabungan foto udara pada UAV menggunakan algoritma SURF merupakan suatu sistem yang dirancang untuk melakukan penggabungan citra. Citra tersebut adalah citra yang dihasilkan fixed-wings UAV. Keluaran dari sistem ini berupa tampilan citra dengan objek yang lebih luas. Sistem ini dirancang untuk dapat melakukan penggabungan foto udara dengan menggunakan algoritma SURF, FLANN, RANSAC, dan warpPerspective. Algoritma SURF digunakan sebagai detektor keypoint dari masing-masing input foto. Metode FLANN untuk melakukan pencocokan keypoint yang ditemukan. RANSAC digunakan untuk pe

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44

Wang Yangping, 王阳萍, 秦安娜 Qin Anna, 郝旗 Hao Qi, and 党建武 Dang Jianwu. "Semi-Global Stereo Matching of Remote Sensing Images Combined with Speeded up Robust Features." Acta Optica Sinica 40, no. 16 (2020): 1628003. http://dx.doi.org/10.3788/aos202040.1628003.

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45

El Makhfi, Noureddine. "A Word Spotting Method for Arabic Manuscripts Based on Speeded Up Robust Features Technique." Advances in Science, Technology and Engineering Systems Journal 4, no. 6 (2019): 99–107. http://dx.doi.org/10.25046/aj040612.

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46

Alwan, Hiba Basim, and Ku Ruhana Ku-Mahamud. "Cancellable face template algorithm based on speeded-up robust features and winner-takes-all." Multimedia Tools and Applications 79, no. 39-40 (2020): 28675–93. http://dx.doi.org/10.1007/s11042-020-09319-1.

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Nawaz, Saqib Ali, Jingbing Li, Uzair Aslam Bhatti, Anum Mehmood, Muhammad Usman Shoukat, and Mughair Aslam Bhatti. "Advance hybrid medical watermarking algorithm using speeded up robust features and discrete cosine transform." PLOS ONE 15, no. 6 (2020): e0232902. http://dx.doi.org/10.1371/journal.pone.0232902.

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48

Mohammad, Awad Alfawair Khalid Alkaabneh. "Secure Image Indexing Using Speeded Up Robust Features (SURF) Key Points and SHAKE256 Hashing." Multicultural Education 7, no. 8 (2021): 108. https://doi.org/10.5281/zenodo.5168797.

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<em>Due to the rapid development of the Internet and storing data, several security attacks have taken place, causing deep fears to computer users. On the other hand, many computer users mainly depend on different techniques to protect their data and keep them safe and secure, including their private images, since they are important and sensitive. Some currently applied procedures do not provide enough protection for the privacy of the stored images. Consequently, researchers are exerting great effort to search for images in a more secure way using different codes. The primary purpose of this

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Belarbi, Mohammed Amin, Saïd Mahmoudi, and Ghalem Belalem. "PCA as Dimensionality Reduction for Large-Scale Image Retrieval Systems." International Journal of Ambient Computing and Intelligence 8, no. 4 (2017): 45–58. http://dx.doi.org/10.4018/ijaci.2017100104.

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Dimensionality reduction in large-scale image research plays an important role for their performance in different applications. In this paper, we explore Principal Component Analysis (PCA) as a dimensionality reduction method. For this purpose, first, the Scale Invariant Feature Transform (SIFT) features and Speeded Up Robust Features (SURF) are extracted as image features. Second, the PCA is applied to reduce the dimensions of SIFT and SURF feature descriptors. By comparing multiple sets of experimental data with different image databases, we have concluded that PCA with a reduction in the ra

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Sheta, Bassem. "Assessments Of Different Speeded Up Robust Features (SURF) Algorithm Resolution For Pose Estimation Of UAV." International Journal of Computer Science & Engineering Survey 3, no. 5 (2012): 15–41. http://dx.doi.org/10.5121/ijcses.2012.3502.

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