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Zeitschriftenartikel zum Thema „Robuck“

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Autor: Grafiati

Veröffentlicht am 27. Juli 2024

Zuletzt aktualisiert am 28. Juli 2024

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1

Purnama, Drajat Indra, und Pardomuan Robinson Sihombing. „PERBANDINGAN ANALISIS KOMPONEN UTAMA DAN ROBUST PCA (ROBPCA)“. Jurnal Bayesian : Jurnal Ilmiah Statistika dan Ekonometrika 1, Nr. 1 (02.03.2021): 67–76. http://dx.doi.org/10.46306/bay.v1i1.7.

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Sulawesi Selatan merupakan provinsi di wilayah timur Indonesia yang merupakan pintu gerbang menuju beberapa wilayah atau kota di Indonesia Timur. Tingkat kesejahteraan penduduk Sulawesi Selatan dapat diukur salah satunya dengan menghitung pengeluaran dalam memenuhi kebutuhan hidupnya baik kebutuhan makanan maupun bukan makanan. Pada penelitian ini akan dilakukan analisis terhadap rata-rata pengeluaran per kapita sebulan pada empat belas kelompok makanan. Untuk memudahkan analisis terhadap variabel yang jumlahnya banyak maka perlu dilakukan penyederhanaan jumlah variabel menggunakan analisis kompone utama dengan metode PCA klasik dan ROBPCA. Hasil yang didapat metode ROBPCA memberikan performa yang lebih baik dari pada PCA klasik, hal ini dapat dilihat model ROBPCA mampu menghasilkan jumlah komponen utama yang lebih sedikit daripada variabel asalnya, hanya dengan 3 komponen sudah mampu menjelaskan hingga 80,69 persen

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2

Hubert, Mia, Peter J. Rousseeuw und Karlien Vanden Branden. „ROBPCA: A New Approach to Robust Principal Component Analysis“. Technometrics 47, Nr. 1 (Februar 2005): 64–79. http://dx.doi.org/10.1198/004017004000000563.

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3

Noor, Amina, Aitzaz Ahmad, Erchin Serpedin, Mohamed Nounou und Hazem Nounou. „ROBNCA: robust network component analysis for recovering transcription factor activities“. Bioinformatics 29, Nr. 19 (11.08.2013): 2410–18. http://dx.doi.org/10.1093/bioinformatics/btt433.

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4

Leotta, Maurizio, Andrea Stocco, Filippo Ricca und Paolo Tonella. „Robula+: an algorithm for generating robust XPath locators for web testing“. Journal of Software: Evolution and Process 28, Nr. 3 (März 2016): 177–204. http://dx.doi.org/10.1002/smr.1771.

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5

Chary, V. Vishnu Vardhan, und Ch Sandeep Ch.Sandeep. „Robust Offline Signature Verification“. International Journal of Scientific Research 2, Nr. 12 (01.06.2012): 93–95. http://dx.doi.org/10.15373/22778179/dec2013/30.

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6

YULIANI, NI WAYAN, I. KOMANG GDE SUKARSA und I. GUSTI AYU MADE SRINADI. „PERBANDINGAN REGRESI KOMPONEN UTAMA DAN ROBPCA DALAM MENGATASI MULTIKOLINEARITAS DAN PENCILAN PADA REGRESI LINEAR BERGANDA“. E-Jurnal Matematika 2, Nr. 4 (22.01.2014): 1. http://dx.doi.org/10.24843/mtk.2013.v02.i04.p050.

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Multiple linear regression analysis with a lot of independent variable always makes many problems because there is a relationship between two or more independent variables. The independent variables which correlated each other are called multicollinearity. Principal component analysis which based on variance covariance matrix is very sensitive toward the existence of outlier in the observing data. Therefore in order to overcome the problem of outlier it is needed a method of robust estimator toward outlier. ROBPCA is a robust method for PCA toward the existence of outlier in the data. In order to obtain the robust principal component is needed a combination of Projection Pursuit (PP) with Minimum Covariant Determinant (MCD). The results showed that the ROBPCA method has a bias parameter and Mean Square Error (MSE) parameter lower than Principal Component Regression method. This case shows that the ROBPCA method better cope with the multicollinearity observational data influenced by outlier.

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7

Mayaud, L., S. Filipe, L. Pétégnief, O. Rochecouste und M. Congedo. „Robust Brain-computer Interface for virtual Keyboard (RoBIK): Project results“. IRBM 34, Nr. 2 (April 2013): 131–38. http://dx.doi.org/10.1016/j.irbm.2013.01.013.

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8

Aung, Zaw Win. „A Robust Speaker Identification System“. International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (31.08.2018): 2057–64. http://dx.doi.org/10.31142/ijtsrd18274.

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9

Kondolembang, Ferry. „PEMODELAN STATISTICAL DOWNSCALLING DENGAN PENDEKATAN REGRESI BAYES ROBUST PCA (STUDI KASUS : DATA GCM STASIUN AMBON)“. BAREKENG: Jurnal Ilmu Matematika dan Terapan 6, Nr. 2 (01.12.2012): 45–50. http://dx.doi.org/10.30598/barekengvol6iss2pp45-50.

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Masalah mendasar dari prediksi prediksi model curah hujan adalah keakuratan model berdasarkan proses stokhastik skala global maupun skala kecil. Statistical Downscalling(SD) merupakan salah satu alternatif untuk mengatasi masalah tersebut. SD adalah model yang menghubungkan skala global GCM dengan skala yang lebih kecil (lokal) dengan jalanpra-pemrosesan .reduksi dimensi domain grid untuk mengatasi kasus multikolinearitas. Metode reduksi dimensi yang serikali digunakan adalah Principal Component Analysis(PCA). Namun PCA tidak dapat diandalkan jika ada pengamatan outlier dalam data, sehingga diperlukan reduksi dimensi yang robust. Reduksi dimensi robust menggunakanRobust Principal Component Analysis (ROBPCA) dengan estimator robust MCD. Dari hasil reduksi dimensi domain grid tersebut selanjutnya diregresikan dengan variabel responberupa data curah hujan di stasiun Ambon dengan pendekatan regresi Bayes. Pendekatan regresi Bayes ROBUST PCA menjadi salah satu alternatif pada pemodelan SD. HasilPenelitian menunjukkan Metode regresi Bayes ROBPCA cenderung lebih baik pada domain 8x8 dilihat pada kriteria kebaikan model RMSE terkecil yaitu 231,4 dan R-Squareterbesar 38,1% dibandingkan domain 3x3 dan domain 12x12

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10

Prat, N., R. Acosta und M. Rieradevall. „Presence of Symbiocladius wygodzinskyi Roback, 1965 (Diptera, Chironomidae) in Peru. Taxonomic remarks“. Graellsia 69, Nr. 1 (22.02.2013): 117–21. http://dx.doi.org/10.3989/graellsia.2013.v69.074.

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11

Kuznetsov, S. P. „Generation of Robust Hyperbolic Chaos in CNN“. Nelineinaya Dinamika 15, Nr. 2 (2019): 109–24. http://dx.doi.org/10.20537/nd190201.

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12

Keream, Settar S., Ahmed N. Abdalla, Ruzlaini Ghoni, Mohd Razali Daud und Youssif Al Mashhadany. „Robust Dynamic Inverse Controller For Spacecraft Model“. International Journal of Scientific Research 3, Nr. 5 (01.06.2012): 113–17. http://dx.doi.org/10.15373/22778179/may2014/37.

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13

Yu, Hengjun, Kohei Inoue, Kenji Hara und Kiichi Urahama. „A Robust K-Means for Document Clustering“. Journal of the Institute of Industrial Applications Engineers 6, Nr. 2 (25.04.2018): 60–65. http://dx.doi.org/10.12792/jiiae.6.60.

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14

Susilowati, Bekti Endar, und Pardomuan Robinson Sihombing. „Metode ROBPCA (Robust Principal Component Analysis) dan Clara (Clustering Large Area) pada Data dengan Outlier“. Jurnal Ilmu Komputer 13, Nr. 2 (28.09.2020): 11. http://dx.doi.org/10.24843/jik.2020.v13.i02.p04.

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Principal Component Analysis (PCA) merupakan salah satu analisis multivariat yang digunakan untuk mengganti variable dengan Principal Component yang sedikit jumlahnya namun tidak terlalu banyak informasi yang hilang. Atau dengan kata lain, it used to explain the underlying variance-covariance structure of the large data set of variables through a few linear combination of these variables. PCA sangat dipengaruhi oleh kehadiran outlier karena didasarkan pada matriks kovarian yang sensitive terhadap outlier. Oleh karena itu, pada analisis ini akan digunakan PCA yang robust terhadap outlier yaitu ROBPCA atau PCA Hubert. Selanjutnya, dari Principal Component yang terbentuk digunakan sebagai input (masukan) untuk cluster analysis dengan metode Clara (Clustering Large Area). Clustering Large Area merupakan salah satu metode k-medoids yang robust terhadap outlier dan baik digunakan pada data dalam jumlah besar. Dalam studi kasus terhadap variabel penyusun indeks kebahagiaan berdasarkan The World Happiness Report 2018 dengan metode Clara yang menggunakan jarak manhattan didapatkan nilai rata-rata Overall Average Silhouette Width yang terbaik pada 5 cluster.

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15

R. Bornstein. „Professor Alan Robock“. Environmental Software 1, Nr. 2 (September 1986): 132–35. http://dx.doi.org/10.1016/0266-9838(86)90015-8.

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16

Li Lin, Yu, Chien Lung Hsu und Yu Hao Chuang. „A Robust Time-Bound Hierarchical Key Assignment Scheme“. International Journal of Future Computer and Communication 3, Nr. 5 (Oktober 2014): 337–40. http://dx.doi.org/10.7763/ijfcc.2014.v3.322.

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17

Yao Tingting, 姚婷婷, 张波 Zhang Bo, 李鹏飞 Li Pengfei und 柳晓鸣 Liu Xiaoming. „基于鲁棒背景估计的船舶显著性检测“. Laser & Optoelectronics Progress 59, Nr. 8 (2022): 0810008. http://dx.doi.org/10.3788/lop202259.0810008.

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18

Peresada, S., S. Bozhko, S. Kovbasa und Ye Nikonenko. „ROBUST DIRECT FIELD ORIENTED CONTROL OF INDUCTION GENERATOR“. Tekhnichna Elektrodynamika 2021, Nr. 4 (17.06.2021): 14–24. http://dx.doi.org/10.15407/techned2021.04.014.

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A novel and robust field oriented vector control method for standalone induction generators (IG) is presented. The proposed controller exploits the concept of direct field orientation and provides asymptotic rotor flux modulus and DC-link voltage regulations when a DC-load is constant or slowly varying. Flux subsystem, designed using Lyapunov’s second method, has, in contrast to standard structures, closed loop properties and therefore is robust with respect to rotor resistance variations. A decomposition approach on the base of the two-time scale separation of the voltage and torque current dynamics is used for design of the voltage subsystem. The feedback linearizing voltage controller is designed using a steady state IG power balance equation. The resulting quasi-linear dynamics of the voltage control loop allows use of simple controllers tuning procedure and provides an improved dynamic performance for variable speed and flux operation. Results of a comparative experimental study with standard indirect field oriented control are presented. In contrast to existing solutions, the designed controller provides system performances stabilization when speed and flux are varying. It is experimentally shown that a robust field oriented controller ensures robust flux regulation and robust stabilization of the torque current dynamics leading to improved energy efficiency of the electromechanical conversion process. The proposed controller is suitable for energy generation systems with variable speed operation. References 18, figures 8.

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19

Jiang, H., K. Huang, T. Mu, R. Zhang, T. O. Ting und C. Wang. „Robust One-Shot Facial Expression Recognition with Sunglasses“. International Journal of Machine Learning and Computing 6, Nr. 2 (April 2016): 80–86. http://dx.doi.org/10.18178/ijmlc.2016.6.2.577.

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20

Jeong, Seok Kwon, und Tae Eun Kwon. „Robust Linear Quadratic Gaussian Controller Design for Oil Coolers Based on a State Space Model“. Korean Journal of Air-Conditioning and Refrigeration Engineering 31, Nr. 3 (31.03.2019): 130–39. http://dx.doi.org/10.6110/kjacr.2019.31.3.130.

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21

Rongli Liu, Rongli Liu, und Zhongliang Jing Zhongliang Jing. „Robust kernel-based tracking algorithm with background contrasting“. Chinese Optics Letters 10, Nr. 2 (2012): 021001–21003. http://dx.doi.org/10.3788/col201210.021001.

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22

Farmanullah Jan, Farmanullah Jan, Imran Usman Imran Usman und Shahid A. Khan Shahid A. Khan. „Robust iris biometric system for visible wavelength data“. Chinese Optics Letters 11, Nr. 8 (2013): 081501–81507. http://dx.doi.org/10.3788/col201311.081501.

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23

Korobov, V. I., und T. V. Revina. „On Robust Feedback for Systems with Multidimensional Control“. Zurnal matematiceskoj fiziki, analiza, geometrii 13, Nr. 1 (25.03.2017): 35–56. http://dx.doi.org/10.15407/mag13.01.035.

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24

Ahamed, Bulbul, Rasel Ahmed, Khaled Mahmud und Mohammad Nurul Huda. „Robust Phoneme Recognizer at Noise Corrupted Acoustic Environment“. Scholars Journal of Engineering and Technology 4, Nr. 7 (Juli 2016): 308–11. http://dx.doi.org/10.21276/sjet.2016.4.7.3.

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25

Jung, Kang-Mo. „Robust Algorithm for Multiclass Weighted Support Vector Machine“. SIJ Transactions on Advances in Space Research & Earth Exploration 4, Nr. 3 (10.06.2016): 1–5. http://dx.doi.org/10.9756/sijasree/v4i3/0203430402.

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26

Prabhjot, Kaur, Lamba I. M. S und Gosain Anjana. „DOFCM: A Robust Clustering Technique Based upon Density“. International Journal of Engineering and Technology 3, Nr. 3 (2011): 297–303. http://dx.doi.org/10.7763/ijet.2011.v3.241.

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27

Ghaib, Zainab, und Ebtesam N. AlShemmary. „A Robust Segmentation of Non-ideal Iris Images“. Journal of Advanced Research in Dynamical and Control Systems 11, Nr. 10 (31.10.2019): 99–103. http://dx.doi.org/10.5373/jardcs/v11i10/20193011.

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28

Ningrum, Ika Purwanti, Agus Harjoko und Munakhir Mudjosemedi. „Robust Cephalometric Landmark Identification on Cephalometric Downs Analysis“. International Journal of Computer and Electrical Engineering 6, Nr. 2 (2014): 172–75. http://dx.doi.org/10.7763/ijcee.2014.v6.816.

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29

SALIM, Nurul Dayana, Dafizal DERAWI, Hairi ZAMZURI, Yang YI, Kenzo NONAMI und Daisuke IWAKURA. „A212 Robust LQR Attitude Control of Hexarotor UAVs“. Proceedings of the Symposium on the Motion and Vibration Control 2015.14 (2015): 251–56. http://dx.doi.org/10.1299/jsmemovic.2015.14.251.

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30

Liu Fei, 刘飞, 黄瀚霖 Huang Hanlin, 杨恬 Yang Tian, 李文博 Li Wenbo und 杨炀 Yang Yang. „面向狭窄场景的鲁棒多视角配准方法“. Infrared and Laser Engineering 51, Nr. 12 (2022): 20220114. http://dx.doi.org/10.3788/irla20220114.

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31

Nashed, Maged N. F., Mona Eskander und Adel M. Sharaf Life. „A Stand Alone Robust PV-FC-Electrolyzer Utilization Scheme“. International Journal of Engineering Research 4, Nr. 3 (01.03.2015): 145–50. http://dx.doi.org/10.17950/ijer/v4s3/314.

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32

Hong, Phuc Nguyen, Musrrat Ali und Chang Wook Ahn. „Robust Stereo Matching Method for Radiometric Distortion between Images“. International Journal of Computer and Electrical Engineering 7, Nr. 2 (2015): 82–90. http://dx.doi.org/10.17706/ijcee.2015.v7.874.

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33

Goloveshkin, Alexey V., und Stanislav S. Mikhalkovich. „Robust algorithmic binding to arbitrary fragment of program code“. Program Systems: Theory and Applications 13, Nr. 1 (19.02.2022): 35–62. http://dx.doi.org/10.25209/2079-3316-2022-13-1-35-62.

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When solving a task, a programmer actively interacts with a finite set of code fragments. The information about their locations is important for quick navigation, for other developers, and as a kind of documentation. Integrated development environments (IDEs) provide tools for marking code fragments with labels, displaying lists of labels, and using these labels for quick navigation. However, they often lose the correspondence between the label and the marked place when the code is edited, in particular when changes are made outside the IDE. In previous works, the authors propose a tool to be integrated into various IDEs for “binding” to large syntactic entities of a program and building a markup that is robust to code editing. The description of the marked element is built on the basis of the abstract syntax tree (AST) of the program. Later it is used to algorithmically search for the element in an edited code. The search has a success rate from 99 to 100%. This article aims at robust algorithmic binding to an arbitrary section of the code. For binding to a single-line code fragment, we propose an extension of the model describing the marked fragment, and an additional search algorithm. We also propose an algorithm for embedding nodes corresponding to multi-line fragments in an AST. We show that the correctness of the AST is not violated by these embeddings. Bindings to randomly selected lines were made in the code of three large C# projects. Manual check of these lines search results in the edited code has confirmed that the bindings are robust to code editing.

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34

Mhalla, Anis, Mohanned Gaied und Dimitri Lefebre. „Robust control strategies facing disturbances in Railway Transport Networks“. Indian Journal of Science and Technology 14, Nr. 42 (10.11.2021): 3133–43. http://dx.doi.org/10.17485/ijst/v14i42.1181.

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35

Bongulwar, Deepali M., und S. N. Talbar. „Robust Convolutional Neural Network Model For Recognition of Fruits“. Indian Journal of Science and Technology 14, Nr. 45 (05.12.2021): 3318–34. http://dx.doi.org/10.17485/ijst/v14i45.1493.

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36

Priyanka, R., und S. Ramadevi. „A Hyperbolic PDE-ODE System with Delay-Robust Stabilization“. International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (31.08.2018): 1988–90. http://dx.doi.org/10.31142/ijtsrd17157.

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37

Sano, Natsuki, Hideo Suzuki und Masato Koda. „A ROBUST ENSEMBLE LEARNING USING ZERO-ONE LOSS FUNCTION“. Journal of the Operations Research Society of Japan 51, Nr. 1 (2008): 95–110. http://dx.doi.org/10.15807/jorsj.51.95.

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38

Bose, Smarajit, Amita Pal, Anish Mukherjee und Debasmita Das. „Robust Speaker Identification Using Fusion of Features and Classifiers“. International Journal of Machine Learning and Computing 7, Nr. 5 (Oktober 2017): 133–38. http://dx.doi.org/10.18178/ijmlc.2017.7.5.635.

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39

Jianfang Dou, Jianfang Dou, und Jianxun Li Jianxun Li. „Robust image matching based on SIFT and delaunay triangulation“. Chinese Optics Letters 10, s1 (2012): S11001–311005. http://dx.doi.org/10.3788/col201210.s11001.

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40

Dongshan Jiang, Dongshan Jiang, Faxiang Zhang Faxiang Zhang, Wentao Zhang Wentao Zhang, Feng Li Feng Li und Fang Li Fang Li. „Robust 3-component optical fiber accelerometer for seismic monitoring“. Chinese Optics Letters 11, Nr. 2 (2013): 020602–20605. http://dx.doi.org/10.3788/col201311.020602.

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41

Huang, Guoheng, und Chi-Man Pun. „Robust Interactive Segmentation Using Color Histogram and Contourlet Transform“. International Journal of Computer Theory and Engineering 7, Nr. 6 (Dezember 2015): 489–94. http://dx.doi.org/10.7763/ijcte.2015.v7.1007.

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42

Hamdan, Yasir B., und Samah O. Altayeb. „Robust Stability of Networked Control Systems with Communication Constraints“. International Journal of Computer and Communication Engineering 3, Nr. 6 (2014): 388–93. http://dx.doi.org/10.7763/ijcce.2014.v3.355.

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43

OLIINYK, VIACHESLAV, und VOLODYMYR LUKIN. „USE OF SIMILARITY METRICS IN ROBUST TIME DELAY ESTIMATION“. Herald of Khmelnytskyi National University. Technical sciences 319, Nr. 2 (27.04.2023): 224–30. http://dx.doi.org/10.31891/2307-5732-2023-319-1-224-230.

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This paper addresses the task of time delay and direction of arrival estimation for a source of the wideband signal using two sensors with fixed displacement. The peculiarity of the task statement is that a limited time of signal observation is supposed and additive noise is assumed non-Gaussian with a heavy-tail distribution. This leads to a high probability of abnormal estimates for the conventional signal processing method based on cross-correlation. To decrease this probability, it is proposed to reformulate the task of cross-correlation processing to the task of similarity estimation between two data arrays. This allows using different similarity metrics, particularly those that have less sensitivity to outliers in data (impulse noise), and, thus, provide better robustness for non-Gaussian environments typical for several applications of time delay estimation. More than ten different similarity metrics are considered for the model of the symmetric α-stable distribution describing noise properties. It is shown that some metrics including cosine distance, Hellinger, and some others are able to provide sufficiently better accuracy of time delay estimation both in the sense of less RMSE of normal estimates and probability of abnormal estimates for typical values of α and a wide range of γ values for symmetric α-stable distribution.

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44

Muthukrishnan, R., und S. Kalaivani. „Robust Weighted Support Vector Regression Approach for Predictive Modeling“. Indian Journal Of Science And Technology 16, Nr. 30 (14.08.2023): 2287–96. http://dx.doi.org/10.17485/ijst/v16i30.1180.

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45

Samaniego, Angel. „CAPM-alpha estimation with robust regression vs. linear regression“. Análisis Económico 38, Nr. 97 (20.01.2023): 27–37. http://dx.doi.org/10.24275/uam/azc/dcsh/ae/2022v38n97/samaniego.

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46

Reddy, Chappidi Sree Teja, und Geetha Ramalingam. „Analysis and Comparison of Accuracy in Brain Tumor using Berkeley Wavelet Transform and Robust Principal Component Analysis“. E3S Web of Conferences 477 (2024): 00082. http://dx.doi.org/10.1051/e3sconf/202447700082.

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The main objective of this study is to compare Berkeley wavelet transform (BWT) and robust principal component analysis (ROBPCA) techniques in tumor analysis to improve the accuracy of image processing. Based on the sample sizes of BWT (N=16) and ROBPCA (N=16), tumor MR pictures of various brain tumor illnesses have been gathered. Image segmentation has been finished, and textural features have been retrieved using image processing methods. The accuracy and sensitivity of the parameter are taken into consideration by both organizations when evaluating tumor detection and evaluation. The sample size for each group could be determined by maintaining the enrollment ratio at 1, the threshold alpha at 0.05, the g power at 80%, and the confidence interval at 95%. The absence of a statistically significant difference (p = 0.182) between the two groups was verified using an Independent Sample T-test. The accuracy numbers in BWT are 81.5%, while 84% is the accuracy value in ROBPCA. When it comes to brain tumor detection and analysis, ROBPCA has performed well when compared to BWT.

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Hamzenejad, Ali, Saeid Jafarzadeh Ghoushchi und Vahid Baradaran. „Clustering of Brain Tumor Based on Analysis of MRI Images Using Robust Principal Component Analysis (ROBPCA) Algorithm“. BioMed Research International 2021 (31.08.2021): 1–11. http://dx.doi.org/10.1155/2021/5516819.

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Automated detection of brain tumor location is essential for both medical and analytical uses. In this paper, we clustered brain MRI images to detect tumor location. To obtain perfect results, we presented an unsupervised robust PCA algorithm to clustered images. The proposed method clusters brain MR image pixels to four leverages. The algorithm is implemented for five brain diseases such as glioma, Huntington, meningioma, Pick, and Alzheimer’s. We used ten images of each disease to validate the optimal identification rate. According to the results obtained, 2% of the data in the bad leverage part of the image were determined, which acceptably discerned the tumor. Results show that this method has the potential to detect tumor location for brain disease with high sensitivity. Moreover, results show that the method for the Glioma images has approximately better results than others. However, according to the ROC curve for all selected diseases, the present method can find lesion location.

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Richard, Patrick. „Robust heteroskedasticity-robust tests“. Economics Letters 159 (Oktober 2017): 28–32. http://dx.doi.org/10.1016/j.econlet.2017.07.008.

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Vaswani, Namrata, Thierry Bouwmans, Sajid Javed und Praneeth Narayanamurthy. „Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery“. IEEE Signal Processing Magazine 35, Nr. 4 (Juli 2018): 32–55. http://dx.doi.org/10.1109/msp.2018.2826566.

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50

Wang, Wu, Jinxu Tao, Weiquan Ye, Yongjun Jiang und Zhongfu Ye. „Robust Object Tracking via Multi-block and Sparsity-Based Representation“. International Journal of Knowledge Engineering-IACSIT 1, Nr. 1 (2015): 72–77. http://dx.doi.org/10.7763/ijke.2015.v1.13.

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