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Thematische Bibliographien / Object-Based model / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Object-Based model“

Autor: Grafiati

Veröffentlicht am 25. Mai 2024

Zuletzt aktualisiert am 22. Juni 2025

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1

Biegelbauer, Georg, Markus Vincze, and Walter Wohlkinger. "Model-based 3D object detection." Machine Vision and Applications 21, no. 4 (2008): 497–516. http://dx.doi.org/10.1007/s00138-008-0178-3.

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2

Arumugam, Chamundeswari, and Chitra Babu. "Test Size Estimation for Object Oriented Software Based on Analysis Model." Journal of Software 10, no. 6 (2015): 713–29. http://dx.doi.org/10.17706//jsw.10.6.713-729.

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3

Qi Chen, Qi Chen, Xinyi Gao Qi Chen, Renjie Li Xinyi Gao, and Yong Zhang Renjie Li. "Optimized Object Detection Based on The Improved Lightweight Model Mini Net." 網際網路技術學刊 25, no. 2 (2024): 223–32. http://dx.doi.org/10.53106/160792642024032502005.

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<p>This paper proposes a Mini Net lightweight model that can be used for real-time detection. This model works together with Mini Lower and Mini Higher, which greatly improves the detection efficiency while ensuring the accuracy. The Mini module designs both the batch normalization layer and the excitation function at the front end of the module, which realizes efficient convolution, greatly reduces the amount of parameters and computation, and introduces the nonlinearity brought by more layers in the spatial dimension, which can improve the performance of the module extraction capacity. Based on the Mini convolution module, a multi-stage training strategy is proposed. The first stage makes the system fast and stable. In order to improve the overfitting phenomenon of the system, the second and third stages use finer features to improve the detection of small targets, thereby improving the Model training efficiency and detection accuracy.</p> <p>&nbsp;</p>

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4

Bae, Je-Min. "Analogy-based Reuse of Object Model." KIPS Transactions:PartD 14D, no. 6 (2007): 665–74. http://dx.doi.org/10.3745/kipstd.2007.14-d.6.665.

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5

KIM, SUNGHO, GIJEONG JANG, WANG-HEON LEE, and IN SO KWEON. "COMBINED MODEL-BASED 3D OBJECT RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 07 (2005): 839–52. http://dx.doi.org/10.1142/s0218001405004368.

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This paper presents a combined model-based 3D object recognition method motivated by the robust properties of human vision. The human visual system (HVS) is very efficient and robust in identifying and grabbing objects, in part because of its properties of visual attention, contrast mechanism, feature binding, multiresolution and part-based representation. In addition, the HVS combines bottom-up and top-down information effectively using combined model representation. We propose a method for integrating these aspects under a Monte Carlo method. In this scheme, object recognition is regarded as a parameter optimization problem. The bottom-up process initializes parameters, and the top-down process optimizes them. Experimental results show that the proposed recognition model is feasible for 3D object identification and pose estimation.

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6

Lamdan, Y., J. T. Schwartz, and H. J. Wolfson. "Affine invariant model-based object recognition." IEEE Transactions on Robotics and Automation 6, no. 5 (1990): 578–89. http://dx.doi.org/10.1109/70.62047.

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7

Jang, Dae-Sik, Gye-Young Kim, and Hyung-Il Choi. "Model-based tracking of moving object." Pattern Recognition 30, no. 6 (1997): 999–1008. http://dx.doi.org/10.1016/s0031-3203(96)00128-8.

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8

UENO, Haruki, and Yasumasa OOMORI. "Expert Systems Based on Object Model." Geoinformatics 2, no. 2 (1991): 97–108. http://dx.doi.org/10.6010/geoinformatics1990.2.2_97.

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9

Jalila, A., and D. Jeya Mala. "Object-oriented model-based specification languages." ACM SIGSOFT Software Engineering Notes 39, no. 5 (2014): 1–4. http://dx.doi.org/10.1145/2659118.2659132.

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10

Nixon, D., and R. Lobb. "A fluid-based soft-object model." IEEE Computer Graphics and Applications 22, no. 4 (2002): 68–75. http://dx.doi.org/10.1109/mcg.2002.1016700.

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11

Korn, Matthew R., and Charles R. Dyer. "3-D multiview object representations for model-based object recognition." Pattern Recognition 20, no. 1 (1987): 91–103. http://dx.doi.org/10.1016/0031-3203(87)90020-3.

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12

Corcoran, Padraig. "Topology Based Object Tracking." Mathematical and Computational Applications 24, no. 3 (2019): 84. http://dx.doi.org/10.3390/mca24030084.

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A model for tracking objects whose topological properties change over time is proposed. Such changes include the splitting of an object into multiple objects or the merging of multiple objects into a single object. The proposed model employs a novel formulation of the tracking problem in terms of homology theory whereby 0-dimensional homology classes, which correspond to connected components, are tracked. A generalisation of this model for tracking spatially close objects lying in an ambient metric space is also proposed. This generalisation is particularly suitable for tracking spatial-temporal phenomena such as rain clouds. The utility of the proposed model is demonstrated with respect to tracking communities in a social network and tracking rain clouds in radar imagery.

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13

Board, Oliver, and Kim-Sau Chung. "Object-based unawareness: Axioms." Journal of Mechanism and Institution Design 6, no. 1 (2021): 1–36. http://dx.doi.org/10.22574/jmid.2021.12.001.

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This paper provides foundations for a model of unawareness, called object-based unawareness (OBU) structures, that can be used to distinguish between what an agent is unaware of and what she simply does not know. At an informal level, this distinction plays a key role in a number of papers such as Tirole (2009) and Chung & Fortnow (2016). In this paper, we give the model-theoretic description of OBU structures by showing how they assign truth conditions to every sentence of the formal language used. We then prove a model-theoretic sound and completeness theorem, which characterizes OBU structures in terms of a system of axioms. We then verify that agents in OBU structures do not violate any of the introspection axioms that are generally considered to be necessary conditions for a plausible notion of unawareness. Applications are provided in our companion paper.

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14

Wang, Yanlong, and Jinhua Liu. "Object-oriented Design based Comprehensive Experimental Development of Document Object Model." Advances in Engineering Technology Research 3, no. 1 (2022): 390. http://dx.doi.org/10.56028/aetr.3.1.390.

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JavaScript code using Document Object Model (DOM) can realize the dynamic control of Web pages, which is the important content of the Web development technology course. The application of DOM is very flexible and includes many knowledge points, so it is difficult for students to master. In order to help students to understand each knowledge point and improve their engineering ability to solve practical problems, a DOM comprehensive experiment project similar to blind box is designed and implemented. This experimental project integrates knowledge points such as DOM events, DOM operations, and communication between objects. Practice has proved that running and debugging of the project can help students to understand and master relevant knowledge points.

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15

Jin, Qing Kai, Jun Yi Li, and Yan Zhang. "Object-Oriented Program Tailoring Based on Model." Applied Mechanics and Materials 608-609 (October 2014): 23–30. http://dx.doi.org/10.4028/www.scientific.net/amm.608-609.23.

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For the redundant codes caused by software reuse and rapid development in object-oriented program, the paper proposes a cutting model and designs a cutting scheme based on this model. Firstly, a cutting model is established. Then, static analysis is made for object-oriented codes and the cutting information base of objects to be clipped is established. Lastly, the cutting information base and cutting model is matched to cut the program. A test on a practical java project indicates that the cutting scheme can make the volume of the project reduce by about 21%.

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16

Yahya, Abdelfatah Aref, and Rana Mohamad Idrees Bader. "Distributed Shared Memory Consistency Object-based Model." Journal of Computer Science 3, no. 1 (2007): 57–61. http://dx.doi.org/10.3844/jcssp.2007.57.61.

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17

WANG, Lin, Jianfu CAO, and Chongzhao HAN. "Superquadrics Model-based 3D Object Localization Algorithm." Robot 35, no. 4 (2013): 439. http://dx.doi.org/10.3724/sp.j.1218.2013.00439.

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18

Russell, Alexander F., Stefan Mihalaş, Rudiger von der Heydt, Ernst Niebur, and Ralph Etienne-Cummings. "A model of proto-object based saliency." Vision Research 94 (January 2014): 1–15. http://dx.doi.org/10.1016/j.visres.2013.10.005.

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19

Baoxin Li, R. Chellappa, Qinfen Zheng, and S. Z. Der. "Model-based temporal object verification using video." IEEE Transactions on Image Processing 10, no. 6 (2001): 897–908. http://dx.doi.org/10.1109/83.923286.

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20

Basila, M. R., A. Cinar, and G. Stefanek. "Mobecs: Model-Object Based Expert Control Systems." IFAC Proceedings Volumes 22, no. 8 (1989): 163–68. http://dx.doi.org/10.1016/s1474-6670(17)53352-2.

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21

Mao, Bing, and Li Xie. "An object-based model for cooperative computing." Science in China Series E: Technological Sciences 41, no. 1 (1998): 22–30. http://dx.doi.org/10.1007/bf02916868.

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22

Ri, Changyong, Duho Pak, Cholryong Choe, Suhyang Kim, and Yonghak Sin. "Contextual object categorisation with energy-based model." Journal of Engineering 2017, no. 10 (2017): 566–73. http://dx.doi.org/10.1049/joe.2017.0319.

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23

Wen, Chenglin, Guangfu Zhou, Jingli Gao, Hongwei Li, and Xiaobin Xu. "Object Recognition Based on Improved Context Model." Chinese Journal of Electronics 27, no. 3 (2018): 573–81. http://dx.doi.org/10.1049/cje.2018.03.014.

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24

Nagasaka, Yasumi, Yasushi Mochiduki, Hideyuki Otaki, Yoshio Ishikawa, and Keiichi Watanuki. "Building of Window based on Object-Model." Transactions of the Japan Society of Mechanical Engineers Series C 59, no. 560 (1993): 1321–27. http://dx.doi.org/10.1299/kikaic.59.1321.

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25

Jianping Fan, Xingquan Zhu, and Lide Wu. "Automatic model-based semantic object extraction algorithm." IEEE Transactions on Circuits and Systems for Video Technology 11, no. 10 (2001): 1073–84. http://dx.doi.org/10.1109/76.954494.

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26

Mili, Hafedh, John Sibert, and Yoav Intrator. "An object-oriented model based on relations." Journal of Systems and Software 12, no. 2 (1990): 139–55. http://dx.doi.org/10.1016/0164-1212(90)90076-x.

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27

ANDO, Noriaki, Tetsuo KOTOKU, Olibier LEMAIRE, Kosei KITAGAKI, and Takashi SUEHIRO. "1P1-C23 RT-Component Object Model based on SDD (Super Distributed Object)." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2006 (2006): _1P1—C23_1—_1P1—C23_4. http://dx.doi.org/10.1299/jsmermd.2006._1p1-c23_1.

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28

M, Raviraja Holla. "Synergy of Classical and Model-Based Object-Oriented (OO) Metrics in Reducing Test Costs." Bonfring International Journal of Software Engineering and Soft Computing 4, no. 1 (2014): 01–04. http://dx.doi.org/10.9756/bijsesc.4813.

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29

Hardeep Kaur , Damanpreet Kaur, Hardeep Kaur ,. Damanpreet Kaur. "Dual-Compression Based Model Using the Active Object Detection Model." International Journal of Computer Science Engineering and Information Technology Research 7, no. 4 (2017): 71–80. http://dx.doi.org/10.24247/ijcseitraug201710.

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30

Guan, Zeyu. "Real time object recognition based on YOLO model." Theoretical and Natural Science 28, no. 1 (2023): 137–43. http://dx.doi.org/10.54254/2753-8818/28/20230450.

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With the rapid development of computer technology, the concept of computer vision has been proposed. Since then, many object recognition methods have been developed to lay the foundation for computer vision. Object recognition is vital in various computer vision applications, such as autonomous driving, surveillance systems, robotics, and other areas. The You Only Look Once (YOLO) model has gained significant attention due to its ability to achieve real-time object detection and localization in images and videos. This paper comprehensively reviews real-time object recognition based on the YOLO model. We discuss the YOLO architecture's underlying principles and advantages over traditional object detection methods. Then, according to the article by Joseph Redmon, the inventor of YOLO, the benefits of each version of the YOLO model and the performance optimization compared to the previous work are briefly introduced in the order of release. Furthermore, this paper explores its applications in different domains.

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31

Penurkar, Milind R., and Rekha S. Sugandhi. "OPDSM: A Combinatorial Object-Based and Page-Based DSM Model." International Journal of Computer Applications 1, no. 6 (2010): 5–10. http://dx.doi.org/10.5120/150-271.

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32

Soodamani, R., and Z. Q. Liu. "GA-based learning for a model-based object recognition system." International Journal of Approximate Reasoning 23, no. 2 (2000): 85–109. http://dx.doi.org/10.1016/s0888-613x(99)00036-5.

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33

Chander, Prabha *. Iram Shah. "BACK PROPAGATION NEURAL NETWORK FOR OBJECT DETECTION AND CLASSIFICATION OVER VIDEO DATA." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 6 (2016): 70–81. https://doi.org/10.5281/zenodo.54666.

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The vehicle detection on roads becomes an important task when it comes to the urban surveillance and monitoring the traffic on the roads. The live monitoring of the tasks over the roads requires the specifically designed algorithm for vehicular movement tracking. The vehicular movement tracking enables the vehicle detection model to track the object from the points of its appearance in the video and till its disappearance out of the video. Each vehicular object must be tracked and localized for the efficient classification. The image processing techniques in this paper include the various object detection and classification methods. The neural network has been incorporated for the vehicular detection and classification on the basis of the knowledge-driven application. The accuracy of the object algorithm determines the robustness of the vehicular classification and categorization for the in-depth analysis. The performance of the proposed model has been evaluated after conducting the appropriate series of experiments. The proposed model has been found accurate and efficient on the limited data under the experiments performed.

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34

Biju, Soly Mathew. "Model-Based Software Testing for Object-Oriented Software." E-Learning and Digital Media 5, no. 4 (2008): 485–91. http://dx.doi.org/10.2304/elea.2008.5.4.485.

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35

LIU Xiang, 刘翔, 杨鑫 YANG Xin, and 王蕾 WANG Lei. "Object detection algorithm based on improved codebook model." Chinese Journal of Liquid Crystals and Displays 29, no. 6 (2014): 997–1002. http://dx.doi.org/10.3788/yjyxs20142906.0997.

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36

RUAN, Zhiwei, Guijin WANG, Xinggang LIN, Jing-Hao XUE, and Yong JIANG. "Deformable Part-Based Model Transfer for Object Detection." IEICE Transactions on Information and Systems E97.D, no. 5 (2014): 1394–97. http://dx.doi.org/10.1587/transinf.e97.d.1394.

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37

Qiaorong Zhang Huiyu Ren. "A Computational Model for Object-based Visual Attention." Journal of Convergence Information Technology 6, no. 8 (2011): 23–34. http://dx.doi.org/10.4156/jcit.vol6.issue8.3.

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38

TAJIMA, Fujio, Isao SHIMOYAMA, and Hirofumi MIURA. "Trajectory reconstruction based on an operator-object model." Journal of the Robotics Society of Japan 6, no. 5 (1988): 393–96. http://dx.doi.org/10.7210/jrsj.6.5_393.

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39

Froese, Thomas M., and Boyd C. Paulson. "OPIS: An Object Model-Based Project Information System." Computer-Aided Civil and Infrastructure Engineering 9, no. 1 (1994): 13–28. http://dx.doi.org/10.1111/j.1467-8667.1994.tb00358.x.

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40

Myung Jin Choi, Antonio Torralba, and Alan S. Willsky. "A Tree-Based Context Model for Object Recognition." IEEE Transactions on Pattern Analysis and Machine Intelligence 34, no. 2 (2012): 240–52. http://dx.doi.org/10.1109/tpami.2011.119.

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41

Kaiser, Gail E., and Brent Hailpern. "An object-based programming model for shared data." ACM Transactions on Programming Languages and Systems 14, no. 2 (1992): 201–64. http://dx.doi.org/10.1145/128861.128866.

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42

Yuan Yuan, Sabu Emmanuel, Yuming Fang, and Weisi Lin. "Visual Object Tracking Based on Backward Model Validation." IEEE Transactions on Circuits and Systems for Video Technology 24, no. 11 (2014): 1898–910. http://dx.doi.org/10.1109/tcsvt.2014.2319632.

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43

Kumar, K. S. Vipin, and Sheena Mathew. "Model Based Distributed Testing of Object Oriented Programs." Procedia Computer Science 46 (2015): 859–66. http://dx.doi.org/10.1016/j.procs.2015.02.155.

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44

Polat, Ediz, Mohammed Yeasin, and Rajeev Sharma. "A 2D/3D model-based object tracking framework." Pattern Recognition 36, no. 9 (2003): 2127–41. http://dx.doi.org/10.1016/s0031-3203(03)00041-4.

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45

Ji, Zhangjian, and Weiqiang Wang. "Object tracking based on local dynamic sparse model." Journal of Visual Communication and Image Representation 28 (April 2015): 44–52. http://dx.doi.org/10.1016/j.jvcir.2015.01.008.

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46

Pang, Yu, Xiaosheng Yu, Ying Wang, and Chengdong Wu. "Salient object detection based on novel graph model." Journal of Visual Communication and Image Representation 65 (December 2019): 102676. http://dx.doi.org/10.1016/j.jvcir.2019.102676.

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47

Tinnachote, C., and X. Chen. "An approach for object‐based positional error model." Journal of Spatial Science 50, no. 1 (2005): 1–12. http://dx.doi.org/10.1080/14498596.2005.9635034.

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48

Chu, Wenqing, and Deng Cai. "Deep feature based contextual model for object detection." Neurocomputing 275 (January 2018): 1035–42. http://dx.doi.org/10.1016/j.neucom.2017.09.048.

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49

Riahi, Fatemeh, and Oliver Schulte. "Model-based exception mining for object-relational data." Data Mining and Knowledge Discovery 34, no. 3 (2020): 681–722. http://dx.doi.org/10.1007/s10618-020-00677-w.

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50

Wei, Longsheng, Nong Sang, and Yuehuan Wang. "A biologically inspired object-based visual attention model." Artificial Intelligence Review 34, no. 2 (2010): 109–19. http://dx.doi.org/10.1007/s10462-010-9162-1.

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