Relevant bibliographies by topics / Object-Based model

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Object-Based model'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Object-Based model"

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Biegelbauer, Georg, Markus Vincze, and Walter Wohlkinger. "Model-based 3D object detection." Machine Vision and Applications 21, no. 4 (December 2, 2008): 497–516. http://dx.doi.org/10.1007/s00138-008-0178-3.

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

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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 (March 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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Bae, Je-Min. "Analogy-based Reuse of Object Model." KIPS Transactions:PartD 14D, no. 6 (October 31, 2007): 665–74. http://dx.doi.org/10.3745/kipstd.2007.14-d.6.665.

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

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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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Jalila, A., and D. Jeya Mala. "Object-oriented model-based specification languages." ACM SIGSOFT Software Engineering Notes 39, no. 5 (September 17, 2014): 1–4. http://dx.doi.org/10.1145/2659118.2659132.

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

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Dissertations / Theses on the topic "Object-Based model"

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Uluat, Mehmet Fatih. "Model-based Approach To The Federation Object Model Independence Problem." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/3/12608672/index.pdf.

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One of the promises of High Level Architecture (HLA) is the reusability of simulation components. Although HLA supports reusability to some extent with mechanisms provided by Object Model Template (OMT), when the developer wants to use an existing federate application within another federation with a different Federation Object Model (FOM) problem arises. She usually has to modify the federate code and rebuilt it. There have been some attempts to solve this problem and they, in fact, accomplish this to some extent but usually they fall short of providing flexible but also a complete mapping mechanism. In this work, a model based approach that mainly focuses on Declaration, Object and Federation Management services is explored. The proposed approach makes use of Model Integrated Computing (MIC) and .NET 2.0 technologies by grouping federate transitioning activities into three well-defined phases, namely, modeling, automatic code generation and component generation. As a side product, a .NET 2.0 wrapper to Runtime Infrastructure (RTI) has been developed to help developers create IEEE 1516 compatible .NET 2.0 federates in a programming language independent way.
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Andersson, Peter. "Model based object finding in occluded cluttered environments." Thesis, Umeå University, Department of Computing Science, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-36524.

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The aim of the thesis is object finding in occluded and cluttered environment using computer vision techniques and robot motion. Difficulties of the object finding are 1. finding objects at hidden area and 2. finding unrecognized objects. For solving the difficulties, two methods were developed, one is for finding objects in occluded cluttered environments using model based object finding and the other to increase the robustness in object finding by identifying known objects that are unidentified. The goal was to search occluded areas with the bumblebee2 stereo camera to be able to identify all known objects in the environment by removing all visible known objects To identify known objects SURF [9] was used and to be able to remove the identified objects their location first needed to be localized. To localize the object‘s x and y coordinate the information from SURF [9] was used, and the distance coordinate z is calculated using the depth image from the stereo camera. The method to identify objects the SURF [9] algorithm had missed to identify uses a method to find unknown segments in the environment. By using a push motion on the segments to change their angle it can remove possible light reflections and the object can be identified. The results of this research show that the method can find objects in occluded cluttered areas and it can also identified missed known objects.

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Watanabe, Toyohide, Yuuji Yoshida, and Teruo Fukumura. "Editing model based on the object-oriented approach." IEEE, 1988. http://hdl.handle.net/2237/6930.

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Beis, Jeffrey S. "Indexing without invariants in model-based object recognition." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq25014.pdf.

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黃業新 and Yip-san Wong. "A two-level model-based object recognition technique." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1995. http://hub.hku.hk/bib/B31213807.

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Wong, Yip-san. "A two-level model-based object recognition technique /." Hong Kong : University of Hong Kong, 1995. http://sunzi.lib.hku.hk/hkuto/record.jsp?B14705552.

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Rivas, Diaz Juan Manuel. "Model-based object tracking with an infrared stereo camera." Thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-45442.

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Object tracking has become really important in the field of robotics in the last years. Frequently, the goal is to obtain the trajectory of the tracked target over time and space by acquiring and processing information from the sensors. In this thesis we are interested in tracking objects at a very short range. The primary application of our approach is targeting the domain of object tracking during grasp execution with a hand-in-eye sensor setup. To this end, a promising approach investigated in this work is based on the Leap Motion sensor, which is designed for tracking human hands. However, we are interested in tracking grasped objects thus we need to extend its functionality. The main goal of the thesis is to track the 3D position and orientation of an object from a set of simple primitives (cubes, cylinders, triangles) over a video sequence. That is the reason we have designed and developed two different approaches for tracking objects with the Leap Motion device as stereo vision system.
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Procter, Stephen. "Model-based polyhedral object recognition using edge-triple features." Thesis, University of Surrey, 1998. http://epubs.surrey.ac.uk/843142/.

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While significant progress has been made in the computer vision field over the past decade, and machines capable of performing specialised visual inspection tasks are now being used in many industrial applications, the problem of recognising three-dimensional objects from two-dimensional imagery remains an area of ongoing research. Vision is undoubtedly our most important sense, and solutions to the problem of general three-dimensional machine vision must be found if the long term goal of autonomous robotic agents interacting naturally with humans in the real world is to be realised. In this work the problem of recognising polyhedra from two-dimensional images is investigated. The use of perceptual grouping and intermediate-level geometric features is considered, in particular the "edge-triple" feature. The edge-triple feature consists of three connected straight edges of an object, projecting to a triple of connected lines in the image, and can be used as a key feature, or indexing primitive, in model-based object recognition. The geometric constraints provided by matching such a configuration of image lines to an edge-triple are sufficient to uniquely determine the pose of the object. A probabilistic analysis of the edge-triple feature is performed, and a method for computing the probability densities of the angles formed by the projections of object edges under parallel projection is developed. These probabilities are used to prioritise the processing of potential model/scene feature matches produced by the hypothesis generation stage of a polyhedral object recognition system, substantially increasing the efficiency of the verification stage of the recognition while imposing negligible computational and storage penalties on the method. A new polyhedral object recognition system based on geometric hashing is implemented using edge-triple features. The method relies on extensive preprocessing of object models to encode invariant object data in a hash table. By performing as much of the object analysis as possible off-line, the efficiency of the actual recognition stage is maximised, at the expense of heavy demands on memory due to the large amount of data stored in the hash tables. However, the memory requirements of our edge-triple method are lower than those of conventional geometric hashing algorithms. Additionally, since our method employs lines and line groupings as key features rather than sets of interest points, our method is less susceptible to noise in the imaging and feature extraction stages than conventional geometric hashing. The validity of these assertions is demonstrated by extensive testing and evaluation of the method using both synthetic and real image data. It is demonstrated that the accuracy of pose estimates produced by the method is commensurate with theoretical predictions based on algorithm parameters and the expected errors in the image feature extraction. Since the projection from three-dimensional space to a single two-dimensional image necessarily involves a loss of information, the question of combining information from several images is addressed. A multi-view viewpoint consistency constraint is proposed, enabling the compatibility of recognition hypotheses from several viewpoints to be confirmed prior to the computationally expensive pose determination stage. The extra constraints provided by a multi-view analysis increase the reliability and robustness of the recognition system, while the consistency constraint helps to maintain the efficiency of the system. An active method to determine the complete three-dimensional structure of an edge-triple feature from two images is described. Finally, the limitations of the methods proposed and potential solutions to these shortcomings are discussed. Potential directions for future research are proposed.
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Yeung, Stephen Siu Kau. "Model-based tactile object recognition using pseudo-random encoding." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1996. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/nq21021.pdf.

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Guo, Jing-Ming, Nguyen Van Thinh, and Hua Lee. "Moving Object Detection Based on Ordered Dithering Codebook Model." International Foundation for Telemetering, 2014. http://hdl.handle.net/10150/577451.

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ITC/USA 2014 Conference Proceedings / The Fiftieth Annual International Telemetering Conference and Technical Exhibition / October 20-23, 2014 / Town and Country Resort & Convention Center, San Diego, CA
This paper presents an effective multi-layer background modeling method to detect moving objects by exploiting the advantage of novel distinctive features and hierarchical structure of the Codebook (CB) model. In the block-based structure, the mean-color feature within a block often does not contain sufficient texture information, causing incorrect classification especially in large block size layers. Thus, the Binary Ordered Dithering (BOD) feature becomes an important supplement to the mean RGB feature In summary, the uniqueness of this approach is the incorporation of the halftoning scheme with the codebook model for superior performance over the existing methods.
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Books on the topic "Object-Based model"

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Dawson, K. M. Model-based 3-D object recognition using scalartransform descriptors. Dublin: Trinity College, Department of Computer Science, 1991.

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Darawish, Issam. An object-based analysis and model of on-line auctions. Ottawa: National Library of Canada, 1999.

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Pop, Adrian. Integrated model-driven development environments for equation-based object-oriented languages. Linko ping: Department of Computer and Information Science, Linko pings universitet, 2008.

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Li, Chen-Kuo. AN/SLQ-32 EW system model: An expandable, object-oriented, process-based simulation. Monterey, Calif: Naval Postgraduate School, 1992.

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Adeli, Hojjat. Construction scheduling, cost optimization, and management: A new model based on neurocomputing and object technologies. London: Spon Press, 2001.

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Short-term object relations couples therapy: The 5-step model. New York, NY: Brunner-Routledge, 2003.

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Han, Yanbo. Software infrastructure for configurable workflow systems: A model-driven approach based on higher order object nets and CORBA. Berlin: Wissenschaft und Technik, 1997.

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1935-, Lasker G. E., International Institute for Advanced Studies in Systems Research and Cybernetics., and International Conference on Systems Research, Informatics and Cybernetics (9th : 1997 : Baden-Baden, Germany), eds. Advances in database and expert systems: Design issues, data mining, object-oriented databases, generic model for knowledge bases, rapid database prototyping, knowledge-based tools in decision support systems. Windsor, Ont: International Institute for Advanced Studies in Systems Research and Cybernetics, 1997.

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Ciancarini, Paolo, Oscar Nierstrasz, and Akinori Yonezawa, eds. Object-Based Models and Languages for Concurrent Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-59450-7.

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Rasch, Jochen. On value based identification in object oriented data models. Kiel: Inst. für Informatik und Praktische Mathematik, 1998.

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Book chapters on the topic "Object-Based model"

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Sun, Min, and Silvio Savarese. "Model-Based Object Recognition." In Computer Vision, 488–92. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_334.

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Zhou, Chunbo, and Atsumi Imamiya. "Object-based Linear Undo model." In Human-Computer Interaction INTERACT ’97, 252–59. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-0-387-35175-9_43.

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Li, Zhongmin, and Zhanwu Yu. "Object-Based Storage Model for Object-Oriented Database." In Lecture Notes in Computer Science, 354–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74784-0_36.

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Ma, Lili, Jian Cheng, Jing Liu, Jinqiao Wang, and Hanqing Lu. "Visual Attention Model Based Object Tracking." In Advances in Multimedia Information Processing - PCM 2010, 483–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15696-0_45.

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Huang, Xiaolei, Zhen Qian, Rui Huang, and Dimitris Metaxas. "Deformable-Model Based Textured Object Segmentation." In Lecture Notes in Computer Science, 119–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11585978_9.

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Sun, Min, and Silvio Savarese. "Model-Based Object Recognition: Traditional Approach." In Computer Vision, 1–5. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-03243-2_334-1.

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Lienhardt, Michaël, Mario Bravetti, and Davide Sangiorgi. "An Object Group-Based Component Model." In Leveraging Applications of Formal Methods, Verification and Validation. Technologies for Mastering Change, 64–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34026-0_6.

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Sun, Min, and Silvio Savarese. "Model-Based Object Recognition: Traditional Approach." In Computer Vision, 807–12. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63416-2_334.

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Süß, Jörn Guy, Tim McComb, Soon-Kyeong Kim, Luke Wildman, and Geoffrey Watson. "MDA-Based Re-engineering with Object-Z." In Model Driven Engineering Languages and Systems, 291–305. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11880240_21.

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Rumpe, Bernhard. "Model-Based Testing of Object-Oriented Systems." In Formal Methods for Components and Objects, 380–402. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39656-7_16.

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Conference papers on the topic "Object-Based model"

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Klare, Heiko, Timur Saglam, Erik Burger, and Ralf Reussner. "Applying Metamodel-based Tooling to Object-oriented Code." In 7th International Conference on Model-Driven Engineering and Software Development. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007246202170228.

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Klare, Heiko, Timur Saglam, Erik Burger, and Ralf Reussner. "Applying Metamodel-based Tooling to Object-oriented Code." In 7th International Conference on Model-Driven Engineering and Software Development. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007246202190230.

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Muller, A., M. Manz, M. Himmelsbach, and H. J. Wunsche. "A model-based object following system." In 2009 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2009. http://dx.doi.org/10.1109/ivs.2009.5164285.

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PHAM, TRONG TON, AUGUSTIN LUX, and THI THANH HAI TRAN. "GRAPH-BASED MODEL FOR OBJECT RECOGNITION." In Proceedings of the First International Conference on Theories and Applications of Computer Science 2006. WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812772671_0006.

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Shimizu, Atsushi, and Shih-Fu Chang. "Image-object extraction using a genetic-programming-based object model." In Electronic Imaging, edited by Bhaskaran Vasudev, T. Russell Hsing, Andrew G. Tescher, and Robert L. Stevenson. SPIE, 2000. http://dx.doi.org/10.1117/12.382978.

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De-Kai Huang, Kwang-Yu Chen, and Shyi-Chyi Cheng. "Video object detection by model-based tracking." In 2013 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2013. http://dx.doi.org/10.1109/iscas.2013.6572358.

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Geng, Wenjing, Shuzhen Li, Tongwei Ren, and Gangshan Wu. "Object proposals using SVM-based integrated model." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727741.

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Thornham, A., C. J. Taylor, and D. Cooper. "Object Cues for Model-based Image Interpretation." In Alvey Vision Conference 1988. Alvey Vision Club, 1988. http://dx.doi.org/10.5244/c.2.9.

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Armbruster, Walter. "Model-based object recognition in range imagery." In SPIE Europe Security + Defence, edited by David A. Huckridge and Reinhard R. Ebert. SPIE, 2009. http://dx.doi.org/10.1117/12.830299.

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Paulus, Dietrich W., Andraes Winzen, and Heinrich Niemann. "Knowledge-based object recognition and model generation." In Electronic Imaging Device Engineering, edited by Donald W. Braggins. SPIE, 1993. http://dx.doi.org/10.1117/12.164884.

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Reports on the topic "Object-Based model"

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Cyganski, David, R. F. Vaz, and J. A. Orr. Model-Based 3-D Object Identification. Fort Belvoir, VA: Defense Technical Information Center, March 1998. http://dx.doi.org/10.21236/ada344653.

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Chang, Huey, Katsushi Ikeuchi, and Takeo Kanade. Model-Based Vision System by Object-Oriented Programming. Fort Belvoir, VA: Defense Technical Information Center, February 1988. http://dx.doi.org/10.21236/ada195819.

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Michaelis, Stephen. A Model of Suicidal Behavior In Latency Age Children Based on Developmental Object Relations Theory. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.2934.

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Heckel, Jeffery S. Building Composer: The Development of an Object Model for Facility Planning and Design Based on Customer Criteria. Fort Belvoir, VA: Defense Technical Information Center, November 2002. http://dx.doi.org/10.21236/ada409160.

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Yaroshchuk, Svitlana O., Nonna N. Shapovalova, Andrii M. Striuk, Olena H. Rybalchenko, Iryna O. Dotsenko, and Svitlana V. Bilashenko. Credit scoring model for microfinance organizations. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3683.

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The purpose of the work is the development and application of models for scoring assessment of microfinance institution borrowers. This model allows to increase the efficiency of work in the field of credit. The object of research is lending. The subject of the study is a direct scoring model for improving the quality of lending using machine learning methods. The objective of the study: to determine the criteria for choosing a solvent borrower, to develop a model for an early assessment, to create software based on neural networks to determine the probability of a loan default risk. Used research methods such as analysis of the literature on banking scoring; artificial intelligence methods for scoring; modeling of scoring estimation algorithm using neural networks, empirical method for determining the optimal parameters of the training model; method of object-oriented design and programming. The result of the work is a neural network scoring model with high accuracy of calculations, an implemented system of automatic customer lending.
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Zhang, Caiyun, David Brodylo, Mizanur Rahman, Md Atiqur Rahman, Thomas Douglas, and Xavier Comas. Using an object-based machine learning ensemble approach to upscale evapotranspiration measured from eddy covariance towers in a subtropical wetland. Engineer Research and Development Center (U.S.), January 2024. http://dx.doi.org/10.21079/11681/48056.

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Accurate prediction of evapotranspiration (ET) in wetlands is critical for understanding the coupling effects of water, carbon, and energy cycles in terrestrial ecosystems. Multiple years of eddy covariance (EC) tower ET measurements at five representative wetland ecosystems in the subtropical Big Cypress National Preserve (BCNP), Florida (USA) provide a unique opportunity to assess the performance of the Moderate Resolution Imaging Spectroradiometer (MODIS) ET operational product MOD16A2 and upscale tower measured ET to generate local/regional wetland ET maps. We developed an object-based machine learning ensemble approach to evaluate and map wetland ET by linking tower measured ET with key predictors from MODIS products and meteorological variables. The results showed MOD16A2 had poor performance in characterizing ET patterns and was unsatisfactory for estimating ET over four wetland communities where Nash-Sutcliffe model Efficiency (NSE) was less than 0.5. In contrast, the site-specific machine learning ensemble model had a high predictive power with a NSE larger than 0.75 across all EC sites. We mapped the ET rate for two distinctive seasons and quantified the prediction diversity to identify regions easier or more challenging to estimate from model-based analyses. An integration of MODIS products and other datasets through the machine learning upscaling paradigm is a promising tool for local wetland ET mapping to guide regional water resource management.
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Asari, Vijayan, Paheding Sidike, Binu Nair, Saibabu Arigela, Varun Santhaseelan, and Chen Cui. PR-433-133700-R01 Pipeline Right-of-Way Automated Threat Detection by Advanced Image Analysis. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), December 2015. http://dx.doi.org/10.55274/r0010891.

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A novel algorithmic framework for the robust detection and classification of machinery threats and other potentially harmful objects intruding onto a pipeline right-of-way (ROW) is designed from three perspectives: visibility improvement, context-based segmentation, and object recognition/classification. In the first part of the framework, an adaptive image enhancement algorithm is utilized to improve the visibility of aerial imagery to aid in threat detection. In this technique, a nonlinear transfer function is developed to enhance the processing of aerial imagery with extremely non-uniform lighting conditions. In the second part of the framework, the context-based segmentation is developed to eliminate regions from imagery that are not considered to be a threat to the pipeline. Context based segmentation makes use of a cascade of pre-trained classifiers to search for regions that are not threats. The context based segmentation algorithm accelerates threat identification and improves object detection rates. The last phase of the framework is an efficient object detection model. Efficient object detection �follows a three-stage approach which includes extraction of the local phase in the image and the use of local phase characteristics to locate machinery threats. The local phase is an image feature extraction technique which partially removes the lighting variance and preserves the edge information of the object. Multiple orientations of the same object are matched and the correct orientation is selected using feature matching by histogram of local phase in a multi-scale framework. The classifier outputs locations of threats to pipeline.�The advanced automatic image analysis system is intended to be capable of detecting construction equipment along the ROW of pipelines with a very high degree of accuracy in comparison with manual threat identification by a human analyst. �
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8

Tkachuk, Viktoriia V., Vadym P. Shchokin, and Vitaliy V. Tron. The Model of Use of Mobile Information and Communication Technologies in Learning Computer Sciences to Future Professionals in Engineering Pedagogy. [б. в.], November 2018. http://dx.doi.org/10.31812/123456789/2668.

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Research goal: the research is aimed at developing a model of use of mobile ICT in learning Computer Sciences to future professionals in Engineering Pedagogy. Object of research is the model of use of mobile ICT in learning Computer Sciences to future professionals in Engineering Pedagogy. Results of the research: the developed model of use of mobile ICT as tools of learning Computer Sciences to future professionals in Engineering Pedagogy is based on the competency-based, person-centered and systemic approaches considering principles of vocational education, general didactic principles, principles of Computer Science learning, and principles of mobile learning. It also takes into account current conditions and trends of mobile ICT development. The model comprises four blocks: the purpose-oriented block, the content-technological block, the diagnostic block and the result-oriented block. According to the model, the learning content of Computer Sciences consists of 5 main units: 1) Fundamentals of Computer Science; 2) Architecture of Modern Computers; 3) Fundamentals of Algorithmization and Programming; 4) Software of Computing Systems; 5) Computer Technologies in the Professional Activity of Engineer-pedagogues.
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9

Yan, Yujie, and Jerome F. Hajjar. Automated Damage Assessment and Structural Modeling of Bridges with Visual Sensing Technology. Northeastern University, May 2021. http://dx.doi.org/10.17760/d20410114.

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Recent advances in visual sensing technology have gained much attention in the field of bridge inspection and management. Coupled with advanced robotic systems, state-of-the-art visual sensors can be used to obtain accurate documentation of bridges without the need for any special equipment or traffic closure. The captured visual sensor data can be post-processed to gather meaningful information for the bridge structures and hence to support bridge inspection and management. However, state-of-the-practice data postprocessing approaches require substantial manual operations, which can be time-consuming and expensive. The main objective of this study is to develop methods and algorithms to automate the post-processing of the visual sensor data towards the extraction of three main categories of information: 1) object information such as object identity, shapes, and spatial relationships - a novel heuristic-based method is proposed to automate the detection and recognition of main structural elements of steel girder bridges in both terrestrial and unmanned aerial vehicle (UAV)-based laser scanning data. Domain knowledge on the geometric and topological constraints of the structural elements is modeled and utilized as heuristics to guide the search as well as to reject erroneous detection results. 2) structural damage information, such as damage locations and quantities - to support the assessment of damage associated with small deformations, an advanced crack assessment method is proposed to enable automated detection and quantification of concrete cracks in critical structural elements based on UAV-based visual sensor data. In terms of damage associated with large deformations, based on the surface normal-based method proposed in Guldur et al. (2014), a new algorithm is developed to enhance the robustness of damage assessment for structural elements with curved surfaces. 3) three-dimensional volumetric models - the object information extracted from the laser scanning data is exploited to create a complete geometric representation for each structural element. In addition, mesh generation algorithms are developed to automatically convert the geometric representations into conformal all-hexahedron finite element meshes, which can be finally assembled to create a finite element model of the entire bridge. To validate the effectiveness of the developed methods and algorithms, several field data collections have been conducted to collect both the visual sensor data and the physical measurements from experimental specimens and in-service bridges. The data were collected using both terrestrial laser scanners combined with images, and laser scanners and cameras mounted to unmanned aerial vehicles.
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Shyshkina, Mariya P. The Problems of Personnel Training for STEM Education in the Modern Innovative Learning and Research Environment. [б. в.], November 2018. http://dx.doi.org/10.31812/123456789/2664.

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The aim of the article is to describe the problems of personnel training that arise in view of extension of the STEM approach to education, development of innovative technologies, in particular, virtualization, augmented reality, the use of ICT outsourcing in educational systems design. The object of research is the process of formation and development of the educational and scientific environment of educational institution. The subject of the study is the formation and development of the cloud-based learning and research environment for STEM education. The methods of research are: the analysis of publications on the problem; generalization of domestic and foreign experience; theoretical analysis, system analysis, systematization and generalization of research facts and laws for the development and design of the model of the cloud-based learning environment, substantiation of the main conclusions. The results of the research are the next: the concepts and the model of the cloud-based environment of STEM education is substantiated, the problems of personnel training at the present stage are outlined.
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