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Relevant bibliographies by topics / And Scene Understanding

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Academic literature on the topic 'And Scene Understanding'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "And Scene Understanding"

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Wojek, Christian, Stefan Walk, Stefan Roth, Konrad Schindler, and Bernt Schiele. "Monocular Visual Scene Understanding: Understanding Multi-Object Traffic Scenes." IEEE Transactions on Pattern Analysis and Machine Intelligence 35, no. 4 (2013): 882–97. http://dx.doi.org/10.1109/tpami.2012.174.

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Cooper, Paul R., Lawrence A. Birnbaum, and Matthew E. Brand. "Causal Scene Understanding." Computer Vision and Image Understanding 62, no. 2 (1995): 215–31. http://dx.doi.org/10.1006/cviu.1995.1051.

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Potnis, Abhishek V., Surya S. Durbha, and Rajat C. Shinde. "Semantics-Driven Remote Sensing Scene Understanding Framework for Grounded Spatio-Contextual Scene Descriptions." ISPRS International Journal of Geo-Information 10, no. 1 (2021): 32. http://dx.doi.org/10.3390/ijgi10010032.

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Earth Observation data possess tremendous potential in understanding the dynamics of our planet. We propose the Semantics-driven Remote Sensing Scene Understanding (Sem-RSSU) framework for rendering comprehensive grounded spatio-contextual scene descriptions for enhanced situational awareness. To minimize the semantic gap for remote-sensing-scene understanding, the framework puts forward the transformation of scenes by using semantic-web technologies to Remote Sensing Scene Knowledge Graphs (RSS-KGs). The knowledge-graph representation of scenes has been formalized through the development of a

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Zolghadr, Esfandiar, and Borko Furht. "Context-Based Scene Understanding." International Journal of Multimedia Data Engineering and Management 7, no. 1 (2016): 22–40. http://dx.doi.org/10.4018/ijmdem.2016010102.

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Context plays an important role in performance of object detection. There are two popular considerations in building context models for computer vision applications; type of context (semantic, spatial, scale) and scope of the relations (pairwise, high-order). In this paper, a new unified framework is presented that combines multiple sources of context in high-order relations to encode semantical coherence and consistency of the scenes. This framework introduces a new descriptor called context relevance score to model context-based distribution of the response variables and apply it to two dist

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Zhou, Wujie, Sijia Lv, Qiuping Jiang, and Lu Yu. "Deep Road Scene Understanding." IEEE Signal Processing Letters 26, no. 4 (2019): 587–91. http://dx.doi.org/10.1109/lsp.2019.2896793.

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Hoiem, Derek, James Hays, Jianxiong Xiao, and Aditya Khosla. "Guest Editorial: Scene Understanding." International Journal of Computer Vision 112, no. 2 (2015): 131–32. http://dx.doi.org/10.1007/s11263-015-0807-z.

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Qiu, Yue, Yutaka Satoh, Ryota Suzuki, Kenji Iwata, and Hirokatsu Kataoka. "Indoor Scene Change Captioning Based on Multimodality Data." Sensors 20, no. 17 (2020): 4761. http://dx.doi.org/10.3390/s20174761.

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This study proposes a framework for describing a scene change using natural language text based on indoor scene observations conducted before and after a scene change. The recognition of scene changes plays an essential role in a variety of real-world applications, such as scene anomaly detection. Most scene understanding research has focused on static scenes. Most existing scene change captioning methods detect scene changes from single-view RGB images, neglecting the underlying three-dimensional structures. Previous three-dimensional scene change captioning methods use simulated scenes consi

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Bandera Rubio, Antonio J., Jorge Dias, and Francisco Escolano. "Scene understanding and behaviour analysis." Pattern Recognition Letters 34, no. 7 (2013): 711–12. http://dx.doi.org/10.1016/j.patrec.2013.01.009.

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Gould, Stephen, and Xuming He. "Scene understanding by labeling pixels." Communications of the ACM 57, no. 11 (2014): 68–77. http://dx.doi.org/10.1145/2629637.

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Blaschof, W. F., and T. Caelli. "Scene understanding by rule evaluation." IEEE Transactions on Pattern Analysis and Machine Intelligence 19, no. 11 (1997): 1284–88. http://dx.doi.org/10.1109/34.632987.

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Dissertations / Theses on the topic "And Scene Understanding"

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Zhu, Shanshan, and 朱珊珊. "Using semantic sub-scenes to facilitate scene categorization and understanding." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/206459.

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This thesis proposes to learn the absent cognitive element in conventional scene categorization methods: sub-scenes, and use them to better categorize and understand scenes. In scene categorization, it has been observed that the problem of ambiguity occurs when treating the scene as a whole. Scene ambiguity arises from when a similar set of sub-scenes are arranged differently to compose different scenes, or when a scene literally contains several categories. However, these ambiguities can be discerned by the knowledge of sub-scenes. Thus, it is worthy to study sub-scenes and use them to better

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Satkin, Scott. "Data-Driven Geometric Scene Understanding." Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/280.

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In this thesis, we describe a data-driven approach to leverage repositories of 3D models for scene understanding. Our ability to relate what we see in an image to a large collection of 3D models allows us to transfer information from these models, creating a rich understanding of the scene. We develop a framework for auto-calibrating a camera, rendering 3D models from the viewpoint an image was taken, and computing a similarity measure between each 3D model and an input image. We demonstrate this data-driven approach in the context of geometry estimation and show the ability to find the identi

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Xu, Philippe. "Information fusion for scene understanding." Thesis, Compiègne, 2014. http://www.theses.fr/2014COMP2153/document.

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La compréhension d'image est un problème majeur de la robotique moderne, la vision par ordinateur et l'apprentissage automatique. En particulier, dans le cas des systèmes avancés d'aide à la conduite, la compréhension de scènes routières est très importante. Afin de pouvoir reconnaître le grand nombre d’objets pouvant être présents dans la scène, plusieurs capteurs et algorithmes de classification doivent être utilisés. Afin de pouvoir profiter au mieux des méthodes existantes, nous traitons le problème de la compréhension de scènes comme un problème de fusion d'informations. La combinaison d'

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Schulz, Kristle Cory. "Automating Model Construction for Scene Understanding." Thesis, The University of Arizona, 2014. http://hdl.handle.net/10150/321949.

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Ladicky, Lubor. "Global structured models towards scene understanding." Thesis, Oxford Brookes University, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.543818.

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Park, Hyun Soo. "Social Scene Understanding from Social Cameras." Research Showcase @ CMU, 2014. http://repository.cmu.edu/dissertations/453.

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In social scenes, humans interact with each other by sending visible social signals, such as facial expressions, body gestures, and gaze movements. Social cognition, the ability to perceive, model, and predict such social signals, enables people to understand social interactions and to plan their behavior in accordance with the understanding. Computational social cognition is a necessary function allowing artificial agents to enter the social spaces because it enables a socially acceptable behavior. However, two key challenges preclude developing computational social cognition: (1) the core at

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Diaz, Garcia Raul. "Strong geometric context for scene understanding." Thesis, University of California, Irvine, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10195873.

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<p> Humans are able to recognize objects in a scene almost effortlessly. Our visual system can easily handle ambiguous settings, like partial occlusions or large variations in viewpoint. One hypothesis that explains this ability is that we process the scene as a global instance. Using global contextual reasoning (e.g., a car sits on a road, but not on a building facade) can constrain interpretations of objects to plausible, coherent precepts. This type of reasoning has been explored in Computer Vision using weak 2D context, mostly extracted from monocular cues. In this thesis, we explore the b

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Khan, Aamir. "Scene understanding by robotic interactive perception." Thesis, University of Glasgow, 2018. http://theses.gla.ac.uk/30773/.

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This thesis presents a novel and generic visual architecture for scene understanding by robotic interactive perception. This proposed visual architecture is fully integrated into autonomous systems performing object perception and manipulation tasks. The proposed visual architecture uses interaction with the scene, in order to improve scene understanding substantially over non-interactive models. Specifically, this thesis presents two experimental validations of an autonomous system interacting with the scene: Firstly, an autonomous gaze control model is investigated, where the vision sensor d

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Dillon, Craig. "A theory of scene understanding and object recognition." Curtin University of Technology, School of Computing, 1996. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=11179.

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This dissertation presents a new approach to image interpretation which can produce hierarchical descriptions of visually sensed scenes based on an incrementally learnt hierarchical knowledge base. Multiple segmentation and labelling hypotheses are generated with local constraint satisfaction being achieved through a hierarchical form of relaxation labelling. The traditionally unidirectional segmentation-matching process is recast into a dynamic closed-loop system where the current interpretation state is used to drive the lower level image processing functions. The theory presented in this di

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Bohg, Jeannette. "Multi-Modal Scene Understanding for Robotic Grasping." Doctoral thesis, KTH, Datorseende och robotik, CVAP, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-49062.

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Current robotics research is largely driven by the vision of creatingan intelligent being that can perform dangerous, difficult orunpopular tasks. These can for example be exploring the surface of planet mars or the bottomof the ocean, maintaining a furnace or assembling a car. They can also be more mundane such as cleaning an apartment or fetching groceries. This vision has been pursued since the 1960s when the first robots were built. Some of the tasks mentioned above, especially those in industrial manufacturing, arealready frequently performed by robots. Others are still completelyout of

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Books on the topic "And Scene Understanding"

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Schauerte, Boris. Multimodal Computational Attention for Scene Understanding and Robotics. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33796-8.

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Battles, Rodney A. Night games!: A guide to understanding and enjoying the nightclub and bar scene. Brown Books, 2002.

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Sagerer, Gerhard. Semantic Networks for Understanding Scenes. Springer US, 1997.

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Heinrich, Niemann, ed. Semantic networks for understanding scenes. Plenum Press, 1997.

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Sagerer, Gerhard, and Heinrich Niemann. Semantic Networks for Understanding Scenes. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4899-1913-7.

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Denhard, Janet. Scent sense: A guide to understanding fragrances. Element Children's Books, 1998.

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Warren, Raymond. Opera workshop: Studies in understanding and interpretation. Scolar Press, 1995.

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Groff, Elizabeth (Elizabeth R.) and Yang Sue-Ming, eds. The criminology of place: Street segments and our understanding of the crime problem. Oxford University Press, 2012.

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Alent'eva, Tat'yana. Public opinion in the United States on the eve of the Civil war (1850-1861), was. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1068789.

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The monograph first examines American public opinion as a major factor of social and political life in the period of the maturing of the Civil war (1861-1865 gg.). Special value it is given by the study of the struggle in the South and in the North, consideration of the process of formation of two socio-cultural models.  On the wide canvas of the socio-economic and political history in the monograph analyses the state and development of public opinion in the United States, sequentially from the compromise of 1850, a small civil war in Kansas, the uprising of John brown, of the maturing o

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Multimodal Scene Understanding. Elsevier, 2019. http://dx.doi.org/10.1016/c2018-0-01791-0.

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Book chapters on the topic "And Scene Understanding"

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Plemenos, Dimitri, and Georgios Miaoulis. "Scene understanding." In Visual Complexity and Intelligent Computer Graphics Techniques Enhancements. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01259-4_3.

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Chen, Chen, Yuzhuo Ren, and C. C. Jay Kuo. "Scene Understanding Datasets." In SpringerBriefs in Electrical and Computer Engineering. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0631-9_2.

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Golfinopoulos, Vassilios S. "Understanding Scenes." In Intelligent Scene Modelling Information Systems. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-92902-4_3.

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Burger, Wilhelm, and Bir Bhanu. "The Qualitative Scene Model." In Qualitative Motion Understanding. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3566-9_7.

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Athira, S., R. Manjusha, and Latha Parameswaran. "Scene Understanding in Images." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47952-1_20.

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Plemenos, Dimitri, and Georgios Miaoulis. "Scene understanding and human intelligence." In Visual Complexity and Intelligent Computer Graphics Techniques Enhancements. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01259-4_7.

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Wang, Wenfeng, Hengjin Cai, Xiangyang Deng, Chenguang Lu, and Limin Zhang. "Pattern Analysis and Scene Understanding." In Interdisciplinary Evolution of the Machine Brain. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4244-6_4.

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Nadeem, Uzair, Syed Afaq Ali Shah, Ferdous Sohel, Roberto Togneri, and Mohammed Bennamoun. "Deep Learning for Scene Understanding." In Handbook of Deep Learning Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11479-4_2.

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Iordache, Livia, Vlad Paunescu, Wonjun Kang, Joonhyung Kwon, Andrei Leica, and ByeongMoon Jeon. "Low-Complexity Scene Understanding Network." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30642-7_22.

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Spehr, Jens. "Scene Understanding for Intelligent Vehicles." In Studies in Systems, Decision and Control. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11325-8_8.

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Conference papers on the topic "And Scene Understanding"

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Ismail, A. S., M. M. Seifelnasr, and Hongxing Guo. "Understanding Indoor Scene." In the 3rd International Conference. ACM Press, 2018. http://dx.doi.org/10.1145/3220162.3220182.

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Aarthi, S., and S. Chitrakala. "Scene understanding — A survey." In 2017 International Conference on Computer, Communication and Signal Processing (ICCCSP). IEEE, 2017. http://dx.doi.org/10.1109/icccsp.2017.7944094.

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Xiao, Jianxiong, Bryan C. Russell, James Hays, Krista A. Ehinger, Aude Oliva, and Antonio Torralba. "Basic level scene understanding." In SIGGRAPH Asia 2012 Technical Briefs. ACM Press, 2012. http://dx.doi.org/10.1145/2407746.2407782.

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Ye, Yuxiang, Yijuan Lu, and Hao Jiang. "Human's Scene Sketch Understanding." In ICMR'16: International Conference on Multimedia Retrieval. ACM, 2016. http://dx.doi.org/10.1145/2911996.2912067.

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Souiai, Mohamed, Claudia Nieuwenhuis, Evgeny Strekalovskiy, and Daniel Cremers. "Convex Optimization for Scene Understanding." In 2013 IEEE International Conference on Computer Vision Workshops (ICCVW). IEEE, 2013. http://dx.doi.org/10.1109/iccvw.2013.131.

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Zhu, Lin, Jie Zhou, and Jingyan Song. "Scene understanding with tri-training." In International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Tianxu Zhang, Carl A. Nardell, Duane D. Smith, and Hangqing Lu. SPIE, 2007. http://dx.doi.org/10.1117/12.750064.

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Shan-shan Zhu and Nelson H. C. Yung. "Sub-scene generation: A step towards complex scene understanding." In 2011 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2011. http://dx.doi.org/10.1109/icme.2011.6011910.

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Kasper, Alexander, Rainer Jakel, and Rudiger Dillmann. "Using spatial relations of objects in real world scenes for scene structuring and scene understanding." In 2011 15th International Conference on Advanced Robotics (ICAR 2011). IEEE, 2011. http://dx.doi.org/10.1109/icar.2011.6088634.

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Ye, Jinwei, Yu Ji, and Jingyi Yu. "Manhattan Scene Understanding via XSlit Imaging." In 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2013. http://dx.doi.org/10.1109/cvpr.2013.18.

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Li, Chi, Han Xiao, Keisuke Tateno, Federico Tombari, Nassir Navab, and Gregory D. Hager. "Incremental scene understanding on dense SLAM." In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2016. http://dx.doi.org/10.1109/iros.2016.7759111.

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Reports on the topic "And Scene Understanding"

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Itti, Laurent, Nader Noori, and Lior Elazary. Neuroscience-Enabled Complex Visual Scene Understanding. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada579652.

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Tunick, Arnold. SpaceTime Environmental Image Information for Scene Understanding. Defense Technical Information Center, 2016. http://dx.doi.org/10.21236/ad1007247.

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Baker, H. H. Building and Using Scene Representation in Image Understanding. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada461044.

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Tunick, Arnold. Mission Driven Scene Understanding: Candidate Model Training and Validation. Defense Technical Information Center, 2016. http://dx.doi.org/10.21236/ad1016729.

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Rosenblum, Mark, and Benny Gothard. A High Fidelity Multi-Sensor Scene Understanding System for Autonomous Navigation. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada444473.

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Golovko, Khrystyna. TRAVEL REPORT BY ALEKSANDER JANTA-POŁCZYNSKI «INTO THE USSR» (1932): FROG PERSPECTIVE. Ivan Franko National University of Lviv, 2021. http://dx.doi.org/10.30970/vjo.2021.50.11091.

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The article analyzes a series of materials by Aleksander Janta-Polczynski «Into the USSR» from Soviet Russia during the in 1932, published on «Wiadomości Literackiе». The purpose of this article is explain the uniqueness of the reporter’s style and personality. We want to emphasize the role of Janta-Polczynski as the pioneer of reportage journalism. He was the first who worked professionally in this position in the full sense of this word. Analyzed the cycle of Alexander Janta-Polczynski from Russia, we can emphasize the scale of the reporter’s trip: in 1932 the journalist made the largest jou

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