Relevant bibliographies by topics / Change detection

Contents

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

Academic literature on the topic 'Change detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Change detection"

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Yang, Le, Yiming Chen, Shiji Song, Fan Li, and Gao Huang. "Deep Siamese Networks Based Change Detection with Remote Sensing Images." Remote Sensing 13, no. 17 (2021): 3394. http://dx.doi.org/10.3390/rs13173394.

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Although considerable success has been achieved in change detection on optical remote sensing images, accurate detection of specific changes is still challenging. Due to the diversity and complexity of the ground surface changes and the increasing demand for detecting changes that require high-level semantics, we have to resort to deep learning techniques to extract the intrinsic representations of changed areas. However, one key problem for developing deep learning metho for detecting specific change areas is the limitation of annotated data. In this paper, we collect a change detection datas
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Javed, Aisha, Sejung Jung, Won Hee Lee, and Youkyung Han. "Object-Based Building Change Detection by Fusing Pixel-Level Change Detection Results Generated from Morphological Building Index." Remote Sensing 12, no. 18 (2020): 2952. http://dx.doi.org/10.3390/rs12182952.

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Change detection (CD) is an important tool in remote sensing. CD can be categorized into pixel-based change detection (PBCD) and object-based change detection (OBCD). PBCD is traditionally used because of its simple and straightforward algorithms. However, with increasing interest in very-high-resolution (VHR) imagery and determining changes in small and complex objects such as buildings or roads, traditional methods showed limitations, for example, the large number of false alarms or noise in the results. Thus, researchers have focused on extending PBCD to OBCD. In this study, we proposed a m
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Kennette, Lynne N., Lee H. Wurm, and Lisa R. Van Havermaet. "Change detection." Mental Lexicon 5, no. 1 (2010): 47–86. http://dx.doi.org/10.1075/ml.5.1.03ken.

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A version of the change-detection paradigm was used to examine Good-Enough Representation (Ferreira, Bailey, & Ferraro, 2002). Participants read sentence pairs where a subject noun (e.g., flower) could change to a Superordinate (e.g., plant), Subordinate (e.g., rose), or an Unrelated (e.g., prince) noun. The task was completed cross-linguistically for bilinguals, where the first sentence appeared in English (L1) and the second in French (L2). Linguistic focus was also manipulated. Change detection was extremely high in all conditions in the monolingual sample. In the bilingual sample, focu
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Rensink, Ronald A. "Change Detection." Annual Review of Psychology 53, no. 1 (2002): 245–77. http://dx.doi.org/10.1146/annurev.psych.53.100901.135125.

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Politz, Florian, Monika Sester, and Claus Brenner. "Building Change Detection of Airborne Laser Scanning and Dense Image Matching Point Clouds using Height and Class Information." AGILE: GIScience Series 2 (June 4, 2021): 1–14. http://dx.doi.org/10.5194/agile-giss-2-10-2021.

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Abstract. Detecting changes is an important task to update databases and find irregularities in spatial data. Every couple of years, national mapping agencies (NMAs) acquire nation-wide point cloud data from Airborne Laser Scanning (ALS) as well as from Dense Image Matching (DIM) using aerial images. Besides deriving several other products such as Digital Elevation Models (DEMs) from them, those point clouds also offer the chance to detect changes between two points in time on a large scale. Buildings are an important object class in the context of change detection to update cadastre data. As
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Tse, P. U., D. L. Sheinberg, and N. K. Logothetis. "Attentional Enhancement Opposite a Peripheral Flash Revealed Using Change Blindness." Psychological Science 14, no. 2 (2003): 91–99. http://dx.doi.org/10.1111/1467-9280.t01-1-01425.

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We describe a new method for mapping spatial attention that reveals a pooling of attention in the hemifield opposite a peripheral flash. Our method exploits the fact that a brief full-field blank can interfere with the detection of changes in a scene that occur during the blank. Attending to the location of a change, however, can overcome this change blindness, so that changes are detected. The likelihood of detecting a new element in a scene therefore provides a measure of the occurrence of attention at that element's location. Using this measure, we mapped how attention changes in response t
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Perry, Kimberly, Matthew Pacailler, and Mark W. Scerbo. "The Impact of Natural Visual Interruptions and Cueing on Detecting Changes in Dynamic Scenes." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 66, no. 1 (2022): 1240–44. http://dx.doi.org/10.1177/1071181322661398.

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The goal of the present study was to examine how naturalistic interruptions (head turns) and cueing affect change detection within dynamic scenes. Based on the memory for goals (Altmann & Trafton, 2002) and visual memory theories (Hollingsworth & Henderson, 2001), participants monitoring videos were expected to detect fewer target changes when interrupted than without interruptions. Additionally, reliable cues that provided information about the target were expected to improve target detection compared to neutral cues (Logan, 1996; Posner, Snyder, & Davidson, 1980). Undergraduate s
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Cho, Kyusik, Dong Yeop Kim, and Euntai Kim. "Zero-Shot Scene Change Detection." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 3 (2025): 2509–17. https://doi.org/10.1609/aaai.v39i3.32253.

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We present a novel, training-free approach to scene change detection. Our method leverages tracking models, which inherently perform change detection between consecutive frames of video by identifying common objects and detecting new or missing objects. Specifically, our method takes advantage of the change detection effect of the tracking model by inputting reference and query images instead of consecutive frames. Furthermore, we focus on the content gap and style gap between two input images in change detection, and address both issues by proposing adaptive content threshold and style bridgi
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Bhavani, M., V. Hanifar Sangeetha, K. Kalaivani, K. Ulagapriya, and A. Saritha. "Change detection algorithm for multi-temporal satellite images: a review." International Journal of Engineering & Technology 7, no. 2.21 (2018): 206. http://dx.doi.org/10.14419/ijet.v7i2.21.12173.

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Change detection (CD) is the process of detecting changes from multitemporal satellite images that have undergone spatial changes due to natural and man-made disaster. The objective is to analyse different change detection techniques, in order to use appropriately in various applications with the help of image processing. Techniques that are used in current researches are Image Differencing, Image Regression, Change Vector Analysis (CVA),Principal Component Analysis(PCA), Tasselled Cap, Gramm-Schmidt(GS), Post Classification Comparison, EM Detection, Unsupervised Change Detection, Li-Strahler
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Jung, Sejung, Won Hee Lee, and Youkyung Han. "Change Detection of Building Objects in High-Resolution Single-Sensor and Multi-Sensor Imagery Considering the Sun and Sensor’s Elevation and Azimuth Angles." Remote Sensing 13, no. 18 (2021): 3660. http://dx.doi.org/10.3390/rs13183660.

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Building change detection is a critical field for monitoring artificial structures using high-resolution multitemporal images. However, relief displacement depending on the azimuth and elevation angles of the sensor causes numerous false alarms and misdetections of building changes. Therefore, this study proposes an effective object-based building change detection method that considers azimuth and elevation angles of sensors in high-resolution images. To this end, segmentation images were generated using a multiresolution technique from high-resolution images after which object-based building
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Dissertations / Theses on the topic "Change detection"

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Wang, Bo. "Structural change detection via penalized regression." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6520.

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This dissertation research addresses how to detect structural changes in stochastic linear models. By introducing a special structure to the design matrix, we convert the structural change detection problem to a variable selection problem. There are many existing variable selection strategies, however, they do not fully cope with structural change detection. We design two penalized regression algorithms specifically for the structural change detection purpose. We also propose two methods involving these two algorithms to accomplish a bi-level structural change detection: they locate the change
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Hofer, Heiko. "Large-Scale Gradual Change Detection." Neubiberg Universitätsbibliothek der Universität der Bundeswehr, 2010. http://d-nb.info/1001920856/34.

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Yang, Jiangbin. "Change detection in autocorrelated processes." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0009/NQ41349.pdf.

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Comber, Alexis. "Automated land cover change detection." Thesis, University of Aberdeen, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.248628.

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This thesis describes a generic approach for automated land cover monitoring. Knowledge about land cover is acquired through a knowledge acquisition exercise and used to augment image analysis in order to determine land cover change direction. It is demonstrated that taking a task-oriented approach to the change problem avoids the specificity of more traditional data-oriented approaches. The approach described here involves four key analyses that have contributed to the overall problem solution: Identifying the knowledge used to determine different land cover elements (elicitation and modellin
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Bashir, Sulaimon A. "Change detection for activity recognition." Thesis, Robert Gordon University, 2017. http://hdl.handle.net/10059/3104.

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Activity Recognition is concerned with identifying the physical state of a user at a particular point in time. Activity recognition task requires the training of classification algorithm using the processed sensor data from the representative population of users. The accuracy of the generated model often reduces during classification of new instances due to the non-stationary sensor data and variations in user characteristics. Thus, there is a need to adapt the classification model to new user haracteristics. However, the existing approaches to model adaptation in activity recognition are blin
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Jones, Zygmunt. "Wide-baseline image change detection." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/32406.

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Growth in the prevalence of cameras has resulted in larger amounts of available image data. This has resulted in demand for automated methods of analysing this data. One key area of demand is automated change detection, the automated detection of changes in a scene, as recorded by a reference and sample image. Established methods of change detection tend to rely on the reference and sample image being captured from the same position, but much of the available data does not fit this criteria. This thesis presents novel approaches to key challenges in wide-baseline cases involving differences in
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Almutairi, Abdullah. "Monitoring land-cover change detection in an arid urban environment a comparison of change detection techniques /." Morgantown, W. Va. : [West Virginia University Libraries], 2000. http://etd.wvu.edu/templates/showETD.cfm?recnum=1410.

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Thesis (M.A.)--West Virginia University, 2000.<br>Title from document title page. Document formatted into pages; contains xi, 77 p. : ill. (some col.), maps (some col.) Includes abstract. Includes bibliographical references (p. 74-77).
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Vongsy, Karmon Marie. "CHANGE DETECTION METHODS FOR HYPERSPECTRAL IMAGERY." Wright State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=wright1184010751.

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Yousif, Osama. "Change Detection Using Multitemporal SAR Images." Licentiate thesis, KTH, Geodesi och geoinformatik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-123494.

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Multitemporal SAR images have been used successfully for the detection of different types of environmental changes. The detection of urban change using SAR images is complicated due to the special characteristics of SAR images—for example, the existence of speckle and the complex mixture of the urban environment. This thesis investigates the detection of urban changes using SAR images with the following specific objectives: (1) to investigate unsupervised change detection, (2) to investigate reduction of the speckle effect and (3) to investigate spatio-contextual change detection. Beijing and
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Brolin, Morgan. "Automatic Change Detection in Visual Scenes." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301611.

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This thesis proposes a Visual Scene Change Detector(VSCD) system which is a system which involves four parts, image retrieval, image registration, image change detection and panorama creation. Two prestudies are conducted in order to find a proposed image registration method and a image retrieval method. The two found methods are then combined with a proposed image registration method and a proposed panorama creation method to form the proposed VSCD. The image retrieval prestudy evaluates a SIFT related method with a bag of words related method and finds the SIFT related method to be the super
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Books on the topic "Change detection"

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Sampson, Philip A. Change detection. UMIST, 1998.

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Polich, John, ed. Detection of Change. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0294-4.

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İlsever, Murat, and Cem Ünsalan. Two-Dimensional Change Detection Methods. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4255-3.

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Gustafsson, Fredrik. Adaptive Filtering and Change Detection. John Wiley & Sons, Ltd, 2001. http://dx.doi.org/10.1002/0470841613.

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Kurt, Mehmet Necip. Data-Driven Quickest Change Detection. [publisher not identified], 2020.

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Gustafsson, Fredrik. Adaptive filtering and change detection. Wiley, 2000.

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World Meteorological Organization. Commission for Climatology. and World Climate Data and Monitoring Programme., eds. Climate change detection report: Reports for CCL-XII from rapporteurs that relate to climate change detection. World Meteorological Organization, 1997.

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Jenssen, A. C. Algorithms for change detection and diagnosis indynamicplants. UMIST, 1994.

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Baruch, Menahem. Mass change detection based on reduced measurements. Technion-Israel Institute of Technology, Faculty of Aerospace Engineering, 1995.

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Behrens, Richard J. Change detection analysis with spectral thermal imagery. Naval Postgraduate School, 1998.

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Book chapters on the topic "Change detection"

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Théau, Jérôme. "Change Detection." In Encyclopedia of GIS. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23519-6_129-2.

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Théau, Jérôme. "Change Detection." In Springer Handbook of Geographic Information. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-540-72680-7_7.

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Théau, Jérôme. "Change Detection." In Encyclopedia of GIS. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-17885-1_129.

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Théau, Jérôme. "Change Detection." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_129.

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Tenneson, Karis, John Dilger, Crystal Wespestad, et al. "Change Detection." In Cloud-Based Remote Sensing with Google Earth Engine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_16.

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AbstractThis chapter introduces change detection mapping. It will teach you how to make a two-date land cover change map using image differencing and threshold-based classification. You will use what you have learned so far in this book to produce a map highlighting changes in the land cover between two time steps. You will first explore differences between the two images extracted from these time steps by creating a difference layer. You will then learn how to directly classify change based on the information in both of your images.
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Mundy, Joseph L. "Change Detection." In Computer Vision. Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_214.

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Wright, Anthony A. "Change Detection." In Encyclopedia of Animal Cognition and Behavior. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-47829-6_1590-1.

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Canty, Morton John. "Change Detection." In Image Analysis, Classification, and Change Detection in Remote Sensing. CRC Press, 2019. http://dx.doi.org/10.1201/9780429464348-9.

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Wright, Anthony A. "Change Detection." In Encyclopedia of Animal Cognition and Behavior. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-55065-7_1590.

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Théau, Jérôme. "Change Detection." In Springer Handbook of Geographic Information. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-53125-6_7.

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Conference papers on the topic "Change detection"

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Chen, Yu-Zhen Janice, Jinhang Zuo, Venugopal V. Veeravalli, and Don Towsley. "Quickest Change Detection with Confusing Change." In 2024 58th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2024. https://doi.org/10.1109/ieeeconf60004.2024.10942912.

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A, Sujith Kumar, Venkatesan Meenakshi Sundaram, and P. Prabhavathy. "Change Vector Change Indices Method for Unsupervised Remote Sensing Change Detection." In 2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT). IEEE, 2024. http://dx.doi.org/10.1109/iconscept61884.2024.10627810.

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Noman, Mubahsir, Mustansar Fiaz, and Hisham Cholakkal. "ChangeBind: A Hybrid Change Encoder for Remote Sensing Change Detection." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10640559.

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Wu, Jiawen. "Residual Attention-Enhanced Bitemporal Change Detection with Pseudo-Change Suppression." In 2025 4th International Symposium on Computer Applications and Information Technology (ISCAIT). IEEE, 2025. https://doi.org/10.1109/iscait64916.2025.11010307.

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Xie, Tao, Lei Fu, Dong Zhao, Yang Hu, Hantong Xing, and Shuang Wang. "Object-Level Change Detection via Siamese Detection Network." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10641859.

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Mitkari, Kavita V., Manoj K. Arora, and Reet K. Tiwari. "Detecting Glacier Surface Changes Using Object-Based Change Detection." In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018. http://dx.doi.org/10.1109/igarss.2018.8519230.

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Suzuki, Tomoyuki, Munetaka Minoguchi, Ryota Suzuki, et al. "Semantic Change Detection." In 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE, 2018. http://dx.doi.org/10.1109/icarcv.2018.8581264.

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de Carvalho, Osmar Abilio, Renato Fontes, and Nilton Correia da Silva. "Spectral change detection." In 2007 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2007. http://dx.doi.org/10.1109/igarss.2007.4423205.

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Buades, A., J. L. Lisani, and L. Rudin. "Adaptive Change Detection." In 2009 16th International Conference on Systems, Signals and Image Processing. IEEE, 2009. http://dx.doi.org/10.1109/iwssip.2009.5367788.

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Borgolte, Kevin, Christopher Kruegel, and Giovanni Vigna. "Relevant change detection." In the 23rd International Conference. ACM Press, 2014. http://dx.doi.org/10.1145/2567948.2578039.

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Reports on the topic "Change detection"

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McCulloh, Ian A., and Kathleen M. Carley. Social Network Change Detection. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada487504.

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McCulloh, Ian, Matthew Webb, John Graham, Kathleen Carley, and Daniel B. Horn. Change Detection in Social Networks. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada484175.

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Tillman, Stan, ed. OWS-7 Motion Video Change Detection. Open Geospatial Consortium, Inc., 2010. http://dx.doi.org/10.62973/10-036r2.

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Lohrenz, M. C., M. L. Gendron, and G. J. Layne. Automic Change Detection and Classification (ACDC) System. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada494240.

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Bickel, Douglas L. On Radar Resolution in Coherent Change Detection. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1227341.

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Liu, F., and L. A. Bush. Activity Level Change Detection for Persistent Surveillance. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada457106.

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Miao, B. Q., and L. C. Zhao. Detection of Change Points Using Rank Methods. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada198406.

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Wigley, T. M. L., and P. D. Jones. Detection of greenhouse-gas-induced climatic change. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/7015088.

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Jones, P. D., and T. M. L. Wigley. Detection of Greenhouse-Gas-Induced Climatic Change. Office of Scientific and Technical Information (OSTI), 1998. http://dx.doi.org/10.2172/6615.

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G-Michael, Tesfaye, Bradley Marchand, J. D. Tucker, Daniel D. Sternlicht, Timothy M. Marston, and Mahmood R. Azimi-Sadjadi. Automated Change Detection for Synthetic Aperture Sonar. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada601363.

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