Academic literature on the topic 'Interets points'
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Journal articles on the topic "Interets points"
Parsons, Jim. "Points of Interest." Synergist 17, no. 1 (2006): 37. http://dx.doi.org/10.3320/1.2759306.
Full textSmith, F. J. "Points of interest." Learned Publishing 11, no. 4 (October 1998): 293–96. http://dx.doi.org/10.1087/09531519850146238.
Full textFowler, John. "Points of interest." Learned Publishing 11, no. 2 (April 1998): 136. http://dx.doi.org/10.1087/09531519850146544.
Full textFowler, John. "Points of interest." Learned Publishing 12, no. 1 (January 1999): 50. http://dx.doi.org/10.1087/09531519950146101.
Full textAanæs, Henrik, Anders Lindbjerg Dahl, and Kim Steenstrup Pedersen. "Interesting Interest Points." International Journal of Computer Vision 97, no. 1 (June 22, 2011): 18–35. http://dx.doi.org/10.1007/s11263-011-0473-8.
Full textZupanćić, Bostjan. "Criminal Law and its Influence upon Normative Integration." Acta Criminologica 7, no. 1 (January 19, 2006): 53–105. http://dx.doi.org/10.7202/017031ar.
Full textWang, Yangyang, Yibo Li, and Xiaofei Ji. "Human Action Recognition Based on Normalized Interest Points and Super-Interest Points." International Journal of Humanoid Robotics 11, no. 01 (March 2014): 1450005. http://dx.doi.org/10.1142/s0219843614500054.
Full textCâmara, Carlos Alexandre. "Esthetics in Orthodontics: interest points, reference points and discrepancy points." Dental Press Journal of Orthodontics 17, no. 5 (October 2012): 4–7. http://dx.doi.org/10.1590/s2176-94512012000500003.
Full textFieldseth, Melanie. "Turning Points and Conflicting Interests." Nordic Theatre Studies 33, no. 1 (March 12, 2022): 43–57. http://dx.doi.org/10.7146/nts.v33i1.131991.
Full textFieldseth, Melanie. "Turning Points and Conflicting Interests." Nordic Theatre Studies 33, no. 1 (March 12, 2022): 43–57. http://dx.doi.org/10.7146/nts.v33i1.131991.
Full textDissertations / Theses on the topic "Interets points"
Loiseau-Witon, Nicolas. "Détection et description de points clés par apprentissage." Electronic Thesis or Diss., Lyon, INSA, 2023. http://www.theses.fr/2023ISAL0101.
Full textHospitals are increasingly generating 3D medical images that require automatic registration for systematic and large-scale analysis. Key points are used to reduce the time and memory required for this registration, and can be detected and described using various classical methods, as well as neural networks, as demonstrated numerous times in 2D. This thesis presents results and discussions on methods for detecting and describing key points using 3D neural networks. Two types of networks were studied to detect and/or describe characteristic points in 3D medical images. The first networks studied describe the areas directly surrounding key points, while the second type performs both detection and description of key points in a single step
Olofsson, Erik, and Simon Edström. "Points-Of-Interest CollectionGame." Thesis, Umeå universitet, Institutionen för datavetenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-38374.
Full textBayram, Ilker. "Interest Point Matching Across Arbitrary Views." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605114/index.pdf.
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is certainly one of the greatest challanges for today. Apart from possible applications, the solution may also shed light or at least give some idea on how, actually, the biological vision works. Many problems faced en route to successful algorithms require finding corresponding tokens in different views, which is termed the correspondence problem. For instance, given two images of the same scene from different views, if the camera positions and their internal parameters are known, it is possible to obtain the 3-Dimensional coordinates of a point in space, relative to the cameras, if the same point may be located in both images. Interestingly, the camera positions and internal parameters may be extracted solely from the images if a sufficient number of corresponding tokens can be found. In this sense, two subproblems, as the choice of the tokens and how to match these tokens, are examined. Due to the arbitrariness of the image pairs, invariant schemes for extracting and matching interest points, which were taken as the tokens to be matched, are utilised. In order to appreciate the ideas of the mentioned schemes, topics as scale-space, rotational and affine invariants are introduced. The geometry of the problem is briefly reviewed and the epipolar constraint is imposed using statistical outlier rejection methods. Despite the satisfactory matching performance of simple correlation-based matching schemes on small-baseline pairs, the simulation results show the improvements when the mentioned invariants are used on the cases for which they are strictly necessary.
Chavez, Aaron J. "A fast interest point detection algorithm." Thesis, Manhattan, Kan. : Kansas State University, 2008. http://hdl.handle.net/2097/538.
Full textChucre, Mirla Rafaela Rafael Braga. "K-nearest neighbors queries in time-dependent road networks: analyzing scenarios where points of interest move to the query point." reponame:Repositório Institucional da UFC, 2015. http://www.repositorio.ufc.br/handle/riufc/23696.
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A kNN query retrieve the k points of interest that are closest to the query point, where proximity is computed from the query point to the points of interest. Time-dependent road networks are represented as weighted graphs, where the weight of an edge depends on the time one passes through that edge. This way, we can model periodic congestions during rush hour and similar effects. Travel time on road networks heavily depends on the traffic and, typically, the time a moving object takes to traverse a segment depends on departure time. In time-dependent networks, a kNN query, called TD-kNN, returns the k points of interest with minimum travel-time from the query point. As a more concrete example, consider the following scenario. Imagine a tourist in Paris who is interested to visit the touristic attraction closest from him/her. Let us consider two points of interest in the city, the Eiffel Tower and the Cathedral of Notre Dame. He/she asks a query asking for the touristic attraction whose the path leading up to it is the fastest at that time, the answer depends on the departure time. For example, at 10h it takes 10 minutes to go to the Cathedral. It is the nearest attraction. Although, if he/she asks the same query at 22h, in the same spatial point, the nearest attraction is the Eiffel Tower. In this work, we identify a variation of nearest neighbors queries in time-dependent road networks that has wide applications and requires novel algorithms for processing. Differently from TD-kNN queries, we aim at minimizing the travel time from points of interest to the query point. With this approach, a cab company can find the nearest taxi in time to a passenger requesting transportation. More specifically, we address the following query: find the k points of interest (e.g. taxi drivers) which can move to the query point (e.g. a taxi user) in the minimum amount of time. Previous works have proposed solutions to answer kNN queries considering the time dependency of the network but not computing the proximity from the points of interest to the query point. We propose and discuss a solution to this type of query which are based on the previously proposed incremental network expansion and use the A∗ search algorithm equipped with suitable heuristic functions. We also discuss the design and correctness of our algorithm and present experimental results that show the efficiency and effectiveness of our solution.
Uma consulta de vizinhos mais próximos (ou kNN, do inglês k nearest neighbours) recupera o conjunto de k pontos de interesse que são mais próximos a um ponto de consulta, onde a proximidade é computada do ponto de consulta para cada ponto de interesse. Nas redes de rodovias tradicionais (estáticas) o custo de deslocamento de um ponto a outro é dado pela distância física entre esses dois pontos. Por outro lado, nas redes dependentes do tempo o custo de deslocamento (ou seja, o tempo de viagem) entre dois pontos varia de acordo com o instante de partida. Nessas redes, as consultas kNN são denominadas TD-kNN (do inglês Time-Dependent kNN). As redes de rodovias dependentes do tempo representam de forma mais adequada algumas situações reais, como, por exemplo, o deslocamento em grandes centros urbanos, onde o tempo para se deslocar de um ponto a outro durante os horários de pico, quando o tráfego é intenso e as ruas estão congestionadas, é muito maior do que em horários normais. Neste contexto, uma consulta típica consiste em descobrir os k restaurantes (pontos de interesse) mais próximos de um determinado cliente (ponto de consulta) caso este inicie o seu deslocamento ao meio dia. Nesta dissertação nós estudamos o problema de processar uma variação de consulta de vizinhos mais próximos em redes viárias dependentes do tempo. Diferentemente das consultas TD-kNN, onde a proximidade é calculada do ponto de consulta para um determinado ponto de interesse, estamos interessados em situações onde a proximidade deve ser calculada de um ponto de interesse para o ponto de consulta. Neste caso, uma consulta típica consiste em descobrir os k taxistas (pontos de interesse) mais próximos (ou seja, com o menor tempo de viagem) de um determinado cliente (ponto de consulta) caso eles iniciem o seu deslocamento até o referido cliente ao meio dia. Desta forma, nos cenários investigados nesta dissertação, são os pontos de interesse que se deslocam até o ponto de consulta, e não o contrário. O método proposto para executar este tipo de consulta aplica uma busca A∗ à medida que vai, de maneira incremental, explorando a rede. O objetivo do método é reduzir o percentual da rede avaliado na busca. A construção e a corretude do método são discutidas e são apresentados resultados experimentais com dados reais e sintéticos que mostram a eficiência da solução proposta.
Van, Niekerk A., and M. Conradie. "Literary genres as points of interest in print advertising." Journal for New Generation Sciences, Vol 12, Issue 2: Central University of Technology, Free State, Bloemfontein, 2014. http://hdl.handle.net/11462/665.
Full textThe article focuses on the interplay between print advertisements and literary genres such as poetry, drama, folklore, etc. This interplay may be used to develop an advertisement's point of interest to attract attention from a specific target audience. Based on previous research we hypothesise that literary genres are exploited as a means to engage audiences. The study aims to give a description of the typical genre characteristics of modern literary genres in SA print advertising. It also endeavours to do a pragmatic analysis of these advertisements against the background of the specific literary genre category and the genre qualities of advertisements to identify the implied marketing message.
Bachert, Sara-Lois. "Points of Interest: Essays on People, Places and Perceptions." TopSCHOLAR®, 1989. http://digitalcommons.wku.edu/theses/1873.
Full textPiekenbrock, Matthew J. "Discovering Intrinsic Points of Interest from Spatial Trajectory Data Sources." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1527160689990512.
Full textAlghamdi, Hamzah. "E-Tourism: Context-Aware Points of Interest Finder and Trip Designer." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/35676.
Full textHodzic, Lana. "TRPINav: A System for Terrain, Route, Points of Interest, and Navigation." DigitalCommons@CalPoly, 2016. https://digitalcommons.calpoly.edu/theses/1589.
Full textBooks on the topic "Interets points"
Andrew, Hunter. Richard Storms: Points of interest. Kamloops, B.C: Kamloops Art Gallery, 1995.
Find full textJeffcoat, J. W. Belton: Historical points of interest. Loughborough: Dover Reprints, 2003.
Find full textUnited States. Bureau of Land Management. Glennallen District, ed. Denali Highway: Points of interest. Glennallen, Alaska: Bureau of Land Management, Glennallen District, 1994.
Find full textTavecchia, Elena. New York: Directions, points of interest. Milan: Mousse Publishing, 2012.
Find full textJohnston, A. Lethbridge place names and points of interest. Lethbridge, Alta: Whoop-Up Country Chapter, Historical Society of Alberta, 1987.
Find full textBuckingham, S. J. Points of interest: Conserving listed tower blocks. Oxford: Oxford Brookes University, 1999.
Find full textStorms, Richard. Richard Storms: Points of Interest: [exhibition catalogue]. Windsor: Art Gallery of Windsor, 1996.
Find full textUnited States. Works Progress Administration. Division of Women's and Professional Projects. and Iowa Writers Program, eds. Southwestern Iowa guide, geology, points of interest, history. [Des Moines: s.n., 1986.
Find full textPredicting turning points in the interest rate cycle. New York: Garland, 1994.
Find full textF, Edwards Walter. Fredericksburg guidebook: History, statistics, points of interest, annual events. Fredericksburg, Tex: Gargoyle Press, 1994.
Find full textBook chapters on the topic "Interets points"
Platel, B., E. Balmachnova, L. M. J. Florack, and B. M. ter Haar Romeny. "Top-Points as Interest Points for Image Matching." In Computer Vision – ECCV 2006, 418–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11744023_33.
Full textZittrain, Jonathan. "Internet Points of Control." In The Emergent Global Information Policy Regime, 203–27. London: Palgrave Macmillan UK, 2004. http://dx.doi.org/10.1057/9780230377684_10.
Full textSchwenk, Jörg. "Point-to-Point Security." In Guide to Internet Cryptography, 85–97. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-19439-9_5.
Full textLinwood, Jeffrey. "Searching for Points of Interest." In Build Location Apps on iOS with Swift, 41–51. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6083-8_4.
Full textMeneghini, Fabio, and Paolo Biondi. "The “Facial Points of Interest”." In Clinical Facial Analysis, 29–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27228-8_4.
Full textBraun, Max, Ansgar Scherp, and Steffen Staab. "Collaborative Semantic Points of Interests." In Lecture Notes in Computer Science, 365–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13489-0_25.
Full textSchwenk, Jörg. "Point-to-Point-Sicherheit." In Sicherheit und Kryptographie im Internet, 77–89. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-29260-7_5.
Full textSchwenk, Jörg. "Point-To-Point Sicherheit." In Sicherheit und Kryptographie im Internet, 37–53. Wiesbaden: Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-06544-7_2.
Full textComer, H. Thomson, and Bruce A. Draper. "Interest Point Stability Prediction." In Lecture Notes in Computer Science, 315–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04667-4_32.
Full textPlatel, B., E. Balmachnova, L. M. J. Florack, F. M. W. Kanters, and B. M. ter Haar Romeny. "Using Top-Points as Interest Points for Image Matching." In Lecture Notes in Computer Science, 211–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11577812_19.
Full textConference papers on the topic "Interets points"
Ahmed, Mirza Tahir, Mustafa Mohamad, Joshua A. Marshall, and Michael Greenspan. "Registration of Noisy Point Clouds Using Virtual Interest Points." In 2015 12th Conference on Computer and Robot Vision (CRV). IEEE, 2015. http://dx.doi.org/10.1109/crv.2015.12.
Full textTuytelaars, Tinne. "Dense interest points." In 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2010. http://dx.doi.org/10.1109/cvpr.2010.5539911.
Full textAhmed, Mirza Tahir, Joshua A. Marshall, and Michael Greenspan. "Point Cloud Registration with Virtual Interest Points from Implicit Quadric Surface Intersections." In 2017 International Conference on 3D Vision (3DV). IEEE, 2017. http://dx.doi.org/10.1109/3dv.2017.00079.
Full textLam, Joseph, and Michael Greenspan. "On the Repeatability of 3D Point Cloud Segmentation Based on Interest Points." In 2012 Canadian Conference on Computer and Robot Vision (CRV). IEEE, 2012. http://dx.doi.org/10.1109/crv.2012.9.
Full textLaptev and Lindeberg. "Space-time interest points." In ICCV 2003: 9th International Conference on Computer Vision. IEEE, 2003. http://dx.doi.org/10.1109/iccv.2003.1238378.
Full textZitnick, C. Lawrence, and Krishnan Ramnath. "Edge foci interest points." In 2011 IEEE International Conference on Computer Vision (ICCV). IEEE, 2011. http://dx.doi.org/10.1109/iccv.2011.6126263.
Full textRadovanović, Stefan, Stefan Janićijević, Dalibor Radovanović, and Marko Šarac. "Internet Exchange Points." In Sinteza 2017. Belgrade, Serbia: Singidunum University, 2017. http://dx.doi.org/10.15308/sinteza-2017-194-200.
Full textGupta, Sachin Kumar, Prateek Seth, Saksham Giri, and Renu Mishra. "(POIRS) Point of Interest Recommendation System using Data Mining." In 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N). IEEE, 2022. http://dx.doi.org/10.1109/icac3n56670.2022.10074072.
Full textZeng, Xianyou, Long Xu, Lin Ma, and RuiZhen Zhao. "Interest points based collaborative tracking." In 2016 Visual Communications and Image Processing (VCIP). IEEE, 2016. http://dx.doi.org/10.1109/vcip.2016.7805528.
Full textSaydam, Samer R., Ibrahim A. El rube', and Amin A. Shoukry. "Contourlet Based Interest Points Detector." In 2008 20th IEEE International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2008. http://dx.doi.org/10.1109/ictai.2008.24.
Full textReports on the topic "Interets points"
Gagliano, R. IPv6 Deployment in Internet Exchange Points (IXPs). RFC Editor, August 2010. http://dx.doi.org/10.17487/rfc5963.
Full textPerkins, D. Requirements for an Internet Standard Point-to-Point Protocol. RFC Editor, December 1993. http://dx.doi.org/10.17487/rfc1547.
Full textTalvi, Ernesto, Eduardo Fernández-Arias, Carmen M. Reinhart, and Guillermo A. Calvo. The Growth-Interest Rate Cycle in the United States and its Consequences for Emerging Markets. Inter-American Development Bank, March 2001. http://dx.doi.org/10.18235/0010799.
Full textGray, E., J. Rutemiller, and G. Swallow. Internet Code Point (ICP) Assignments for NSAP Addresses. RFC Editor, May 2006. http://dx.doi.org/10.17487/rfc4548.
Full textBesse-Lototskaya, A., A. Matson, C. Hendriks, and G. Hazeu. Reflection on the Dutch points of interest regarding the proposed EU Directive on Soil Monitoring and Resilience. Wageningen: Wageningen University & Research, 2024. http://dx.doi.org/10.18174/651722.
Full textSaltus, Christina, Richard Johansen, Molly Reif, Weston Nowlin, Benjamin Schwartz, and Joshuah Perkins. Next Generation Ecological Models - Central Texas Watersheds: Geospatial Layers and Related Tables. Engineer Research and Development Center (U.S.), September 2023. http://dx.doi.org/10.21079/11681/47608.
Full textWindsor, Callan, Terhi Jokipii, and Matthieu Bussiere. The Impact of Interest Rates on Bank Profitability: A Retrospective Assessment Using New Cross-country Bank-level Data. Reserve Bank of Australia, June 2023. http://dx.doi.org/10.47688/rdp2023-05.
Full textRodier, Caroline, Andrea Broaddus, Miguel Jaller, Jeffery Song, Joschka Bischoff, and Yunwan Zhang. Cost-Benefit Analysis of Novel Access Modes: A Case Study in the San Francisco Bay Area. Mineta Transportation Institute, November 2020. http://dx.doi.org/10.31979/mti.2020.1816.
Full textLevy, Brian. How Political Contexts Influence Education Systems: Patterns, Constraints, Entry Points. Research on Improving Systems of Education (RISE), December 2022. http://dx.doi.org/10.35489/bsg-rise-2022/pe04.
Full textLevy, Brian. How Political Contexts Influence Education Systems: Patterns, Constraints, Entry Points. Research on Improving Systems of Education (RISE), December 2022. http://dx.doi.org/10.35489/bsg-rise-wp_2022/122.
Full text