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Zeitschriftenartikel zum Thema „Interets points“

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Autor: Grafiati
Veröffentlicht am 25. Mai 2024

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1

Parsons, Jim. „Points of Interest“. Synergist 17, Nr. 1 (2006): 37. http://dx.doi.org/10.3320/1.2759306.

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2

Smith, F. J. „Points of interest“. Learned Publishing 11, Nr. 4 (Oktober 1998): 293–96. http://dx.doi.org/10.1087/09531519850146238.

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3

Fowler, John. „Points of interest“. Learned Publishing 11, Nr. 2 (April 1998): 136. http://dx.doi.org/10.1087/09531519850146544.

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4

Fowler, John. „Points of interest“. Learned Publishing 12, Nr. 1 (Januar 1999): 50. http://dx.doi.org/10.1087/09531519950146101.

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5

Aanæs, Henrik, Anders Lindbjerg Dahl und Kim Steenstrup Pedersen. „Interesting Interest Points“. International Journal of Computer Vision 97, Nr. 1 (22.06.2011): 18–35. http://dx.doi.org/10.1007/s11263-011-0473-8.

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6

Zupanćić, Bostjan. „Criminal Law and its Influence upon Normative Integration“. Acta Criminologica 7, Nr. 1 (19.01.2006): 53–105. http://dx.doi.org/10.7202/017031ar.

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Résumé INFLUENCE DE LA JUSTICE CRIMINELLE SUR L'INTEGRATION NORMATIVE Ce document est separe en trois parties : 1) la theorie de la peine ; 2) l'aspect psychologique de l'integration normative ; 3) l'aspect sociologique de l'integration normative. Il trace les grandes lignes des propositions suivantes. 1. L'ordre social engendre l'anomie, si la structure sociale et la conscience sociale dominante ne correspondent pas au degre de developpement de la societe. 2. L'anomie affecte la societe dans son ensemble, mais l'intensite du processus anomique varie selon les divergences entre les interets d'une strate sociale particuliere et les interets representes par la justice criminelle. 3. Le processus anomique demontre la necessite du changement dans la structure normative de la societe. Il ne reussit pas cependant a faire la difference entre les normes socialement utiles et celles qui ne le sont pas. 4. La structure sociale normative dominante est un systeme fortement articule. Comme tel il ne peut changer que dans son ensemble et non pas de facon partielle. Le choix doit etre fait, soit de la defendre comme un tout, ou de ne pas la defendre du tout. 5. La structure normative, a ce moment doit etre defendue en tant que tout, particulierement parce que le processus anomique l'attaque en tant que tout. 6. Le droit penal influence les sentiments collectifs a travers la peine. Plus le sentiment collectif est intense plus il est renforce par la punition. Si cette intensite n'est pas assez forte, la peine ne fera que dissimuler l'anomie ou meme catalysera le processus anomique. 7. L'influence de la peine n'est pertinente qu'en fonction des citoyens qui respectent les lois, parce que c'est la que le sentiment collectif est suffisamment intense. 8. Le manque d'identification au systeme normatif dominant a affecte la theorie sociale et ceux qui sont charges de faire respecter la loi. Cette tendance liee a la concentration de l'attention sur des delinquants, produit ou tend a produire une application de la justice criminelle moralement neutre. 9. Si nous voulons que la peine ait une influence positive sur l'integration normative, si nous voulons que la peine soutienne le sentiment collectif il faudrait que sa connotation morale soit preservee. 10. Toutefois, la peine n'est pas une solution au probleme de l'anomie. Dans le systeme de justice actuel, elle peut le diriger vers differents secteurs de la vie sociale ou le forcer a changer. Devant les besoins toujours plus grands de changement des valeurs et structures sociales, ses buts devraient etre de defendre les valeurs sociales de base qui expriment les besoins de la societe entiere. Cependant elle ne peut defendre ces valeurs qu'en defendant le systeme normatif dans son entier, l'anomie ne pouvant se developper dans certains secteurs sans affecter les points vitaux de la structure normative. 11. En consequence l'application de la justice criminelle aura necessairement un effet ambivalent : elle intensifiera l'integration normative de certaines normes a l'interieur de certains secteurs de la societe, et en meme temps elle augmentera Panomie de certaines normes dans d'autres strates sociales.
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7

Wang, Yangyang, Yibo Li und Xiaofei Ji. „Human Action Recognition Based on Normalized Interest Points and Super-Interest Points“. International Journal of Humanoid Robotics 11, Nr. 01 (März 2014): 1450005. http://dx.doi.org/10.1142/s0219843614500054.

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Visual-based human action recognition is currently one of the most active research topics in computer vision. The feature representation directly has a crucial impact on the performance of the recognition. Feature representation based on bag-of-words is popular in current research, but the spatial and temporal relationship among these features is usually discarded. In order to solve this issue, a novel feature representation based on normalized interest points is proposed and utilized to recognize the human actions. The novel representation is called super-interest point. The novelty of the proposed feature is that the spatial-temporal correlation between the interest points and human body can be directly added to the representation without considering scale and location variance of the points by introducing normalized points clustering. The novelty concerns three tasks. First, to solve the diversity of human location and scale, interest points are normalized based on the normalization of the human region. Second, to obtain the spatial-temporal correlation among the interest points, the normalized points with similar spatial and temporal distance are constructed to a super-interest point by using three-dimensional clustering algorithm. Finally, by describing the appearance characteristic of the super-interest points and location relationship among the super-interest points, a new feature representation is gained. The proposed representation formation sets up the relationship among local features and human figure. Experiments on Weizmann, KTH, and UCF sports dataset demonstrate that the proposed feature is effective for human action recognition.
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8

Câmara, Carlos Alexandre. „Esthetics in Orthodontics: interest points, reference points and discrepancy points“. Dental Press Journal of Orthodontics 17, Nr. 5 (Oktober 2012): 4–7. http://dx.doi.org/10.1590/s2176-94512012000500003.

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It is fundamental for orthodontists and all professionals related with facial, oral and dental esthetics to know how the individuals observe dentofacial structures. Thus, it will be the purpose of this Orthodontic Insight to present and describe the Interest, Reference and Discrepancy. Points With the knowledge and perception of these points it will be easier for orthodontists to create a convergent canal of communication with their patients.
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9

Fieldseth, Melanie. „Turning Points and Conflicting Interests“. Nordic Theatre Studies 33, Nr. 1 (12.03.2022): 43–57. http://dx.doi.org/10.7146/nts.v33i1.131991.

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Teaterhuset Avant Garden (TAG) was a theatre in Trondheim that co-produced and presented the work of independent performing artists and groups fromNorway and abroad. By taking a closer look at events between 1993 and 2003, this article situates TAG within broader aesthetic, organizational, and politicalcontexts and analyses the impact of these events on visions for the theatre’s future. The article is based on archival research for a forthcoming book onTAG that deals with the period 1984–2018. In 2019, TAG changed its name to Rosendal Teater in honour of the historic building where it has since relocated.
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10

Fieldseth, Melanie. „Turning Points and Conflicting Interests“. Nordic Theatre Studies 33, Nr. 1 (12.03.2022): 43–57. http://dx.doi.org/10.7146/nts.v33i1.131991.

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Teaterhuset Avant Garden (TAG) was a theatre in Trondheim that co-produced and presented the work of independent performing artists and groups fromNorway and abroad. By taking a closer look at events between 1993 and 2003, this article situates TAG within broader aesthetic, organizational, and politicalcontexts and analyses the impact of these events on visions for the theatre’s future. The article is based on archival research for a forthcoming book onTAG that deals with the period 1984–2018. In 2019, TAG changed its name to Rosendal Teater in honour of the historic building where it has since relocated.
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11

Fieldseth, Melanie. „Turning Points and Conflicting Interests“. Nordic Theatre Studies 33, Nr. 1 (12.03.2022): 43–57. http://dx.doi.org/10.7146/nts.v33i1.131991.

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Teaterhuset Avant Garden (TAG) was a theatre in Trondheim that co-produced and presented the work of independent performing artists and groups fromNorway and abroad. By taking a closer look at events between 1993 and 2003, this article situates TAG within broader aesthetic, organizational, and politicalcontexts and analyses the impact of these events on visions for the theatre’s future. The article is based on archival research for a forthcoming book onTAG that deals with the period 1984–2018. In 2019, TAG changed its name to Rosendal Teater in honour of the historic building where it has since relocated.
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12

Gao, Ying, Rui Zhao Wang und Jue Yuan. „Interest Points Guided Mesh Simplification“. Applied Mechanics and Materials 263-266 (Dezember 2012): 2320–23. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2320.

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Based on interest point detection, a feature preserving mesh simplification algorithm is proposed. The Harris operator values of all vertices in the mesh were computed firstly. On the base of Garland’s simplification algorithm, we combine the Harris operator value with quadric error metric and change the order of edge collapsing in the simplification. The experimental results show that the proposed algorithm is effective and feature preserving.
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13

Chakraborty, Bhaskar, Michael B. Holte, Thomas B. Moeslund und Jordi Gonzàlez. „Selective spatio-temporal interest points“. Computer Vision and Image Understanding 116, Nr. 3 (März 2012): 396–410. http://dx.doi.org/10.1016/j.cviu.2011.09.010.

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14

Aanæs, Henrik, Anders Lindbjerg Dahl und Kim Steenstrup Pedersen. „Erratum to: Interesting Interest Points“. International Journal of Computer Vision 108, Nr. 3 (16.04.2014): 259. http://dx.doi.org/10.1007/s11263-014-0714-8.

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15

Qi Li. „Interest Points of General Imbalance“. IEEE Transactions on Image Processing 18, Nr. 11 (November 2009): 2536–46. http://dx.doi.org/10.1109/tip.2009.2028253.

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16

Krishnadas, R. „Two points of clinical interest“. BMJ 342, apr05 3 (05.04.2011): d2148. http://dx.doi.org/10.1136/bmj.d2148.

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17

Laptev, Ivan. „On Space-Time Interest Points“. International Journal of Computer Vision 64, Nr. 2-3 (September 2005): 107–23. http://dx.doi.org/10.1007/s11263-005-1838-7.

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18

Vera, Fernando, J. Alfredo Sánchez und Ofelia Cervantes. „Enhancing User Experience in Points of Interest with Augmented Reality“. International Journal of Computer Theory and Engineering 8, Nr. 6 (Dezember 2016): 450–57. http://dx.doi.org/10.7763/ijcte.2016.v8.1088.

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19

Piech, Mateusz, Aleksander Smywinski-Pohl, Robert Marcjan und Leszek Siwik. „Towards Automatic Points of Interest Matching“. ISPRS International Journal of Geo-Information 9, Nr. 5 (01.05.2020): 291. http://dx.doi.org/10.3390/ijgi9050291.

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Complementing information about particular points, places, or institutions, i.e., so-called Points of Interest (POIs) can be achieved by matching data from the growing number of geospatial databases; these include Foursquare, OpenStreetMap, Yelp, and Facebook Places. Doing this potentially allows for the acquisition of more accurate and more complete information about POIs than would be possible by merely extracting the information from each of the systems alone. Problem: The task of Points of Interest matching, and the development of an algorithm to perform this automatically, are quite challenging problems due to the prevalence of different data structures, data incompleteness, conflicting information, naming differences, data inaccuracy, and cultural and language differences; in short, the difficulties experienced in the process of obtaining (complementary) information about the POI from different sources are due, in part, to the lack of standardization among Points of Interest descriptions; a further difficulty stems from the vast and rapidly growing amount of data to be assessed on each occasion. Research design and contributions: To propose an efficient algorithm for automatic Points of Interest matching, we: (1) analyzed available data sources—their structures, models, attributes, number of objects, the quality of data (number of missing attributes), etc.—and defined a unified POI model; (2) prepared a fairly large experimental dataset consisting of 50,000 matching and 50,000 non-matching points, taken from different geographical, cultural, and language areas; (3) comprehensively reviewed metrics that can be used for assessing the similarity between Points of Interest; (4) proposed and verified different strategies for dealing with missing or incomplete attributes; (5) reviewed and analyzed six different classifiers for Points of Interest matching, conducting experiments and follow-up comparisons to determine the most effective combination of similarity metric, strategy for dealing with missing data, and POIs matching classifier; and (6) presented an algorithm for automatic Points of Interest matching, detailing its accuracy and carrying out a complexity analysis. Results and conclusions: The main results of the research are: (1) comprehensive experimental verification and numerical comparisons of the crucial Points of Interest matching components (similarity metrics, approaches for dealing with missing data, and classifiers), indicating that the best Points of Interest matching classifier is a combination of random forest algorithm coupled with marking of missing data and mixing different similarity metrics for different POI attributes; and (2) an efficient greedy algorithm for automatic POI matching. At a cost of just 3.5% in terms of accuracy, it allows for reducing POI matching time complexity by two orders of magnitude in comparison to the exact algorithm.
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20

Fuller, Michael M. „Points of Interest: Understanding Spatial Patterns“. Ecology 86, Nr. 12 (Dezember 2005): 3422–23. http://dx.doi.org/10.1890/0012-9658(2005)86[3422:poiusp]2.0.co;2.

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21

Mukherjee, Amit, Miguel Velez-Reyes und Badrinath Roysam. „Interest Points for Hyperspectral Image Data“. IEEE Transactions on Geoscience and Remote Sensing 47, Nr. 3 (März 2009): 748–60. http://dx.doi.org/10.1109/tgrs.2008.2011280.

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22

Maver, Jasna. „Self-Similarity and Points of Interest“. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, Nr. 7 (Juli 2010): 1211–26. http://dx.doi.org/10.1109/tpami.2009.105.

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23

Loog, Marco, und Francois Lauze. „The Improbability of Harris Interest Points“. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, Nr. 6 (Juni 2010): 1141–47. http://dx.doi.org/10.1109/tpami.2010.53.

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24

Ge, Xiaoyu, Panos K. Chrysanthis, Konstantinos Pelechrinis, Demetrios Zeinalipour-Yazti und Mohamed A. Sharaf. „Serendipity-based Points-of-Interest Navigation“. ACM Transactions on Internet Technology 20, Nr. 4 (08.11.2020): 1–32. http://dx.doi.org/10.1145/3391197.

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25

Yun Tian, Gui, Duke Gledhill und David Taylor. „Comprehensive interest points based imaging mosaic“. Pattern Recognition Letters 24, Nr. 9-10 (Juni 2003): 1171–79. http://dx.doi.org/10.1016/s0167-8655(02)00287-8.

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26

Cellmer, Radosław. „Points of Interest and Housing Prices“. Real Estate Management and Valuation 31, Nr. 1 (01.03.2023): 69–77. http://dx.doi.org/10.2478/remav-2023-0007.

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ABSTRACT Points of Interest (POI) are an inherent element of the urban landscape, and their number and density reflect, among other things, the degree of urbanization and the city’s spatial structure. The very presence of POI in the closest vicinity of a residential property may indirectly or directly affect housing value. This paper aims to demonstrate the usefulness of POI density information of different categories in assessing the quality of a property’s immediate surroundings. While the mere presence of POIs in the nearest neighborhood may affect real estate prices, the influence of specific categories may not necessarily be positive. Therefore, the study attempted to classify POIs and determine their importance in the price formation process using spatial regression models. The results indicate that a high density of POIs in the immediate area is a stimulant for housing prices. The detailed analysis indicated that only some POI categories might be related to transaction prices, while in certain situations, some POI categories may negatively impact prices.
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27

Lim, Kwan Hui, Jeffrey Chan, Christopher Leckie und Shanika Karunasekera. „Personalized trip recommendation for tourists based on user interests, points of interest visit durations and visit recency“. Knowledge and Information Systems 54, Nr. 2 (05.05.2017): 375–406. http://dx.doi.org/10.1007/s10115-017-1056-y.

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28

Van Camp, Arthur, und Teddy Seidenfeld. „Exposing some points of interest about non-exposed points of desirability“. International Journal of Approximate Reasoning 144 (Mai 2022): 129–59. http://dx.doi.org/10.1016/j.ijar.2022.02.003.

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29

Lukas, Moritz, und Markus Nöth. „Interest rate fixation periods and reference points“. Journal of Behavioral and Experimental Economics 94 (Oktober 2021): 101711. http://dx.doi.org/10.1016/j.socec.2021.101711.

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30

Lozano, Jorge Alvarez, J. Antonio García Macías und Edgar Chávez. „Crowd location forecasting at points of interest“. International Journal of Ad Hoc and Ubiquitous Computing 18, Nr. 4 (2015): 191. http://dx.doi.org/10.1504/ijahuc.2015.069056.

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31

Zhou, Qian-Yi, und Vladlen Koltun. „Dense scene reconstruction with points of interest“. ACM Transactions on Graphics 32, Nr. 4 (21.07.2013): 1–8. http://dx.doi.org/10.1145/2461912.2461919.

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32

Liu, Yaqiong, und Hock Soon Seah. „Points of interest recommendation from GPS trajectories“. International Journal of Geographical Information Science 29, Nr. 6 (02.03.2015): 953–79. http://dx.doi.org/10.1080/13658816.2015.1005094.

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33

Fanjie, Meng, Guo Baolong und Wu Xianxiang. „Localized Image Retrieval Based on Interest Points“. Procedia Engineering 29 (2012): 3371–75. http://dx.doi.org/10.1016/j.proeng.2012.01.496.

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34

Erdelj, Milan, Tahiry Razafindralambo und David Simplot-Ryl. „Covering Points of Interest with Mobile Sensors“. IEEE Transactions on Parallel and Distributed Systems 24, Nr. 1 (Januar 2013): 32–43. http://dx.doi.org/10.1109/tpds.2012.46.

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35

de Vries, S. „Points of interest concerning the new IPS5“. World Patent Information 11, Nr. 3 (Januar 1989): 115–20. http://dx.doi.org/10.1016/0172-2190(89)90139-7.

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36

Deng, Ting, Wenfei Fan und Floris Geerts. „On recommendation problems beyond points of interest“. Information Systems 48 (März 2015): 64–88. http://dx.doi.org/10.1016/j.is.2014.08.002.

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37

LI, L. D., B. L. GUO und J. S. PAN. „Video Watermarking by Space-Time Interest Points“. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E91-A, Nr. 8 (01.08.2008): 2252–56. http://dx.doi.org/10.1093/ietfec/e91-a.8.2252.

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38

Yang, Yiyang, Zhiguo Gong und Leong Hou U. „Identifying Points of Interest Using Heterogeneous Features“. ACM Transactions on Intelligent Systems and Technology 5, Nr. 4 (23.01.2015): 1–27. http://dx.doi.org/10.1145/2668111.

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39

Hentschel, K. „Some Historical Points of Interest in Göttingen“. Physics in Perspective 1, Nr. 1 (März 1999): 110–17. http://dx.doi.org/10.1007/s000160050009.

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40

Sarlis, Nicholas, und Loukas Gourgiotis. „Unresolved issues, dilemmas and points of interest in thyroid cancer: A current perspective“. HORMONES 3, Nr. 3 (15.07.2004): 149–70. http://dx.doi.org/10.14310/horm.2002.11123.

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41

Abouqora, Youness, Omar Herouane, Lahcen Moumoun und Taoufiq Gadi. „New Interest Points Detector for 3D Objects Recognition“. International Journal of Intelligent Engineering and Systems 12, Nr. 4 (31.08.2019): 169–76. http://dx.doi.org/10.22266/ijies2019.0831.16.

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42

Bellamine. „MOTION DETECTION USING THE SPACE-TIME INTEREST POINTS“. Journal of Computer Science 10, Nr. 5 (01.05.2014): 828–39. http://dx.doi.org/10.3844/jcssp.2014.828.839.

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43

Loy, G., und A. Zelinsky. „Fast radial symmetry for detecting points of interest“. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, Nr. 8 (August 2003): 959–73. http://dx.doi.org/10.1109/tpami.2003.1217601.

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44

Guo, Han, Dongmei Niu, Mingxuan Zhang, Xiuyang Zhao, Bo Yang und Caiming Zhang. „Multiscale bilateral filtering to detect 3D interest points“. IET Computer Vision 14, Nr. 1 (20.01.2020): 36–47. http://dx.doi.org/10.1049/iet-cvi.2018.5405.

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45

Redfern, Joseph, Kirill Sidorov, Paul L. Rosin, Padraig Corcoran, Simon C. Moore und David Marshall. „Association of violence with urban points of interest“. PLOS ONE 15, Nr. 9 (24.09.2020): e0239840. http://dx.doi.org/10.1371/journal.pone.0239840.

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46

Yu, Donghan, Yong Li, Fengli Xu, Pengyu Zhang und Vassilis Kostakos. „Smartphone App Usage Prediction Using Points of Interest“. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, Nr. 4 (08.01.2018): 1–21. http://dx.doi.org/10.1145/3161413.

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47

Meng, Fanjie, Baolong Guo, Miao Song und Xu Zhang. „Image fusion with saliency map and interest points“. Neurocomputing 177 (Februar 2016): 1–8. http://dx.doi.org/10.1016/j.neucom.2015.10.080.

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48

Mummidi, Lakshmi Narayana, und John Krumm. „Discovering points of interest from users’ map annotations“. GeoJournal 72, Nr. 3-4 (August 2008): 215–27. http://dx.doi.org/10.1007/s10708-008-9181-5.

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49

Bellamine, I., H. Silkan und A. Tmiri. „Track color space-time interest points in video“. Multimedia Tools and Applications 79, Nr. 33-34 (23.06.2020): 24579–93. http://dx.doi.org/10.1007/s11042-020-09037-8.

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50

Wong, Fung, Shijun Lee und Quanrui Wong. „Points of Interest Recommendation Based on Context-aware“. International Journal of Hybrid Information Technology 8, Nr. 3 (31.03.2015): 55–62. http://dx.doi.org/10.14257/ijhit.2015.8.3.06.

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