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Artykuły w czasopismach na temat "Jigsaw puzzles"
Verdine, Brian N., Georgene L. Troseth, Robert M. Hodapp i Elisabeth M. Dykens. "Strategies and Correlates of Jigsaw Puzzle and Visuospatial Performance by Persons With Prader-Willi Syndrome". American Journal on Mental Retardation 113, nr 5 (1.09.2008): 343–55. http://dx.doi.org/10.1352/2008.113:342-355.
Pełny tekst źródłaGebers, Jane. "Jigsaw Puzzles". Academic Therapy 20, nr 5 (maj 1985): 548–49. http://dx.doi.org/10.1177/105345128502000506.
Pełny tekst źródłaVisan, Ioana. "Inflammasome jigsaw puzzles". Nature Immunology 13, nr 6 (18.05.2012): 533. http://dx.doi.org/10.1038/ni.2327.
Pełny tekst źródłaIsaacs, Carol, i Julie Fisher. "Sharing Teaching Ideas: Puzzles, Puzzles,…". Mathematics Teacher 85, nr 4 (kwiecień 1992): 278–79. http://dx.doi.org/10.5951/mt.85.4.0278.
Pełny tekst źródłaMillion, Alison. "Saved by Dissections. The Popularity of Jigsaw Puzzles in Times of Calm and of Crisis. Are Librarians Dissectologists and What Might We Learn from the Bigger Picture?" Legal Information Management 20, nr 3 (wrzesień 2020): 143–50. http://dx.doi.org/10.1017/s1472669620000341.
Pełny tekst źródłaMa, Chang-Hsian, Chien-Liang Lu i Huang-Chia Shih. "Vision-Based Jigsaw Puzzle Solving with a Robotic Arm". Sensors 23, nr 15 (3.08.2023): 6913. http://dx.doi.org/10.3390/s23156913.
Pełny tekst źródłaStewart, Ian. "Two-Way Jigsaw Puzzles". Scientific American 277, nr 4 (październik 1997): 140–45. http://dx.doi.org/10.1038/scientificamerican1097-140.
Pełny tekst źródłaBrown, Burnell R. "Shibboleths and Jigsaw Puzzles". Anesthesiology 82, nr 3 (1.03.1995): 607–8. http://dx.doi.org/10.1097/00000542-199503000-00001.
Pełny tekst źródłaSong, Xingke, Jiahuan Jin, Chenglin Yao, Shihe Wang, Jianfeng Ren i Ruibin Bai. "Siamese-Discriminant Deep Reinforcement Learning for Solving Jigsaw Puzzles with Large Eroded Gaps". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 2 (26.06.2023): 2303–11. http://dx.doi.org/10.1609/aaai.v37i2.25325.
Pełny tekst źródłaGrim, Anna, Timothy O’Connor, Peter J. Olver, Chehrzad Shakiban, Ryan Slechta i Robert Thompson. "Automatic Reassembly of Three-Dimensional Jigsaw Puzzles". International Journal of Image and Graphics 16, nr 02 (kwiecień 2016): 1650009. http://dx.doi.org/10.1142/s0219467816500091.
Pełny tekst źródłaRozprawy doktorskie na temat "Jigsaw puzzles"
Tybon, Robert, i n/a. "Generating Solutions to the Jigsaw Puzzle Problem". Griffith University. School of Management, 2004. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20041101.085937.
Pełny tekst źródłaTybon, Robert. "Generating Solutions to the Jigsaw Puzzle Problem". Thesis, Griffith University, 2004. http://hdl.handle.net/10072/366062.
Pełny tekst źródłaThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Management
Faculty of Commerce and Management
Full Text
Paumard, Marie-Morgane. "Résolution automatique de puzzles par apprentissage profond". Thesis, CY Cergy Paris Université, 2020. http://www.theses.fr/2020CYUN1067.
Pełny tekst źródłaThe objective of this thesis is to develop semantic methods of reassembly in the complicated framework of heritage collections, where some blocks are eroded or missing.The reassembly of archaeological remains is an important task for heritage sciences: it allows to improve the understanding and conservation of ancient vestiges and artifacts. However, some sets of fragments cannot be reassembled with techniques using contour information or visual continuities. It is then necessary to extract semantic information from the fragments and to interpret them. These tasks can be performed automatically thanks to deep learning techniques coupled with a solver, i.e., a constrained decision making algorithm.This thesis proposes two semantic reassembly methods for 2D fragments with erosion and a new dataset and evaluation metrics.The first method, Deepzzle, proposes a neural network followed by a solver. The neural network is composed of two Siamese convolutional networks trained to predict the relative position of two fragments: it is a 9-class classification. The solver uses Dijkstra's algorithm to maximize the joint probability. Deepzzle can address the case of missing and supernumerary fragments, is capable of processing about 15 fragments per puzzle, and has a performance that is 25% better than the state of the art.The second method, Alphazzle, is based on AlphaZero and single-player Monte Carlo Tree Search (MCTS). It is an iterative method that uses deep reinforcement learning: at each step, a fragment is placed on the current reassembly. Two neural networks guide MCTS: an action predictor, which uses the fragment and the current reassembly to propose a strategy, and an evaluator, which is trained to predict the quality of the future result from the current reassembly. Alphazzle takes into account the relationships between all fragments and adapts to puzzles larger than those solved by Deepzzle. Moreover, Alphazzle is compatible with constraints imposed by a heritage framework: at the end of reassembly, MCTS does not access the reward, unlike AlphaZero. Indeed, the reward, which indicates if a puzzle is well solved or not, can only be estimated by the algorithm, because only a conservator can be sure of the quality of a reassembly
Noursobhi, Soroush. "Puzzle Up : Android Jigsaw Puzzle Game". Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13460.
Pełny tekst źródłaYang, Xingwei. "Shape Based Object Detection and Recognition in Silhouettes and Real Images". Diss., Temple University Libraries, 2011. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/111091.
Pełny tekst źródłaPh.D.
Shape is very essential for detecting and recognizing objects. It is robust to illumination, color changes. Human can recognize objects just based on shapes, thus shape based object detection and recognition methods have been popular in many years. Due to problem of segmentation, some researchers have worked on silhouettes instead of real images. The main problem in this area is object recognition and the difficulty is to handle shapes articulation and distortion. Previous methods mainly focus on one to one shape similarity measurement, which ignores context information between shapes. Instead, we utilize graph-transduction methods to reveal the intrinsic relation between shapes on 'shape manifold'. Our methods consider the context information in the dataset, which improves the performance a lot. To better describe the manifold structure, we also propose a novel method to add synthetic data points for densifying data manifold. The experimental results have shown the advantage of the algorithm. Moreover, a novel diffusion process on Tensor Product Graph is carried out for learning better affinities between data. This is also used for shape retrieval, which reaches the best ever results on MPEG-7 dataset. As shapes are important and helpful for object detection and recognition in real images, a lot of methods have used shapes to detect and recognize objects. There are two important parts for shape based methods, model construction and object detection, recognition. Most of the current methods are based on hand selected models, which is helpful but not extendable. To solve this problem, we propose to construct model by shape matching between some silhouettes and one hand decomposed silhouette. This weakly supervised method can be used not only learn the models in one object class, but also transfer the structure knowledge to other classes, which has the similar structure with the hand decomposed silhouette. The other problem is detecting and recognizing objects. A lot of methods search the images by sliding window to detect objects, which can find the global solution but with high complexity. Instead, we use sampling methods to reduce the complexity. The method we utilized is particle filter, which is popular in robot mapping and localization. We modified the standard particle filter to make it suitable for static observations and it is very helpful for object detection. Moreover, The usage of particle filter is extended for solving the jigsaw puzzle problem, where puzzle pieces are square image patches. The proposed method is able to reach much better results than the method with Loopy Belief Propagation.
Temple University--Theses
Bárnet, Lukáš. "Spojování nepřekrývajících se obrazů". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219685.
Pełny tekst źródłaO'Brien, Rachel. "Putting together the jigsaw puzzle : women's sense of self following an episode of postpartum psychosis". Thesis, University of Surrey, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.540729.
Pełny tekst źródłaSalter, B. W. Jim. "A jigsaw puzzle : assessing the English vocabulary level of junior secondary students in Hong Kong /". Thesis, Hong Kong : University of Hong Kong, 2002. http://sunzi.lib.hku.hk/hkuto/record.jsp?B25262750.
Pełny tekst źródłaStanczak, Arnaud. "La méthode de la "classe puzzle" est-elle efficace pour améliorer l'apprentissage ?" Thesis, Université Clermont Auvergne (2017-2020), 2020. http://www.theses.fr/2020CLFAL013.
Pełny tekst źródłaThe objective of this thesis is to test the effect of the Jigsaw classroom on learning. The Jigsaw classroom is a cooperative technique created by Aronson and his colleagues in the 1970s to promote the inclusion of ethnic minorities (e.g., Mexican and African-American) in desegregated schools. Although this method is presented by its developers as an effective tool for improving student learning, empirical evidence is lacking. According to the social interdependence theory, the structure of interactions between individuals determine the effects of cooperative learning (Deutsch, 1949; Johnson & Johnson, 1989). In Jigsaw, this structure comes from the distribution of complementary resources: each individual owns a “jigsaw piece”, namely a piece of information which requires the coordination of efforts among members to answer a problematic. With the help of other group members, promotive interactions (e.g., helping behaviors, explanations and questioning) should emerge which results in a better learning for the members. In this thesis, Jigsaw's effectiveness will be evaluated through a review of the scientific literature, as well as a meta-analysis of recent research and a set of experimental studies conducted among french sixth graders. To our knowledge, the experimental study of Jigsaw’s effects on learning in student populations is almost non-existent in the scientific literature and even though some research testing these effects is compiled in meta-analyses (Kyndt et al., 2013), there are no meta-analyses to date that specifficaly adress the question of Jigsaw's effects on learning. Hence, the research presented in this manuscript will attempt to evaluate the effectiveness of the Jigsaw method on learning. In Chapter 1, we present “social interdependence theory” (Johnson & Johnson, 1989, 2002, 2005), several definitions and ways of structuring cooperation between students, as well as a review of their effects on learning. Chapter 2 examines one of these cooperative technique in detail: Jigsaw (Aronson et al., 1978; Aronson & Patnoe, 2011). We describe the evolution of empirical studies conducted from its conception to the present day. Chapter 3 points out some of the limitations of this literature, particularly in terms of statistical power, and the impacts it may have on the estimation of Jigsaw's effectiveness on learning. We also develop our main hypothesis, its operationalization and the statistical tools and procedures we use in the empirical chapters: equivalence tests (Lakens, 2017), smallest effect size of interest (Hattie, 2009) and meta-analyses (Borenstein et al., 2010; Goh et al., 2016). Chapter 4 presents the results of a meta-analysis of Jigsaw's effects on learning, which synthesized empirical articles published between 2000 and 2020. We test several moderators (e.g., grade level, discipline, type of Jigsaw, location of research) in order to quantify the dispersion of Jigsaw effects and to assess heterogeneity between studies. Chapter 5 compiles five studies conducted among french sixth graders in which we test the effectiveness of Jigsaw on learning, compared to an “individual” (studies 1 and 2) or a “teaching as usual’ condition (studies 3A, 3B and 3C). The results of this chapter are interpreted with regard to the meta-analysis and the debates related to the structure of Jigsaw. In the last chapter of this manuscript, we summarize the main results developed trough the theoretical and empirical chapters. The contributions and limitations of our research are developed, as well as theoretical and practical perspectives to overcome them in view of future research
Sullivan-Vance, Karen. "A Million Piece Jigsaw Puzzle| Transition Experiences of Foster Youth Accessing Higher Education through Community College". Thesis, Portland State University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10825438.
Pełny tekst źródłaA college education offers people social and economic benefits, yet youth from foster care backgrounds are less likely than their peers to attain a college education, which places this already vulnerable population at higher risk for a lifetime of living on the margins of society. Foster alumni face multiple obstacles to accessing and persisting in higher education. To facilitate and support the success of this frequently overlooked population, professionals in higher education need to understand these obstacles. Little is known about the experiences of youth with foster care backgrounds as they transition into and through higher education. Although existing research has reported the academic, health, and social effects of having been in foster care, little is known about why foster alumni do not persist in higher education. This study used student-development theory, specifically Schlossberg’s transition theory, Tinto’s theory of student departure, and Bourdieu’s work on social and cultural capital to provide a conceptual framework through which to view the lived experiences of youth with foster care backgrounds. Because, for many youths with foster care backgrounds, the pathway to the baccalaureate degree is through a community college, this study examined and explored the transition experiences of foster alumni about to begin or currently enrolled at an Oregon Community College. The study explored the factors that challenge and facilitate foster alumni persistence towards the attainment of a college degree.
Książki na temat "Jigsaw puzzles"
Milne, A. A. Winnie-the-Pooh: Jigsaw book with seven jigsaws. London: Egmont, 2001.
Znajdź pełny tekst źródłaKern, Evan J. Making wooden jigsaw puzzles. Mechanicsburg, Pa: Stackpole Books, 1996.
Znajdź pełny tekst źródłaMcCann, Chris. Master pieces: The art history of jigsaw puzzles. Portland, Or: Collectors Press, 1998.
Znajdź pełny tekst źródłaCastles Jigsaw Cubes: Jigsaw. Flame Tree Publishing, 2007.
Znajdź pełny tekst źródłaJigsaw. Pippin Publishing, 2000.
Znajdź pełny tekst źródłaSquare Jigsaw Box Set (Jigsaw Puzzle). Templar Publishing, 2007.
Znajdź pełny tekst źródłaJigsaw Puzzles Splish Splash (Jigsaw Rhymes). T & N Childrens Publishing, 2000.
Znajdź pełny tekst źródłaSpirit of London Jigsaw: 1000-Piece Jigsaw. Pavilion Books, 2023.
Znajdź pełny tekst źródład'Errico, Camilla. Hydie : A 1,000-Piece Pop Surrealism Jigsaw Puzzle: Jigsaw Puzzles for Adults, Jigsaw Puzzles for Kids. Potter/Ten Speed/Harmony/Rodale, 2020.
Znajdź pełny tekst źródłaPrincess Jigsaw Puzzles. Unknown, 2013.
Znajdź pełny tekst źródłaCzęści książek na temat "Jigsaw puzzles"
Brand, Michael. "No Easy Puzzles: A Hardness Result for Jigsaw Puzzles". W Lecture Notes in Computer Science, 64–73. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07890-8_6.
Pełny tekst źródłaKorashy, Mostafa, Islam A. T. F. Taj-Eddin, Mahmoud Elsaadany i Shoukry I. Shams. "Solving Jigsaw Puzzles Using Variational Autoencoders". W Advances in Intelligent Systems and Computing, 708–12. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55190-2_57.
Pełny tekst źródłaSon, Kilho, James Hays i David B. Cooper. "Solving Square Jigsaw Puzzles with Loop Constraints". W Computer Vision – ECCV 2014, 32–46. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10599-4_3.
Pełny tekst źródłaZhao, Senhua, Yue-Jiao Gong i Xiaolin Xiao. "Multi-strategy Evolutionary Computation for Automated Jigsaw Puzzles". W Neural Information Processing, 50–62. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63833-7_5.
Pełny tekst źródłaHynek, Josef. "Sequence Matching Genetic Algorithm for Square Jigsaw Puzzles". W Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 317–24. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-662-44654-6_31.
Pełny tekst źródłaMarcu, Stefan-Bogdan, Yanlin Mi, Venkata V. B. Yallapragada, Mark Tangney i Sabin Tabirca. "Generating Jigsaw Puzzles and an AI Powered Solver". W Modelling and Development of Intelligent Systems, 148–60. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27034-5_10.
Pełny tekst źródłaTeixeira, João M. X. N., Pedro J. L. Silva, Júlia D. T. de Souza, Filipe F. Monteiro i Veronica Teichrieb. "JigsAR: A Mixed Reality System for Supporting the Assembly of Jigsaw Puzzles". W Design, User Experience, and Usability. Design for Contemporary Interactive Environments, 572–85. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49760-6_41.
Pełny tekst źródłaNoroozi, Mehdi, i Paolo Favaro. "Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles". W Computer Vision – ECCV 2016, 69–84. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46466-4_5.
Pełny tekst źródłaRen, Zhongle, Yiming Lu, Hanxiao Wang, Yu Zhang i Biao Hou. "SAR Scene Classification Based on Self-supervised Jigsaw Puzzles". W IFIP Advances in Information and Communication Technology, 334–43. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14903-0_36.
Pełny tekst źródłaGuo, Wenjing, Wenhong Wei, Yuhui Zhang i Anbing Fu. "A Genetic Algorithm-Based Solver for Small-Scale Jigsaw Puzzles". W Lecture Notes in Computer Science, 362–73. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53956-6_32.
Pełny tekst źródłaStreszczenia konferencji na temat "Jigsaw puzzles"
Taciano, Miguel Silva, Victor Pugliese i Fabio Augusto Faria. "SegSemPuzzle: Solving Jigsaw Puzzles with Semantic Segmentation". W Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/wvc.2023.27545.
Pełny tekst źródłaLau, Cheryl, Yuliy Schwartzburg, Appu Shaji, Zahra Sadeghipoor i Sabine Süsstrunk. "Creating personalized jigsaw puzzles". W the Workshop. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2630397.2630405.
Pełny tekst źródłaBridger, Dov, Dov Danon i Ayellet Tal. "Solving Jigsaw Puzzles With Eroded Boundaries". W 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.00358.
Pełny tekst źródłaCarlucci, Fabio M., Antonio D'Innocente, Silvia Bucci, Barbara Caputo i Tatiana Tommasi. "Domain Generalization by Solving Jigsaw Puzzles". W 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00233.
Pełny tekst źródłaYu, Rui, Chris Russell i Lourdes Agapito. "Solving Jigsaw Puzzles with Linear Programming". W British Machine Vision Conference 2016. British Machine Vision Association, 2016. http://dx.doi.org/10.5244/c.30.139.
Pełny tekst źródłaMondal, Debajyoti, Yang Wang i Stephane Durocher. "Robust Solvers for Square Jigsaw Puzzles". W 2013 International Conference on Computer and Robot Vision (CRV). IEEE, 2013. http://dx.doi.org/10.1109/crv.2013.54.
Pełny tekst źródłaBottoni, Paolo, i Miguel Ceriani. "Linked Data Queries as Jigsaw Puzzles". W CHItaly 2015: 11th biannual Conference of the Italian SIGCHI Chapter. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2808435.2808467.
Pełny tekst źródłaKimoto, Kouki, Yasuyuki Murai, Hiroyuki Tsuji i Shinji Tokumasu. "Rectilinear Jigsaw Puzzles: Theory and Algorithms". W 2006 World Automation Congress. IEEE, 2006. http://dx.doi.org/10.1109/wac.2006.375747.
Pełny tekst źródłaGallagher, A. C. "Jigsaw puzzles with pieces of unknown orientation". W 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2012. http://dx.doi.org/10.1109/cvpr.2012.6247699.
Pełny tekst źródłaLogeswaran, Lajanugen. "Solving Jigsaw Puzzles using Paths and Cycles". W British Machine Vision Conference 2014. British Machine Vision Association, 2014. http://dx.doi.org/10.5244/c.28.117.
Pełny tekst źródłaRaporty organizacyjne na temat "Jigsaw puzzles"
Stanley-Wall, Nicola, Amy Cameron, Erin Hardee, Andrea Davies, Alan Prescott, Paul Harrison, Ali Floyd i in. School of Life Sciences Research Jigsaw Puzzles. University of Dundee, lipiec 2024. http://dx.doi.org/10.20933/100001317.
Pełny tekst źródłaSullivan-Vance, Karen. A Million Piece Jigsaw Puzzle: Transition Experiences of Foster Youth Accessing Higher Education through Community College. Portland State University Library, styczeń 2000. http://dx.doi.org/10.15760/etd.6310.
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