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Artykuły w czasopismach na temat "Sparse features"
Caragea, Cornelia, Adrian Silvescu i Prasenjit Mitra. "Combining Hashing and Abstraction in Sparse High Dimensional Feature Spaces". Proceedings of the AAAI Conference on Artificial Intelligence 26, nr 1 (20.09.2021): 3–9. http://dx.doi.org/10.1609/aaai.v26i1.8117.
Pełny tekst źródłaSimion, Georgiana. "Sparse Features for Finger Detection". Advanced Engineering Forum 8-9 (czerwiec 2013): 535–42. http://dx.doi.org/10.4028/www.scientific.net/aef.8-9.535.
Pełny tekst źródłaKronvall, Ted, Maria Juhlin, Johan Swärd, Stefan I. Adalbjörnsson i Andreas Jakobsson. "Sparse modeling of chroma features". Signal Processing 130 (styczeń 2017): 105–17. http://dx.doi.org/10.1016/j.sigpro.2016.06.020.
Pełny tekst źródłaHe, Wangpeng, Peipei Zhang, Xuan Liu, Binqiang Chen i Baolong Guo. "Group-Sparse Feature Extraction via Ensemble Generalized Minimax-Concave Penalty for Wind-Turbine-Fault Diagnosis". Sustainability 14, nr 24 (14.12.2022): 16793. http://dx.doi.org/10.3390/su142416793.
Pełny tekst źródłaBanihashem, Kiarash, Mohammad Hajiaghayi i Max Springer. "Optimal Sparse Recovery with Decision Stumps". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 6 (26.06.2023): 6745–52. http://dx.doi.org/10.1609/aaai.v37i6.25827.
Pełny tekst źródłaXing, Zhan, Jianhui Lin, Yan Huang i Cai Yi. "A Feature Extraction Method of Wheelset-Bearing Fault Based on Wavelet Sparse Representation with Adaptive Local Iterative Filtering". Shock and Vibration 2020 (25.07.2020): 1–20. http://dx.doi.org/10.1155/2020/2019821.
Pełny tekst źródłaWei, Wang, Tang Can, Wang Xin, Luo Yanhong, Hu Yongle i Li Ji. "Image Object Recognition via Deep Feature-Based Adaptive Joint Sparse Representation". Computational Intelligence and Neuroscience 2019 (21.11.2019): 1–9. http://dx.doi.org/10.1155/2019/8258275.
Pełny tekst źródłaYANG, B. J. "DOMINANT EIGENVECTOR AND EIGENVALUE ALGORITHM IN SPARSE NETWORK SPECTRAL CLUSTERING". Latin American Applied Research - An international journal 48, nr 4 (31.10.2018): 323–28. http://dx.doi.org/10.52292/j.laar.2018.248.
Pełny tekst źródłaSUN, JUN, WENYUAN WANG, QING ZHUO i CHENGYUAN MA. "DISCRIMINATORY SPARSE CODING AND ITS APPLICATION TO FACE RECOGNITION". International Journal of Image and Graphics 03, nr 03 (lipiec 2003): 503–21. http://dx.doi.org/10.1142/s0219467803001135.
Pełny tekst źródłaGrimes, David B., i Rajesh P. N. Rao. "Bilinear Sparse Coding for Invariant Vision". Neural Computation 17, nr 1 (1.01.2005): 47–73. http://dx.doi.org/10.1162/0899766052530893.
Pełny tekst źródłaRozprawy doktorskie na temat "Sparse features"
Strohmann, Thomas. "Very sparse kernel models: Predicting with few examples and few features". Diss., Connect to online resource, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3239405.
Pełny tekst źródłaRadwan, Noha [Verfasser], i Wolfram [Akademischer Betreuer] Burgard. "Leveraging sparse and dense features for reliable state estimation in urban environments". Freiburg : Universität, 2019. http://d-nb.info/1190031361/34.
Pełny tekst źródłaHata, Alberto Yukinobu. "Road features detection and sparse map-based vehicle localization in urban environments". Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-08062017-090428/.
Pełny tekst źródłaNo contexto de veículos autônomos, a localização é um dos componentes fundamentais, pois possibilita tarefas como ultrapassagem, direção assistida e navegação autônoma. A presença de edifícios e o mau tempo interferem na recepção do sinal de GPS que consequentemente dificulta o uso de tal tecnologia para a localização de veículos dentro das cidades. Alternativamente, a localização com suporte aos mapas vem sendo empregada para estimar a posição sem a dependência do GPS. Nesta solução, a posição do veículo é dada pela região em que ocorre a melhor correspondência entre o mapa do ambiente e a leitura do sensor. Antes da criação dos mapas, características dos ambientes devem ser extraídas a partir das leituras dos sensores. Dessa forma, guias e sinalizações horizontais têm sido largamente utilizados para o mapeamento. Entretanto, métodos de mapeamento urbano geralmente necessitam de repetidas leituras do mesmo lugar para compensar as oclusões. A construção de representações precisas dos ambientes é essencial para uma adequada associação dos dados dos sensores como mapa durante a localização. De forma a evitar a necessidade de um processo manual para remover obstáculos que causam oclusão e áreas não observadas, propõe-se um método de localização de veículos com suporte aos mapas construídos a partir de observações parciais do ambiente. No sistema de localização proposto, os mapas são construídos a partir de guias e sinalizações horizontais extraídas a partir de leituras de um sensor multicamadas. As guias podem ser detectadas mesmo na presença de veículos que obstruem a percepção das ruas, por meio do uso de regressão robusta. Na detecção de sinalizações horizontais é empregado o método de limiarização por Otsu que analisa dados de reflexão infravermelho, o que torna o método insensível à variação de luminosidade. Dois tipos de mapas são empregados para a representação das guias e das sinalizações horizontais: mapa de grade de ocupação (OGM) e mapa de ocupação por processo Gaussiano (GPOM). O OGM é uma estrutura que representa o ambiente por meio de uma grade reticulada. OGPOM é uma representação contínua que possibilita a estimação de áreas não observadas. O método de localização por Monte Carlo (MCL) foi adaptado para suportar os mapas construídos. Dessa forma, a localização de veículos foi testada em MCL com suporte ao OGM e MCL com suporte ao GPOM. No caso do MCL baseado em GPOM, um novo modelo de verossimilhança baseado em função densidade probabilidade de distribuição multi-normal é proposto. Experimentos foram realizados em ambientes urbanos reais. Mapas do ambiente foram gerados a partir de dados de laser esparsos de forma a verificar a reconstrução de áreas não observadas. O sistema de localização foi avaliado por meio da comparação das posições estimadas comum GPS de alta precisão. Comparou-se também o MCL baseado em OGM com o MCL baseado em GPOM, de forma a verificar qual abordagem apresenta melhores resultados.
Pundlik, Shrinivas J. "Motion segmentation from clustering of sparse point features using spatially constrained mixture models". Connect to this title online, 2009. http://etd.lib.clemson.edu/documents/1252937182/.
Pełny tekst źródłaQuadros, Alistair James. "Representing 3D shape in sparse range images for urban object classification". Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/10515.
Pełny tekst źródłaMairal, Julien. "Sparse coding for machine learning, image processing and computer vision". Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2010. http://tel.archives-ouvertes.fr/tel-00595312.
Pełny tekst źródłaAbbasnejad, Iman. "Learning spatio-temporal features for efficient event detection". Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/121184/1/Iman_Abbasnejad_Thesis.pdf.
Pełny tekst źródłaLakemond, Ruan. "Multiple camera management using wide baseline matching". Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/37668/1/Ruan_Lakemond_Thesis.pdf.
Pełny tekst źródłaUmakanthan, Sabanadesan. "Human action recognition from video sequences". Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/93749/1/Sabanadesan_Umakanthan_Thesis.pdf.
Pełny tekst źródłaDhanjal, Charanpal. "Sparse Kernel feature extraction". Thesis, University of Southampton, 2008. https://eprints.soton.ac.uk/64875/.
Pełny tekst źródłaKsiążki na temat "Sparse features"
Flexible Sparse Learning of Feature Subspaces. [New York, N.Y.?]: [publisher not identified], 2017.
Znajdź pełny tekst źródłaDiPietro, Vincent. Unusual Mars surface features. Wyd. 4. Glen Dale, Md: Mars Research, 1988.
Znajdź pełny tekst źródłaSchommers, W. Cosmic secrets: Basic features of reality. Singapore: World Scientific, 2012.
Znajdź pełny tekst źródłaDavis, James E. Environmental satellites: Features and acquisition plans. Redaktorzy Thompson Gregory F i United States. General Accounting Office. New York: Nova Novinka, 2012.
Znajdź pełny tekst źródłaEliseeva, Elena. Khudozhestvennoe prostranstvo v otechestvennykh igrovykh filʹmakh XX veka. Moskva: "Starklaĭt", 2012.
Znajdź pełny tekst źródła1965-, Carlson Laura Anne, i Zee Emile van der, red. Functional features in language and space: Insights from perception, categorization, and development. Oxford: Oxford University Press, 2005.
Znajdź pełny tekst źródłaJ, Müller Hermann, i Deutsche Forschungsgemeinschaft, red. Neural binding of space and time: Spatial and temporal mechanisms of feature-object binding. Hove, East Sussex: Psychology Press, 2001.
Znajdź pełny tekst źródłaCrompton, John L. The proximate principle: The impact of parks, open space and water features on residential property values and the property tax base. Wyd. 2. Ashburn, Va: National Recreation and Park Association, 2004.
Znajdź pełny tekst źródłaDoorn, Niels van. Digital spaces, material traces: Investigating the performance of gender, sexuality, and embodiment on internet platforms that feature user-generated content. [S.l]: [s.n.], 2009.
Znajdź pełny tekst źródłaDynamic feature space modelling, filtering, and self-tuning control of stochastic systems: A systems approach with economic and social applications. Berlin: Springer-Verlag, 1985.
Znajdź pełny tekst źródłaCzęści książek na temat "Sparse features"
Ranzato, Marc’Aurelio, Y.-Lan Boureau, Koray Kavukcuoglu, Karol Gregor i Yann LeCun. "Learning Hierarchies of Sparse Features". W Encyclopedia of the Sciences of Learning, 1880–84. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_1880.
Pełny tekst źródłaZou, Yuan, i Teemu Roos. "Sparse Logistic Regression with Logical Features". W Advances in Knowledge Discovery and Data Mining, 316–27. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31753-3_26.
Pełny tekst źródłaHaker, Martin, Thomas Martinetz i Erhardt Barth. "Multimodal Sparse Features for Object Detection". W Artificial Neural Networks – ICANN 2009, 923–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04277-5_93.
Pełny tekst źródłaCarneiro, Gustavo, i David Lowe. "Sparse Flexible Models of Local Features". W Computer Vision – ECCV 2006, 29–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11744078_3.
Pełny tekst źródłaUjaldon, M., E. L. Zapata, B. M. Chapman i H. P. Zima. "Data-parallel Language Features for Sparse Codes". W Languages, Compilers and Run-Time Systems for Scalable Computers, 253–64. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4615-2315-4_19.
Pełny tekst źródłaRebecchi, Sébastien, Hélène Paugam-Moisy i Michèle Sebag. "Learning Sparse Features with an Auto-Associator". W Growing Adaptive Machines, 139–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55337-0_4.
Pełny tekst źródłaBarata, Catarina, Mário A. T. Figueiredo, M. Emre Celebi i Jorge S. Marques. "Local Features Applied to Dermoscopy Images: Bag-of-Features versus Sparse Coding". W Pattern Recognition and Image Analysis, 528–36. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58838-4_58.
Pełny tekst źródłaGaur, Yashesh, Maulik C. Madhavi i Hemant A. Patil. "Speaker Recognition Using Sparse Representation via Superimposed Features". W Lecture Notes in Computer Science, 140–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-45062-4_19.
Pełny tekst źródłaZhang, Ziming, Jiawei Huang i Ze-Nian Li. "Learning Sparse Features On-Line for Image Classification". W Lecture Notes in Computer Science, 122–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21593-3_13.
Pełny tekst źródłaDu, Jianhao, Weihua Sheng, Qi Cheng i Meiqin Liu. "Proactive 3D Robot Mapping in Environments with Sparse Features". W Advances in Visual Computing, 773–82. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14249-4_74.
Pełny tekst źródłaStreszczenia konferencji na temat "Sparse features"
Metwally, Ahmed, i Michael Shum. "Similarity Joins of Sparse Features". W SIGMOD/PODS '24: International Conference on Management of Data. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3626246.3653370.
Pełny tekst źródłaChang, Jen-Hao Rick, Aswin C. Sankaranarayanan i B. V. K. Vijaya Kumar. "Random Features for Sparse Signal Classification". W 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. http://dx.doi.org/10.1109/cvpr.2016.583.
Pełny tekst źródłaGe, Tiezheng, Qifa Ke i Jian Sun. "Sparse-Coded Features for Image Retrieval". W British Machine Vision Conference 2013. British Machine Vision Association, 2013. http://dx.doi.org/10.5244/c.27.132.
Pełny tekst źródłaIglesias, Gonzalo, Adrià de Gispert i Bill Byrne. "Transducer Disambiguation with Sparse Topological Features". W Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2015. http://dx.doi.org/10.18653/v1/d15-1273.
Pełny tekst źródłaChakrabarti, Ayan, i Keigo Hirakawa. "Effective separation of sparse and non-sparse image features for denoising". W ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4517745.
Pełny tekst źródłaYin, Chong, Siqi Liu, Vincent Wai-Sun Wong i Pong C. Yuen. "Learning Sparse Interpretable Features For NAS Scoring From Liver Biopsy Images". W Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/220.
Pełny tekst źródłaZhang, Xiaowang, Qiang Gao i Zhiyong Feng. "InteractionNN: A Neural Network for Learning Hidden Features in Sparse Prediction". W Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/602.
Pełny tekst źródłaKaarna, A. "Sparse Coded Spatial Features from Spectral Images". W 2006 IEEE International Symposium on Geoscience and Remote Sensing. IEEE, 2006. http://dx.doi.org/10.1109/igarss.2006.949.
Pełny tekst źródłaOzcelikkale, Ayca. "Sparse Recovery with Non-Linear Fourier Features". W ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9054050.
Pełny tekst źródłaSainath, Tara N., David Nahamoo, Bhuvana Ramabhadran, Dimitri Kanevsky, Vaibhava Goel i Parikshit M. Shah. "Exemplar-based Sparse Representation phone identification features". W ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5947352.
Pełny tekst źródłaRaporty organizacyjne na temat "Sparse features"
Borgwardt, Stefan, Walter Forkel i Alisa Kovtunova. Finding New Diamonds: Temporal Minimal-World Query Answering over Sparse ABoxes. Technische Universität Dresden, 2019. http://dx.doi.org/10.25368/2023.223.
Pełny tekst źródłaBlundell, S. Micro-terrain and canopy feature extraction by breakline and differencing analysis of gridded elevation models : identifying terrain model discontinuities with application to off-road mobility modeling. Engineer Research and Development Center (U.S.), kwiecień 2021. http://dx.doi.org/10.21079/11681/40185.
Pełny tekst źródłaNaikal, Nikhil, Allen Yang i S. S. Sastry. Informative Feature Selection for Object Recognition via Sparse PCA. Fort Belvoir, VA: Defense Technical Information Center, kwiecień 2011. http://dx.doi.org/10.21236/ada543168.
Pełny tekst źródłaRainey, Katie, i Ana Ascencio. Sparse Representation and Dictionary Learning as Feature Extraction in Vessel Imagery. Fort Belvoir, VA: Defense Technical Information Center, grudzień 2014. http://dx.doi.org/10.21236/ada613963.
Pełny tekst źródłaHorvath, Ildiko. Investigating repeatable ionospheric features during large space storms and superstorms. Fort Belvoir, VA: Defense Technical Information Center, sierpień 2014. http://dx.doi.org/10.21236/ada609369.
Pełny tekst źródłaVeth, Mike, i Meir Pachter. Correspondence Search Mitigation Using Feature Space Anti-Aliasing. Fort Belvoir, VA: Defense Technical Information Center, styczeń 2007. http://dx.doi.org/10.21236/ada473005.
Pełny tekst źródłaBonnie, David John, i Kyle E. Lamb. MarFS: A Scalable Near-POSIX Name Space over Cloud Objects – New Features. Office of Scientific and Technical Information (OSTI), listopad 2016. http://dx.doi.org/10.2172/1333128.
Pełny tekst źródłaMéndez-Vizcaíno, Juan C., i Nicolás Moreno-Arias. A Global Shock with Idiosyncratic Pains: State-Dependent Debt Limits for LATAM during the COVID-19 pandemic. Banco de la República, październik 2021. http://dx.doi.org/10.32468/be.1175.
Pełny tekst źródłaSlotiuk, Tetiana. CONCEPT OF SOLUTIONS JOURNALISM MODEL: CONNOTION, FUNCTIONS, FEATURES OF FUNCTIONING. Ivan Franko National University of Lviv, marzec 2021. http://dx.doi.org/10.30970/vjo.2021.50.11097.
Pełny tekst źródłaJackiewicz, Jason. Automatic Recognition of Solar Features for Developing Data Driven Prediction Models of Solar Activity and Space Weather. Fort Belvoir, VA: Defense Technical Information Center, lipiec 2012. http://dx.doi.org/10.21236/ada563097.
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