Auswahl der wissenschaftlichen Literatur zum Thema „Sparse features“
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Zeitschriftenartikel zum Thema "Sparse features"
Caragea, Cornelia, Adrian Silvescu und 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.
Der volle Inhalt der QuelleSimion, Georgiana. „Sparse Features for Finger Detection“. Advanced Engineering Forum 8-9 (Juni 2013): 535–42. http://dx.doi.org/10.4028/www.scientific.net/aef.8-9.535.
Der volle Inhalt der QuelleKronvall, Ted, Maria Juhlin, Johan Swärd, Stefan I. Adalbjörnsson und Andreas Jakobsson. „Sparse modeling of chroma features“. Signal Processing 130 (Januar 2017): 105–17. http://dx.doi.org/10.1016/j.sigpro.2016.06.020.
Der volle Inhalt der QuelleHe, Wangpeng, Peipei Zhang, Xuan Liu, Binqiang Chen und 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.
Der volle Inhalt der QuelleBanihashem, Kiarash, Mohammad Hajiaghayi und 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.
Der volle Inhalt der QuelleXing, Zhan, Jianhui Lin, Yan Huang und 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.
Der volle Inhalt der QuelleWei, Wang, Tang Can, Wang Xin, Luo Yanhong, Hu Yongle und 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.
Der volle Inhalt der QuelleYANG, 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.
Der volle Inhalt der QuelleSUN, JUN, WENYUAN WANG, QING ZHUO und CHENGYUAN MA. „DISCRIMINATORY SPARSE CODING AND ITS APPLICATION TO FACE RECOGNITION“. International Journal of Image and Graphics 03, Nr. 03 (Juli 2003): 503–21. http://dx.doi.org/10.1142/s0219467803001135.
Der volle Inhalt der QuelleGrimes, David B., und Rajesh P. N. Rao. „Bilinear Sparse Coding for Invariant Vision“. Neural Computation 17, Nr. 1 (01.01.2005): 47–73. http://dx.doi.org/10.1162/0899766052530893.
Der volle Inhalt der QuelleDissertationen zum Thema "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.
Der volle Inhalt der QuelleRadwan, Noha [Verfasser], und 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.
Der volle Inhalt der QuelleHata, 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/.
Der volle Inhalt der QuelleNo 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/.
Der volle Inhalt der QuelleQuadros, 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.
Der volle Inhalt der QuelleMairal, 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.
Der volle Inhalt der QuelleAbbasnejad, 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.
Der volle Inhalt der QuelleLakemond, 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.
Der volle Inhalt der QuelleUmakanthan, Sabanadesan. „Human action recognition from video sequences“. Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/93749/1/Sabanadesan_Umakanthan_Thesis.pdf.
Der volle Inhalt der QuelleDhanjal, Charanpal. „Sparse Kernel feature extraction“. Thesis, University of Southampton, 2008. https://eprints.soton.ac.uk/64875/.
Der volle Inhalt der QuelleBücher zum Thema "Sparse features"
Flexible Sparse Learning of Feature Subspaces. [New York, N.Y.?]: [publisher not identified], 2017.
Den vollen Inhalt der Quelle findenDiPietro, Vincent. Unusual Mars surface features. 4. Aufl. Glen Dale, Md: Mars Research, 1988.
Den vollen Inhalt der Quelle findenSchommers, W. Cosmic secrets: Basic features of reality. Singapore: World Scientific, 2012.
Den vollen Inhalt der Quelle findenDavis, James E. Environmental satellites: Features and acquisition plans. Herausgegeben von Thompson Gregory F und United States. General Accounting Office. New York: Nova Novinka, 2012.
Den vollen Inhalt der Quelle findenEliseeva, Elena. Khudozhestvennoe prostranstvo v otechestvennykh igrovykh filʹmakh XX veka. Moskva: "Starklaĭt", 2012.
Den vollen Inhalt der Quelle finden1965-, Carlson Laura Anne, und Zee Emile van der, Hrsg. Functional features in language and space: Insights from perception, categorization, and development. Oxford: Oxford University Press, 2005.
Den vollen Inhalt der Quelle findenJ, Müller Hermann, und Deutsche Forschungsgemeinschaft, Hrsg. Neural binding of space and time: Spatial and temporal mechanisms of feature-object binding. Hove, East Sussex: Psychology Press, 2001.
Den vollen Inhalt der Quelle findenCrompton, John L. The proximate principle: The impact of parks, open space and water features on residential property values and the property tax base. 2. Aufl. Ashburn, Va: National Recreation and Park Association, 2004.
Den vollen Inhalt der Quelle findenDoorn, 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.
Den vollen Inhalt der Quelle findenDynamic feature space modelling, filtering, and self-tuning control of stochastic systems: A systems approach with economic and social applications. Berlin: Springer-Verlag, 1985.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Sparse features"
Ranzato, Marc’Aurelio, Y.-Lan Boureau, Koray Kavukcuoglu, Karol Gregor und Yann LeCun. „Learning Hierarchies of Sparse Features“. In 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.
Der volle Inhalt der QuelleZou, Yuan, und Teemu Roos. „Sparse Logistic Regression with Logical Features“. In 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.
Der volle Inhalt der QuelleHaker, Martin, Thomas Martinetz und Erhardt Barth. „Multimodal Sparse Features for Object Detection“. In 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.
Der volle Inhalt der QuelleCarneiro, Gustavo, und David Lowe. „Sparse Flexible Models of Local Features“. In Computer Vision – ECCV 2006, 29–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11744078_3.
Der volle Inhalt der QuelleUjaldon, M., E. L. Zapata, B. M. Chapman und H. P. Zima. „Data-parallel Language Features for Sparse Codes“. In 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.
Der volle Inhalt der QuelleRebecchi, Sébastien, Hélène Paugam-Moisy und Michèle Sebag. „Learning Sparse Features with an Auto-Associator“. In Growing Adaptive Machines, 139–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55337-0_4.
Der volle Inhalt der QuelleBarata, Catarina, Mário A. T. Figueiredo, M. Emre Celebi und Jorge S. Marques. „Local Features Applied to Dermoscopy Images: Bag-of-Features versus Sparse Coding“. In Pattern Recognition and Image Analysis, 528–36. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58838-4_58.
Der volle Inhalt der QuelleGaur, Yashesh, Maulik C. Madhavi und Hemant A. Patil. „Speaker Recognition Using Sparse Representation via Superimposed Features“. In 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.
Der volle Inhalt der QuelleZhang, Ziming, Jiawei Huang und Ze-Nian Li. „Learning Sparse Features On-Line for Image Classification“. In 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.
Der volle Inhalt der QuelleDu, Jianhao, Weihua Sheng, Qi Cheng und Meiqin Liu. „Proactive 3D Robot Mapping in Environments with Sparse Features“. In Advances in Visual Computing, 773–82. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14249-4_74.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Sparse features"
Metwally, Ahmed, und Michael Shum. „Similarity Joins of Sparse Features“. In SIGMOD/PODS '24: International Conference on Management of Data. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3626246.3653370.
Der volle Inhalt der QuelleChang, Jen-Hao Rick, Aswin C. Sankaranarayanan und B. V. K. Vijaya Kumar. „Random Features for Sparse Signal Classification“. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. http://dx.doi.org/10.1109/cvpr.2016.583.
Der volle Inhalt der QuelleGe, Tiezheng, Qifa Ke und Jian Sun. „Sparse-Coded Features for Image Retrieval“. In British Machine Vision Conference 2013. British Machine Vision Association, 2013. http://dx.doi.org/10.5244/c.27.132.
Der volle Inhalt der QuelleIglesias, Gonzalo, Adrià de Gispert und Bill Byrne. „Transducer Disambiguation with Sparse Topological Features“. In 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.
Der volle Inhalt der QuelleChakrabarti, Ayan, und Keigo Hirakawa. „Effective separation of sparse and non-sparse image features for denoising“. In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4517745.
Der volle Inhalt der QuelleYin, Chong, Siqi Liu, Vincent Wai-Sun Wong und Pong C. Yuen. „Learning Sparse Interpretable Features For NAS Scoring From Liver Biopsy Images“. In 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.
Der volle Inhalt der QuelleZhang, Xiaowang, Qiang Gao und Zhiyong Feng. „InteractionNN: A Neural Network for Learning Hidden Features in Sparse Prediction“. In 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.
Der volle Inhalt der QuelleKaarna, A. „Sparse Coded Spatial Features from Spectral Images“. In 2006 IEEE International Symposium on Geoscience and Remote Sensing. IEEE, 2006. http://dx.doi.org/10.1109/igarss.2006.949.
Der volle Inhalt der QuelleOzcelikkale, Ayca. „Sparse Recovery with Non-Linear Fourier Features“. In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9054050.
Der volle Inhalt der QuelleSainath, Tara N., David Nahamoo, Bhuvana Ramabhadran, Dimitri Kanevsky, Vaibhava Goel und Parikshit M. Shah. „Exemplar-based Sparse Representation phone identification features“. In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5947352.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Sparse features"
Borgwardt, Stefan, Walter Forkel und 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.
Der volle Inhalt der QuelleBlundell, 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.), April 2021. http://dx.doi.org/10.21079/11681/40185.
Der volle Inhalt der QuelleNaikal, Nikhil, Allen Yang und S. S. Sastry. Informative Feature Selection for Object Recognition via Sparse PCA. Fort Belvoir, VA: Defense Technical Information Center, April 2011. http://dx.doi.org/10.21236/ada543168.
Der volle Inhalt der QuelleRainey, Katie, und Ana Ascencio. Sparse Representation and Dictionary Learning as Feature Extraction in Vessel Imagery. Fort Belvoir, VA: Defense Technical Information Center, Dezember 2014. http://dx.doi.org/10.21236/ada613963.
Der volle Inhalt der QuelleHorvath, Ildiko. Investigating repeatable ionospheric features during large space storms and superstorms. Fort Belvoir, VA: Defense Technical Information Center, August 2014. http://dx.doi.org/10.21236/ada609369.
Der volle Inhalt der QuelleVeth, Mike, und Meir Pachter. Correspondence Search Mitigation Using Feature Space Anti-Aliasing. Fort Belvoir, VA: Defense Technical Information Center, Januar 2007. http://dx.doi.org/10.21236/ada473005.
Der volle Inhalt der QuelleBonnie, David John, und Kyle E. Lamb. MarFS: A Scalable Near-POSIX Name Space over Cloud Objects – New Features. Office of Scientific and Technical Information (OSTI), November 2016. http://dx.doi.org/10.2172/1333128.
Der volle Inhalt der QuelleMéndez-Vizcaíno, Juan C., und Nicolás Moreno-Arias. A Global Shock with Idiosyncratic Pains: State-Dependent Debt Limits for LATAM during the COVID-19 pandemic. Banco de la República, Oktober 2021. http://dx.doi.org/10.32468/be.1175.
Der volle Inhalt der QuelleSlotiuk, Tetiana. CONCEPT OF SOLUTIONS JOURNALISM MODEL: CONNOTION, FUNCTIONS, FEATURES OF FUNCTIONING. Ivan Franko National University of Lviv, März 2021. http://dx.doi.org/10.30970/vjo.2021.50.11097.
Der volle Inhalt der QuelleJackiewicz, 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, Juli 2012. http://dx.doi.org/10.21236/ada563097.
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