Academic literature on the topic 'SIMULINK MODELS FOR VIDEO PROCESSING'
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Journal articles on the topic "SIMULINK MODELS FOR VIDEO PROCESSING"
DOBLANDER, ANDREAS, DIETMAR GÖSSERINGER, BERNHARD RINNER, and HELMUT SCHWABACH. "AN EVALUATION OF MODEL-BASED SOFTWARE SYNTHESIS FROM SIMULINK MODELS FOR EMBEDDED VIDEO APPLICATIONS." International Journal of Software Engineering and Knowledge Engineering 15, no. 02 (April 2005): 343–48. http://dx.doi.org/10.1142/s0218194005002038.
Full textNaso, David, Olha Pohudina, Andrii Pohudin, Sergiy Yashin, and Rossella Bartolo. "Autonomous flight insurance method of unmanned aerial vehicles Parot Mambo using semantic segmentation data." Radioelectronic and Computer Systems, no. 1 (March 7, 2023): 147–54. http://dx.doi.org/10.32620/reks.2023.1.12.
Full textComan, Mircea, and Balan Radu. "Video Camera Measuring Application Using Matlab." Solid State Phenomena 166-167 (September 2010): 139–44. http://dx.doi.org/10.4028/www.scientific.net/ssp.166-167.139.
Full textBobyr, Maxim, Alexander Arkhipov, and Aleksey Yakushev. "Shade recognition of the color label based on the fuzzy clustering." Informatics and Automation 20, no. 2 (March 30, 2021): 407–34. http://dx.doi.org/10.15622/ia.2021.20.2.6.
Full textXie, Xiao Peng, and Yun Yi Li. "Computer Simulation Study Based on Matlab." Applied Mechanics and Materials 513-517 (February 2014): 3049–52. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.3049.
Full textXin Li. "Video Processing Via Implicit and Mixture Motion Models." IEEE Transactions on Circuits and Systems for Video Technology 17, no. 8 (August 2007): 953–63. http://dx.doi.org/10.1109/tcsvt.2007.896656.
Full textZhong, Zhaoqian, and Masato Edahiro. "Model-Based Parallelization for Simulink Models on Multicore CPUs and GPUs." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 20 (January 4, 2020): 1–13. http://dx.doi.org/10.24297/ijct.v20i.8533.
Full textArsinte, Radu, and Eugen Lupu. "Prototyping Industrial Vision Applications and Implementations on Multimedia Processors." Applied Mechanics and Materials 808 (November 2015): 321–26. http://dx.doi.org/10.4028/www.scientific.net/amm.808.321.
Full textHanh, Le Thi My, Nguyen Thanh Binh, and Khuat Thanh Tung. "Parallel Mutant Execution Techniques in Mutation Testing Process for Simulink Models." Journal of Telecommunications and Information Technology 4 (December 20, 2017): 90–100. http://dx.doi.org/10.26636/jtit.2017.113617.
Full textMaqsood, Azka, Imran Touqir, Adil Masood Siddiqui, and Maham Haider. "Wavelet Based Video Denoising using Probabilistic Models." January 2019 38, no. 1 (January 1, 2019): 17–30. http://dx.doi.org/10.22581/muet1982.1901.02.
Full textDissertations / Theses on the topic "SIMULINK MODELS FOR VIDEO PROCESSING"
Monaco, Joseph W. "Generalized motion models for video applications." Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/14926.
Full textOwen, Michael Information Technology & Electrical Engineering Australian Defence Force Academy UNSW. "Temporal motion models for video mosaicing and synthesis." Awarded by:University of New South Wales - Australian Defence Force Academy, 2008. http://handle.unsw.edu.au/1959.4/39028.
Full textJónsson, Ragner H. "Adaptive subband coding of video using probability distribution models." Diss., Georgia Institute of Technology, 1994. http://hdl.handle.net/1853/14453.
Full textZapata, Iván R. "Detecting humans in video sequences using statistical color and shape models." [Gainesville, Fla.] : University of Florida, 2001. http://etd.fcla.edu/etd/uf/2001/anp1058/ivan%5Fthesis2.pdf.
Full textTitle from first page of PDF file. Document formatted into pages; contains viii, 49 p.; also contains graphics. Vita. Includes bibliographical references (p. 47-48).
FOTIO, TIOTSOP LOHIC. "Optimizing Perceptual Quality Prediction Models for Multimedia Processing Systems." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2970982.
Full textLee, Sangkeun. "Video analysis and abstraction in the compressed domain." Diss., Available online, Georgia Institute of Technology, 2004:, 2003. http://etd.gatech.edu/theses/available/etd-04072004-180041/unrestricted/lee%5fsangkeun%5f200312%5fphd.pdf.
Full textLazcano, Vanel. "Some problems in depth enhanced video processing." Doctoral thesis, Universitat Pompeu Fabra, 2016. http://hdl.handle.net/10803/373917.
Full textEn esta tesis se abordan dos problemas: interpolación de datos en el contexto del cálculo de disparidades tanto para imágenes como para video, y el problema de la estimación del movimiento aparente de objetos en una secuencia de imágenes. El primer problema trata de la completación de datos de profundidad en una región de la imagen o video dónde los datos se han perdido debido a oclusiones, datos no confiables, datos dañados o pérdida de datos durante la adquisición. En esta tesis estos problemas se abordan de dos maneras. Primero, se propone una energía basada en gradientes no-locales, energía que puede (localmente) completar planos. Se considera este modelo como una extensión del filtro bilateral al dominio del gradiente. Se ha evaluado en forma exitosa el modelo para completar datos sintéticos y también mapas de profundidad incompletos de un sensor Kinect. El segundo enfoque, para abordar el problema, es un estudio experimental del biased AMLE (Biased Absolutely Minimizing Lipschitz Extension) para interpolación anisotrópica de datos de profundidad en grandes regiones sin información. El operador AMLE es un interpolador de conos, pero el operador biased AMLE es un interpolador de conos exponenciales lo que lo hace estar más adaptado a mapas de profundidad de escenas reales (las que comunmente presentan superficies convexas, concavas y suaves). Además, el operador biased AMLE puede expandir datos de profundidad a regiones grandes. Considerando al dominio de la imagen dotado de una métrica anisotrópica, el método propuesto puede tomar en cuenta información geométrica subyacente para no interpolar a través de los límites de los objetos a diferentes profundidades. Se ha propuesto un modelo numérico, basado en el operador eikonal, para calcular la solución del biased AMLE. Adicionalmente, se ha extendido el modelo numérico a sequencias de video. El cálculo del flujo óptico es uno de los problemas más desafiantes para la visión por computador. Los modelos tradicionales fallan al estimar el flujo óptico en presencia de oclusiones o iluminación no uniforme. Para abordar este problema se propone un modelo variacional para conjuntamente estimar flujo óptico y oclusiones. Además, el modelo propuesto puede tolerar, una limitación tradicional de los métodos variacionales, desplazamientos rápidos de objetos que son más grandes que el tamaño objeto en la escena. La adición de un término para el balance de gradientes e intensidades aumenta la robustez del modelo propuesto ante cambios de iluminación. La inclusión de correspondencias adicionales (obtenidas usando búsqueda exhaustiva en ubicaciones específicas) ayuda a estimar grandes desplazamientos.
Hautala, I. (Ilkka). "From dataflow models to energy efficient application specific processors." Doctoral thesis, Oulun yliopisto, 2019. http://urn.fi/urn:isbn:9789526223681.
Full textTiivistelmä Langattomien verkkojen kehittyminen on luonut edellytykset useille uusille sovelluksille. Muiden muassa sosiaalisen media, suoratoistopalvelut, virtuaalitodellisuus ja esineiden internet asettavat kannettaville ja puettaville laitteille moninaisia toimintoihin, suorituskykyyn, energiankulutukseen ja fyysiseen muotoon liittyviä vaatimuksia. Yksi isoimmista haasteista on sulautettujen laitteiden energiankulutus. Laitteiden energiatehokkuutta on pyritty parantamaan rinnakkaislaskentaa ja räätälöityjä laskentaresursseja hyödyntämällä. Tämä puolestaan on vaikeuttanut niin laite- kuin sovelluskehitystä, koska laajassa käytössä olevat kehitystyökalut perustuvat matalan tason abstraktioihin ja hyödyntävät alun perin yksi ydinprosessoreille suunniteltuja ohjelmointikieliä. Korkean tason ja automatisoitujen kehitysmenetelmien käyttöönottoa on hidastanut aikaansaatujen järjestelmien puutteellinen suorituskyky ja laiteresurssien tehoton hyödyntäminen. Väitöskirja esittelee datavuopohjaiseen suunnitteluun perustuvan työkaluketjun, joka on tarkoitettu energiatehokkaiden signaalikäsittelyjärjestelmien toteuttamiseen. Työssä esiteltävä suunnitteluvuo pohjautuu laitteistoratkaisuissa räätälöitävään ja ohjelmoitavaan siirtoliipaistavaan prosessoritemplaattiin. Ehdotettu suunnitteluvuo mahdollistaa useiden heterogeenisten prosessoriytimien ja niiden välisten kytkentöjen räätälöimisen sovelluksien tarpeiden vaatimalla tavalla. Suunnitteluvuossa ohjelmistot kuvataan korkean tason datavuomallien avulla. Tämä mahdollistaa erityisesti rinnakkaista laskentaa sisältävän ohjelmiston automaattisen sovittamisen erilaisiin moniprosessorijärjestelmiin ja nopeuttaa erilaisten järjestelmätason ratkaisujen kartoittamista. Suunnitteluvuon käyttökelpoisuus osoitetaan käyttäen esimerkkinä kolmea eri signaalinkäsittelysovellusta. Tulokset osoittavat, että suunnittelumenetelmien abstraktiotasoa on mahdollista nostaa ilman merkittävää suorituskyvyn heikkenemistä. Väitöskirjan keskeinen sovellusalue on videonkoodaus. Työ esittelee videonkoodaukseen suunniteltuja energiatehokkaita ja uudelleenohjelmoitavia prosessoriytimiä. Ratkaisut perustuvat usean prosessoriytimen käyttämiseen hyödyntäen erityisesti videonkäsittelyalgoritmeille ominaista liukuhihnarinnakkaisuutta. Prosessorien virrankulutus, suorituskyky ja pinta-ala on analysoitu käyttämällä simulointimalleja, jotka huomioivat logiikkasolujen sijoittelun ja johdotuksen. Ehdotetut sovelluskohtaiset prosessoriratkaisut tarjoavat uuden energiatehokkaan kompromissiratkaisun tavanomaisten ohjelmoitavien prosessoreiden ja kiinteästi johdotettujen video-kiihdyttimien välille
KABRA, PRATEEK. "IMPLEMENTATION OF REAL TIME OBJECT DETECTION & TRACKING." Thesis, 2012. http://dspace.dtu.ac.in:8080/jspui/handle/repository/13908.
Full textIn this project we present an approach to develop a real-time object tracking system using a static camera to grab the video frames and track an object. The work presents the concepts of histogram matching and absolute frame subtraction to implement a robust automated object tracking system. Once the object is detected it is tracked using discrete Kalman filter technique. The histogram matching algorithm helps to identify when the object enters the viewing range of the camera and the absolute frame subtraction gives better results even with low quality videos. Such a tracking system can be used in surveillance applications and proves to be cost effective. A simulink model is also developed for object tracking for real time video
"Markov random fields based image and video processing." Thesis, 2010. http://library.cuhk.edu.hk/record=b6074890.
Full textMany problems in computer vision involve assigning each pixel a label, which represents some spatially varying quantity such as image intensity in image denoising or object index label in image segmentation. In general, such quantities in image processing tend to be spatially piecewise smooth, since they vary smoothly in the object surface and change dramatically at object boundaries, while in video processing, additional temporal smoothness is satisfied as the corresponding pixels in different frames should have similar labels. Markov random field (MRF) models provide a robust and unified framework for many image and video applications. The framework can be elegantly expressed as an MRF-based energy minimization problem, where two penalty terms are defined with different forms. Many approaches have been proposed to solve the MRF-based energy optimization problem, such as simulated annealing, iterated conditional modes, graph cuts, and belief propagation.
Promising results obtained by the proposed algorithms, with both quantitative and qualitative comparisons to the state-of-the-art methods, demonstrate the effectiveness of our algorithms in these image and video processing applications.
Liu, Ming.
Adviser: Xiaoou Tang.
Source: Dissertation Abstracts International, Volume: 72-04, Section: B, page: .
Thesis (Ph.D.)--Chinese University of Hong Kong, 2010.
Includes bibliographical references (leaves 79-89).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstract also in Chinese.
Books on the topic "SIMULINK MODELS FOR VIDEO PROCESSING"
van den Branden Lambrecht, Christian J., ed. Vision Models and Applications to Image and Video Processing. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-3411-9.
Full textBranden Lambrecht, Christian J. van den., ed. Vision models and applications to image and video processing. Boston: Kluwer Academic, 2001.
Find full textLambrecht, Christian J. Branden. Vision Models and Applications to Image and Video Processing. Boston, MA: Springer US, 2001.
Find full textBinh, Le Nguyen. Nonlinear optical systems: Principles, applications, and advanced signal processing with MATLAB and simulink models. Boca Raton: Taylor & Francis, 2012.
Find full textKatsaggelos, Aggelos Konstantinos. Super resolution of images and video. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool Publishers, 2007.
Find full textMicrocomputer spreadsheet models for libraries: Preparing documents, budgets, and statistical reports. Chicago: American Library Association, 1985.
Find full textWinkler, Stefan. Digital Video Quality: Vision Models and Metrics. Wiley & Sons, Incorporated, John, 2013.
Find full textWinkler, Stefan. Digital Video Quality: Vision Models and Metrics. Wiley & Sons, Incorporated, John, 2007.
Find full textDigital Video Quality: Vision Models and Metrics. Wiley, 2005.
Find full textWinkler, Stefan. Digital Video Quality: Vision Models and Metrics. Wiley & Sons, Limited, John, 2013.
Find full textBook chapters on the topic "SIMULINK MODELS FOR VIDEO PROCESSING"
Delakis, Manolis, Guillaume Gravier, and Patrick Gros. "Stochastic Models for Multimodal Video Analysis." In Multimodal Processing and Interaction, 1–19. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-76316-3_3.
Full textWu, Xiaoyu, Yangsheng Wang, and Jituo Li. "Video Background Segmentation Using Adaptive Background Models." In Image Analysis and Processing – ICIAP 2009, 623–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04146-4_67.
Full textWinkler, Stefan, Murat Kunt, and Christian J. van den Branden Lambrecht. "Vision and Video: Models and Applications." In Vision Models and Applications to Image and Video Processing, 201–29. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-3411-9_10.
Full textMeilhac, Christophe, and Chahab Nastar. "Robust fitting of 3D CAD models to video streams." In Image Analysis and Processing, 661–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63507-6_258.
Full textGong, Shengrong, Chunping Liu, Yi Ji, Baojiang Zhong, Yonggang Li, and Husheng Dong. "Dynamic Scene Classification Based on Topic Models." In Advanced Image and Video Processing Using MATLAB, 429–74. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77223-3_12.
Full textTaskiran, Cuneyt M., Ilya Pollak, Charles A. Bouman, and Edward J. Delp. "Stochastic Models of Video Structure for Program Genre Detection." In Visual Content Processing and Representation, 84–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39798-4_13.
Full textFalcon, Alex, Giuseppe Serra, and Oswald Lanz. "Learning Video Retrieval Models with Relevance-Aware Online Mining." In Image Analysis and Processing – ICIAP 2022, 182–94. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06433-3_16.
Full textLedda, Emanuele, Lorenzo Putzu, Rita Delussu, Giorgio Fumera, and Fabio Roli. "On the Evaluation of Video-Based Crowd Counting Models." In Image Analysis and Processing – ICIAP 2022, 301–11. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06433-3_26.
Full textDaly, Scott. "Engineering Observations from Spatiovelocity and Spatiotemporal Visual Models." In Vision Models and Applications to Image and Video Processing, 179–200. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-3411-9_9.
Full textMrak, Marta, and Toni Zgaljic. "Flexible Motion Models for Scalable and High Definition Video Coding." In Recent Advances in Multimedia Signal Processing and Communications, 209–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02900-4_9.
Full textConference papers on the topic "SIMULINK MODELS FOR VIDEO PROCESSING"
Kanayama, Ai, and Tomonori Tabusa. "Auto-capturing system for facial images from video by Simulink model." In 2010 International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS 2010). IEEE, 2010. http://dx.doi.org/10.1109/ispacs.2010.5704635.
Full textOstroumov, Sergey, Pontus Bostrom, and Marina Walden. "Derivation of Parallel and Resilient Programs from Simulink Models." In 2015 23rd Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). IEEE, 2015. http://dx.doi.org/10.1109/pdp.2015.102.
Full textAyed, Tahar, Adnane Cabani, and Joseph Mouzna. "Performance of Video Reception on T-DMB Embedded Receivers using Simulink Modeling." In Signal and Image Processing and Applications / Artificial Intelligence and Soft Computing. Calgary,AB,Canada: ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.738-050.
Full textLiu, Yuhua, Wei Zhou, Hongcheng Li, and Chenfu Yi. "Time-invariant Lyapunov matrix equation solving by Neural Networks with Simulink models." In 2014 12th International Conference on Signal Processing (ICSP 2014). IEEE, 2014. http://dx.doi.org/10.1109/icosp.2014.7015252.
Full textHai, Jerry Chan Ting, Ooi Chee Pun, and Tan Wooi Haw. "Accelerating video and image processing design for FPGA using HDL coder and simulink." In 2015 IEEE Conference on Sustainable Utilization And Development In Engineering and Technology (CSUDET). IEEE, 2015. http://dx.doi.org/10.1109/csudet.2015.7446221.
Full textBosch, Marc, Fengqing Zhu, and Edward J. Delp. "Spatial Texture Models for Video Compression." In 2007 IEEE International Conference on Image Processing. IEEE, 2007. http://dx.doi.org/10.1109/icip.2007.4378899.
Full textButt, Shahzad Ahmad, and Luciano Lavagno. "Design space exploration and synthesis for digital signal processing algorithms from Simulink models." In 2013 Design and Test Symposium (IDT). IEEE, 2013. http://dx.doi.org/10.1109/idt.2013.6727109.
Full textToreyin, B. U., Y. Dedeoglu, and A. E. Cetin. "Flame detection in video using hidden Markov models." In rnational Conference on Image Processing. IEEE, 2005. http://dx.doi.org/10.1109/icip.2005.1530284.
Full textDing, Yi, and Laiyao Fan. "Object-oriented video structuring via hidden Markov models." In Visual Communications and Image Processing 2005. SPIE, 2005. http://dx.doi.org/10.1117/12.631558.
Full textBarman, Nabajeet, Rahul Vanam, and Yuriy Reznik. "Parametric Quality Models for Multiscreen Video Systems." In 2022 10th European Workshop on Visual Information Processing (EUVIP). IEEE, 2022. http://dx.doi.org/10.1109/euvip53989.2022.9922693.
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