Literatura académica sobre el tema "Smoothing image"
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Artículos de revistas sobre el tema "Smoothing image"
BASU, MITRA y MIN SU. "IMAGE SMOOTHING WITH EXPONENTIAL FUNCTIONS". International Journal of Pattern Recognition and Artificial Intelligence 15, n.º 04 (junio de 2001): 735–52. http://dx.doi.org/10.1142/s0218001401001076.
Texto completoSirur, Kedir Kamu, Ye Peng y Qinchuan Zhang. "Smoothing Filters for Waveform Image Segmentation". International Journal of Machine Learning and Computing 7, n.º 5 (octubre de 2017): 139–43. http://dx.doi.org/10.18178/ijmlc.2017.7.5.636.
Texto completoPizarro, Luis, Pavel Mrázek, Stephan Didas, Sven Grewenig y Joachim Weickert. "Generalised Nonlocal Image Smoothing". International Journal of Computer Vision 90, n.º 1 (9 de abril de 2010): 62–87. http://dx.doi.org/10.1007/s11263-010-0337-7.
Texto completoMeer, P., R. H. Park y K. J. Cho. "Multiresolution Adaptive Image Smoothing". CVGIP: Graphical Models and Image Processing 56, n.º 2 (marzo de 1994): 140–48. http://dx.doi.org/10.1006/cgip.1994.1013.
Texto completoXu, Hui-hong y Dong-yuan Ge. "A novel image edge smoothing method based on convolutional neural network". International Journal of Advanced Robotic Systems 17, n.º 3 (1 de mayo de 2020): 172988142092167. http://dx.doi.org/10.1177/1729881420921676.
Texto completoPeng, Anjie, Gao Yu, Yadong Wu, Qiong Zhang y Xiangui Kang. "A Universal Image Forensics of Smoothing Filtering". International Journal of Digital Crime and Forensics 11, n.º 1 (enero de 2019): 18–28. http://dx.doi.org/10.4018/ijdcf.2019010102.
Texto completoEt. al., Ch Kavya ,. "Performance Analysis of Different Filters for Digital Image Processing". Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, n.º 2 (10 de abril de 2021): 2572–76. http://dx.doi.org/10.17762/turcomat.v12i2.2220.
Texto completoHE, QINBIN y FANGYUE CHEN. "DESIGNING CNN GENES FOR BINARY IMAGE EDGE SMOOTHING AND NOISE REMOVING". International Journal of Bifurcation and Chaos 16, n.º 10 (octubre de 2006): 3007–13. http://dx.doi.org/10.1142/s0218127406016604.
Texto completoMa, Xiang, Xuemei Li, Yuanfeng Zhou y Caiming Zhang. "Image smoothing based on global sparsity decomposition and a variable parameter". Computational Visual Media 7, n.º 4 (17 de mayo de 2021): 483–97. http://dx.doi.org/10.1007/s41095-021-0220-1.
Texto completoZhang, Xiaohua, Yuelan Xin y Ning Xie. "Anisotropic Joint Trilateral Rolling Filter for Image Smoothing". Journal of the Institute of Industrial Applications Engineers 7, n.º 3 (25 de julio de 2019): 91–98. http://dx.doi.org/10.12792/jiiae.7.91.
Texto completoTesis sobre el tema "Smoothing image"
Storve, Sigurd. "Kalman Smoothing Techniques in Medical Image Segmentation". Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for elektronikk og telekommunikasjon, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-18823.
Texto completoJarrett, David Ward 1963. "Digital image noise smoothing using high frequency information". Thesis, The University of Arizona, 1987. http://hdl.handle.net/10150/276599.
Texto completoHillebrand, Martin. "On robust corner preserving smoothing in image processing". [S.l. : s.n.], 2003. http://deposit.ddb.de/cgi-bin/dokserv?idn=967514444.
Texto completoOzmen, Neslihan. "Image Segmentation And Smoothing Via Partial Differential Equations". Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610395/index.pdf.
Texto completoActive Contours (Snakes)&rdquo
model and it is correlated with the Chan-Vese model. In this study, all these approaches have been examined in detail. Mathematical and numerical analysis of these models are studied and some experiments are performed to compare their performance.
Athreya, Jayantha Krishna V. "An analog VLSI architecture for image smoothing and segmentation". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0028/MQ39633.pdf.
Texto completoMORGAN, KEITH PATRICK. "IMPROVED METHODS OF IMAGE SMOOTHING AND RESTORATION (NONSTATIONARY MODELS)". Diss., The University of Arizona, 1985. http://hdl.handle.net/10150/187959.
Texto completoRamsay, Tim. "A bivariate finite element smoothing spline applied to image registration". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ54429.pdf.
Texto completoCrespo, José. "Morphological connected filters and intra-region smoothing for image segmentation". Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/15771.
Texto completoPérez, Benito Cristina. "Color Image Processing based on Graph Theory". Doctoral thesis, Universitat Politècnica de València, 2019. http://hdl.handle.net/10251/123955.
Texto completo[CAT] La visió artificial és un dels camps en major creixement en l'actualitat que, junt amb altres tecnlogies com la Biometria o el Big Data, s'ha convertit en el focus d'interés de nombroses investigacions i és considerada com una de les tecnologies del futur. Aquest ampli camp comprén diversos m`etodes entre els quals es troba el processament digital d'imatges i anàlisis d'imatges digitals. L'èxit de l'anàlisis d'imatges i altres tasques de processament d'alt nivell, com poden ser el reconeixement de patrons o la visió 3D, dependrà en gran manera de la bona qualitat de les imatges de partida. Avui dia existeixen multitud de factors que danyen les imatges dificultant l'obtenció d'imatges de qualitat òptima, açò ha convertit el (pre-) processament digital d'imatges en un pas fonamental previa la l'aplicació de qualsevol altra tasca de processament. Els factors més comuns són el soroll i les males condicions d'adquisició: els artefactes provocats pel soroll dificulten la inter- pretació adequada de la imatge i l'adquisició en condicions d'il·luminació o exposició deficients, com a escenes dinàmiques, causen pèrdua d'informació de la imatge que pot ser clau per a certes tasques de processament. Els passos de (pre-) processament d'imatges coneguts com suavitzat i realç s'apliquen comunament per a resoldre aquests problemes: El suavitzat té com a objecte reduir el soroll mentres que el real se centra a millorar o recuperar la informació imprecisa o danyada. Amb aquests mètodes aconseguim reparar informació dels detalls i bords de la imatge amb una nitidesa insuficient o un contingut borrós que impedeix el (post-)processament òptim de la imatge. Existeixen nombrosos mètodes que suavitzen el soroll d'una imatge, no obstant això, en molts casos el procés de filtrat provoca emborronamiento en els bords i detalls de la imatge. De la mateixa manera podem trobar una enorme quantitat de tècniques de realç que intenten combatre les pèrdues d'informació, no obstant això, aquestes tècniques no contemplen l'existència de soroll en la imatge que processen: davant d'una image sorollosa, qualsevol tècnica de realç provocarà també un augment del soroll. Encara que la idea intuïtiva per a solucionar aquest últim cas seria el previ filtrat i posterior realç, aquest enfocament ha demostrat no ser òptim: el filtrat podria eliminar informació que, al seu torn, podria no ser recuperable en el seguënt pas de realç. En la present Tesi doctoral es proposa un model basat en teoria de grafs per al processament d'imatges en color. En aquest model, es construïx un graf per a cada píxel de tal manera que les seues propietats permeten caracteritzar i classificar el píxel en quëstió. Com veurem, el model proposat és robust i capaç d'adaptar-se a una gran varietat d'aplicacions. En particular, apliquem el model per a crear noves solucions als dos problemes fonamentals del processament d'imatges: suavitzat i realç. S'ha estudiat el model en profunditat en funció del llindar, paràmetre clau que assegura la correcta classificació dels píxels de la imatge. A més, també s'han estudiat les possibles característiques i possibilitats del model que ens han permés traure-li el màxim partit en cadascuna de les possibles aplicacions. Basat en aquest model s'ha dissenyat un filtre adaptatiu capaç d'eliminar soroll gaussià d'una imatge sense difuminar els bords ni perdre informació dels detalls. A més, també ha permés desenvolupar un mètode capaç de realçar els bords i detalls d'una imatge al mateix temps que se suavitza el soroll present en la mateixa. Aquesta aplicació simultània aconseguix combinar dues operacions oposades per definició i superar així els inconvenients presentats per l'enfocament en dues etapes.
[EN] Computer vision is one of the fastest growing fields at present which, along with other technologies such as Biometrics or Big Data, has become the focus of interest of many research projects and it is considered one of the technologies of the future. This broad field includes a plethora of digital image processing and analysis tasks. To guarantee the success of image analysis and other high-level processing tasks as 3D imaging or pattern recognition, it is critical to improve the quality of the raw images acquired. Nowadays all images are affected by different factors that hinder the achievement of optimal image quality, making digital image processing a fundamental step prior to the application of any other practical application. The most common of these factors are noise and poor acquisition conditions: noise artefacts hamper proper image interpretation of the image; and acquisition in poor lighting or exposure conditions, such as dynamic scenes, causes loss of image information that can be key for certain processing tasks. Image (pre-) processing steps known as smoothing and sharpening are commonly applied to overcome these inconveniences: Smoothing is aimed at reducing noise and sharpening at improving or recovering imprecise or damaged information of image details and edges with insufficient sharpness or blurred content that prevents optimal image (post-)processing. There are many methods for smoothing the noise in an image, however in many cases the filtering process causes blurring at the edges and details of the image. Besides, there are also many sharpening techniques, which try to combat the loss of information due to blurring of image texture and need to contemplate the existence of noise in the image they process. When dealing with a noisy image, any sharpening technique may amplify the noise. Although the intuitive idea to solve this last case would be the previous filtering and later sharpening, this approach has proved not to be optimal: the filtering could remove information that, in turn, may not be recoverable in the later sharpening step. In the present PhD dissertation we propose a model based on graph theory for color image processing from a vector approach. In this model, a graph is built for each pixel in such a way that its features allow to characterize and classify the pixel. As we will show, the model we proposed is robust and versatile: potentially able to adapt to a variety of applications. In particular, we apply the model to create new solutions for the two fundamentals problems in image processing: smoothing and sharpening. To approach high performance image smoothing we use the proposed model to determine if a pixel belongs to a at region or not, taking into account the need to achieve a high-precision classification even in the presence of noise. Thus, we build an adaptive soft-switching filter by employing the pixel classification to combine the outputs from a filter with high smoothing capability and a softer one to smooth edge/detail regions. Further, another application of our model allows to use pixels characterization to successfully perform a simultaneous smoothing and sharpening of color images. In this way, we address one of the classical challenges within the image processing field. We compare all the image processing techniques proposed with other state-of-the-art methods to show that they are competitive both from an objective (numerical) and visual evaluation point of view.
Pérez Benito, C. (2019). Color Image Processing based on Graph Theory [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/123955
TESIS
Howell, John R. "Analysis Using Smoothing Via Penalized Splines as Implemented in LME() in R". Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1702.pdf.
Texto completoLibros sobre el tema "Smoothing image"
Geological Survey (U.S.), ed. Combining edge-gradient information to improve adaptive discontinuity-preserving smoothing of multispectral images. Reston, VA (521 National Center, Reston 22092): U.S. Geological Survey, 1994.
Buscar texto completoGeological Survey (U.S.), ed. Combining edge-gradient information to improve adaptive discontinuity-preserving smoothing of multispectral images. Reston, VA (521 National Center, Reston 22092): U.S. Geological Survey, 1994.
Buscar texto completoGeological Survey (U.S.), ed. Combining edge-gradient information to improve adaptive discontinuity-preserving smoothing of multispectral images. Reston, VA (521 National Center, Reston 22092): U.S. Geological Survey, 1994.
Buscar texto completoGeological Survey (U.S.), ed. Combining edge-gradient information to improve adaptive discontinuity-preserving smoothing of multispectral images. Reston, VA (521 National Center, Reston 22092): U.S. Geological Survey, 1994.
Buscar texto completoWeinert, Howard L. Fast Compact Algorithms and Software for Spline Smoothing. New York, NY: Springer New York, 2013.
Buscar texto completoCombining edge-gradient information to improve adaptive discontinuity-preserving smoothing of multispectral images. Reston, VA (521 National Center, Reston 22092): U.S. Geological Survey, 1994.
Buscar texto completoCombining edge-gradient information to improve adaptive discontinuity-preserving smoothing of multispectral images. Reston, VA (521 National Center, Reston 22092): U.S. Geological Survey, 1994.
Buscar texto completoButz, Martin V. y Esther F. Kutter. Primary Visual Perception from the Bottom Up. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198739692.003.0008.
Texto completoRose, Molly. Notebook: Negative Image Convenient Composition Book for Kiwi Fruit Smoothie Fans. Independently Published, 2018.
Buscar texto completoSmoothies, Juices and Blended Drinks: 75 Fabulous, Energizing Drinks, with over 200 Images. Anness Publishing, 2013.
Buscar texto completoCapítulos de libros sobre el tema "Smoothing image"
Abidi, Mongi A., Andrei V. Gribok y Joonki Paik. "Regularized 3D Image Smoothing". En Advances in Computer Vision and Pattern Recognition, 197–218. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46364-3_11.
Texto completoBurger, Wilhelm y Mark J. Burge. "Edge-Preserving Smoothing Filters". En Principles of Digital Image Processing, 119–67. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-84882-919-0_5.
Texto completoNoel, Guillaume, Karim Djouani y Yskandar Hamam. "Grid Smoothing for Image Enhancement". En Future Generation Information Technology, 125–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17569-5_14.
Texto completoLiu, Yuying, Yonggang Huang, Jun Zhang, Xu Liu y Hualei Shen. "Noisy Smoothing Image Source Identification". En Cyberspace Safety and Security, 135–47. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69471-9_10.
Texto completoKarlsson, Anders, Jon Bjärkefur, Joakim Rydell y Christina Grönwall. "Smoothing-Based Submap Merging in Large Area SLAM". En Image Analysis, 134–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21227-7_13.
Texto completoPaulus, Dietrich W. R. y Joachim Hornegger. "Filtering and Smoothing Signals". En Pattern Recognition and Image Processing in C++, 263–77. Wiesbaden: Vieweg+Teubner Verlag, 1995. http://dx.doi.org/10.1007/978-3-322-87867-0_19.
Texto completoMahmoodi, Sasan y Steve Gunn. "Scale Space Smoothing, Image Feature Extraction and Bessel Filters". En Image Analysis, 625–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21227-7_58.
Texto completoHu, Xin, Hui Peng, Joseph Kesker, Xiang Cai, William G. Wee y Jing-Huei Lee. "An Improved Adaptive Smoothing Method". En Image Analysis and Processing – ICIAP 2009, 757–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04146-4_81.
Texto completoAverbuch, Amir Z., Pekka Neittaanmaki y Valery A. Zheludev. "Polynomial Smoothing Splines". En Spline and Spline Wavelet Methods with Applications to Signal and Image Processing, 59–68. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-017-8926-4_5.
Texto completoNoel, Guillaume, Karim Djouani y Yskandar Hamam. "Optimisation-Based Image Grid Smoothing for SST Images". En Advanced Concepts for Intelligent Vision Systems, 227–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17691-3_21.
Texto completoActas de conferencias sobre el tema "Smoothing image"
Xu, Li, Cewu Lu, Yi Xu y Jiaya Jia. "Image smoothing viaL0gradient minimization". En the 2011 SIGGRAPH Asia Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2024156.2024208.
Texto completoAlsam, Ali y Hans Jakob Rivertz. "Fast Edge Preserving Smoothing Algorithm". En Signal and Image Processing. Calgary,AB,Canada: ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.759-025.
Texto completoAlsam, Ali y Hans Jakob Rivertz. "Fast Edge Preserving Smoothing Algorithm". En Signal and Image Processing. Calgary,AB,Canada: ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.759-025.
Texto completoWang y Mitra. "Image smoothing based on local image models". En IEEE International Conference on Systems Engineering. IEEE, 1989. http://dx.doi.org/10.1109/icsyse.1989.48625.
Texto completoZhang, Xin, Rui-guang Wang y Xi-feng Zheng. "Application of image smoothing algorithm". En 2010 3rd International Congress on Image and Signal Processing (CISP). IEEE, 2010. http://dx.doi.org/10.1109/cisp.2010.5647444.
Texto completoLetham, Jonathan, Neil M. Robertson y Barry Connor. "Contextual smoothing of image segmentation". En 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops). IEEE, 2010. http://dx.doi.org/10.1109/cvprw.2010.5543910.
Texto completoGustafson, Steven C., Vasiliki E. Nikolaou y Farid Ahmed. "Image smoothing with minimal distortion". En SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, editado por Friedrich O. Huck y Richard D. Juday. SPIE, 1995. http://dx.doi.org/10.1117/12.211975.
Texto completoLi, Liang, Xiaojie Guo, Wei Feng y Jiawan Zhang. "Soft Clustering Guided Image Smoothing". En 2018 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2018. http://dx.doi.org/10.1109/icme.2018.8486448.
Texto completoTaguchi, Akira, Hironori Takashima y Yutaka Murata. "Fuzzy filters for image smoothing". En IS&T/SPIE 1994 International Symposium on Electronic Imaging: Science and Technology, editado por Edward R. Dougherty, Jaakko Astola y Harold G. Longbotham. SPIE, 1994. http://dx.doi.org/10.1117/12.172570.
Texto completoAlvarez, Luis y Julio Esclarin. "Image quantization by nonlinear smoothing". En SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, editado por Leonid I. Rudin y Simon K. Bramble. SPIE, 1995. http://dx.doi.org/10.1117/12.218473.
Texto completoInformes sobre el tema "Smoothing image"
Weiss, Isaac. Image Smoothing and Differentiation with Minimal-Curvature Filters. Fort Belvoir, VA: Defense Technical Information Center, noviembre de 1989. http://dx.doi.org/10.21236/ada215184.
Texto completoWang, Jingyue y Bradley J. Lucier. Error Bounds for Finite-Difference Methods for Rudin-Osher-Fatemi Image Smoothing. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 2009. http://dx.doi.org/10.21236/ada513262.
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