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Статті в журналах з теми "Smoothing image"
BASU, MITRA, and MIN SU. "IMAGE SMOOTHING WITH EXPONENTIAL FUNCTIONS." International Journal of Pattern Recognition and Artificial Intelligence 15, no. 04 (June 2001): 735–52. http://dx.doi.org/10.1142/s0218001401001076.
Повний текст джерелаSirur, Kedir Kamu, Ye Peng, and Qinchuan Zhang. "Smoothing Filters for Waveform Image Segmentation." International Journal of Machine Learning and Computing 7, no. 5 (October 2017): 139–43. http://dx.doi.org/10.18178/ijmlc.2017.7.5.636.
Повний текст джерелаPizarro, Luis, Pavel Mrázek, Stephan Didas, Sven Grewenig, and Joachim Weickert. "Generalised Nonlocal Image Smoothing." International Journal of Computer Vision 90, no. 1 (April 9, 2010): 62–87. http://dx.doi.org/10.1007/s11263-010-0337-7.
Повний текст джерелаMeer, P., R. H. Park, and K. J. Cho. "Multiresolution Adaptive Image Smoothing." CVGIP: Graphical Models and Image Processing 56, no. 2 (March 1994): 140–48. http://dx.doi.org/10.1006/cgip.1994.1013.
Повний текст джерелаXu, Hui-hong, and Dong-yuan Ge. "A novel image edge smoothing method based on convolutional neural network." International Journal of Advanced Robotic Systems 17, no. 3 (May 1, 2020): 172988142092167. http://dx.doi.org/10.1177/1729881420921676.
Повний текст джерелаPeng, Anjie, Gao Yu, Yadong Wu, Qiong Zhang, and Xiangui Kang. "A Universal Image Forensics of Smoothing Filtering." International Journal of Digital Crime and Forensics 11, no. 1 (January 2019): 18–28. http://dx.doi.org/10.4018/ijdcf.2019010102.
Повний текст джерелаEt. al., Ch Kavya ,. "Performance Analysis of Different Filters for Digital Image Processing." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 10, 2021): 2572–76. http://dx.doi.org/10.17762/turcomat.v12i2.2220.
Повний текст джерелаHE, QINBIN, and FANGYUE CHEN. "DESIGNING CNN GENES FOR BINARY IMAGE EDGE SMOOTHING AND NOISE REMOVING." International Journal of Bifurcation and Chaos 16, no. 10 (October 2006): 3007–13. http://dx.doi.org/10.1142/s0218127406016604.
Повний текст джерелаMa, Xiang, Xuemei Li, Yuanfeng Zhou, and Caiming Zhang. "Image smoothing based on global sparsity decomposition and a variable parameter." Computational Visual Media 7, no. 4 (May 17, 2021): 483–97. http://dx.doi.org/10.1007/s41095-021-0220-1.
Повний текст джерелаZhang, Xiaohua, Yuelan Xin, and Ning Xie. "Anisotropic Joint Trilateral Rolling Filter for Image Smoothing." Journal of the Institute of Industrial Applications Engineers 7, no. 3 (July 25, 2019): 91–98. http://dx.doi.org/10.12792/jiiae.7.91.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаJarrett, David Ward 1963. "Digital image noise smoothing using high frequency information." Thesis, The University of Arizona, 1987. http://hdl.handle.net/10150/276599.
Повний текст джерелаHillebrand, Martin. "On robust corner preserving smoothing in image processing." [S.l. : s.n.], 2003. http://deposit.ddb.de/cgi-bin/dokserv?idn=967514444.
Повний текст джерелаOzmen, Neslihan. "Image Segmentation And Smoothing Via Partial Differential Equations." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610395/index.pdf.
Повний текст джерелаActive 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.
Повний текст джерелаMORGAN, KEITH PATRICK. "IMPROVED METHODS OF IMAGE SMOOTHING AND RESTORATION (NONSTATIONARY MODELS)." Diss., The University of Arizona, 1985. http://hdl.handle.net/10150/187959.
Повний текст джерелаRamsay, 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.
Повний текст джерелаCrespo, José. "Morphological connected filters and intra-region smoothing for image segmentation." Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/15771.
Повний текст джерелаPérez, Benito Cristina. "Color Image Processing based on Graph Theory." Doctoral thesis, Universitat Politècnica de València, 2019. http://hdl.handle.net/10251/123955.
Повний текст джерела[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.
Повний текст джерелаКниги з теми "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.
Знайти повний текст джерелаGeological 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.
Знайти повний текст джерелаGeological 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.
Знайти повний текст джерелаGeological 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.
Знайти повний текст джерелаWeinert, Howard L. Fast Compact Algorithms and Software for Spline Smoothing. New York, NY: Springer New York, 2013.
Знайти повний текст джерелаCombining edge-gradient information to improve adaptive discontinuity-preserving smoothing of multispectral images. Reston, VA (521 National Center, Reston 22092): U.S. Geological Survey, 1994.
Знайти повний текст джерелаCombining edge-gradient information to improve adaptive discontinuity-preserving smoothing of multispectral images. Reston, VA (521 National Center, Reston 22092): U.S. Geological Survey, 1994.
Знайти повний текст джерелаButz, Martin V., and 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.
Повний текст джерелаRose, Molly. Notebook: Negative Image Convenient Composition Book for Kiwi Fruit Smoothie Fans. Independently Published, 2018.
Знайти повний текст джерелаSmoothies, Juices and Blended Drinks: 75 Fabulous, Energizing Drinks, with over 200 Images. Anness Publishing, 2013.
Знайти повний текст джерелаЧастини книг з теми "Smoothing image"
Abidi, Mongi A., Andrei V. Gribok, and Joonki Paik. "Regularized 3D Image Smoothing." In 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.
Повний текст джерелаBurger, Wilhelm, and Mark J. Burge. "Edge-Preserving Smoothing Filters." In Principles of Digital Image Processing, 119–67. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-84882-919-0_5.
Повний текст джерелаNoel, Guillaume, Karim Djouani, and Yskandar Hamam. "Grid Smoothing for Image Enhancement." In Future Generation Information Technology, 125–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17569-5_14.
Повний текст джерелаLiu, Yuying, Yonggang Huang, Jun Zhang, Xu Liu, and Hualei Shen. "Noisy Smoothing Image Source Identification." In Cyberspace Safety and Security, 135–47. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69471-9_10.
Повний текст джерелаKarlsson, Anders, Jon Bjärkefur, Joakim Rydell, and Christina Grönwall. "Smoothing-Based Submap Merging in Large Area SLAM." In Image Analysis, 134–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21227-7_13.
Повний текст джерелаPaulus, Dietrich W. R., and Joachim Hornegger. "Filtering and Smoothing Signals." In 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.
Повний текст джерелаMahmoodi, Sasan, and Steve Gunn. "Scale Space Smoothing, Image Feature Extraction and Bessel Filters." In Image Analysis, 625–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21227-7_58.
Повний текст джерелаHu, Xin, Hui Peng, Joseph Kesker, Xiang Cai, William G. Wee, and Jing-Huei Lee. "An Improved Adaptive Smoothing Method." In 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.
Повний текст джерелаAverbuch, Amir Z., Pekka Neittaanmaki, and Valery A. Zheludev. "Polynomial Smoothing Splines." In 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.
Повний текст джерелаNoel, Guillaume, Karim Djouani, and Yskandar Hamam. "Optimisation-Based Image Grid Smoothing for SST Images." In 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.
Повний текст джерелаТези доповідей конференцій з теми "Smoothing image"
Xu, Li, Cewu Lu, Yi Xu, and Jiaya Jia. "Image smoothing viaL0gradient minimization." In the 2011 SIGGRAPH Asia Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2024156.2024208.
Повний текст джерелаAlsam, Ali, and Hans Jakob Rivertz. "Fast Edge Preserving Smoothing Algorithm." In Signal and Image Processing. Calgary,AB,Canada: ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.759-025.
Повний текст джерелаAlsam, Ali, and Hans Jakob Rivertz. "Fast Edge Preserving Smoothing Algorithm." In Signal and Image Processing. Calgary,AB,Canada: ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.759-025.
Повний текст джерелаWang and Mitra. "Image smoothing based on local image models." In IEEE International Conference on Systems Engineering. IEEE, 1989. http://dx.doi.org/10.1109/icsyse.1989.48625.
Повний текст джерелаZhang, Xin, Rui-guang Wang, and Xi-feng Zheng. "Application of image smoothing algorithm." In 2010 3rd International Congress on Image and Signal Processing (CISP). IEEE, 2010. http://dx.doi.org/10.1109/cisp.2010.5647444.
Повний текст джерелаLetham, Jonathan, Neil M. Robertson, and Barry Connor. "Contextual smoothing of image segmentation." In 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.
Повний текст джерелаGustafson, Steven C., Vasiliki E. Nikolaou, and Farid Ahmed. "Image smoothing with minimal distortion." In SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, edited by Friedrich O. Huck and Richard D. Juday. SPIE, 1995. http://dx.doi.org/10.1117/12.211975.
Повний текст джерелаLi, Liang, Xiaojie Guo, Wei Feng, and Jiawan Zhang. "Soft Clustering Guided Image Smoothing." In 2018 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2018. http://dx.doi.org/10.1109/icme.2018.8486448.
Повний текст джерелаTaguchi, Akira, Hironori Takashima, and Yutaka Murata. "Fuzzy filters for image smoothing." In IS&T/SPIE 1994 International Symposium on Electronic Imaging: Science and Technology, edited by Edward R. Dougherty, Jaakko Astola, and Harold G. Longbotham. SPIE, 1994. http://dx.doi.org/10.1117/12.172570.
Повний текст джерелаAlvarez, Luis, and Julio Esclarin. "Image quantization by nonlinear smoothing." In SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, edited by Leonid I. Rudin and Simon K. Bramble. SPIE, 1995. http://dx.doi.org/10.1117/12.218473.
Повний текст джерелаЗвіти організацій з теми "Smoothing image"
Weiss, Isaac. Image Smoothing and Differentiation with Minimal-Curvature Filters. Fort Belvoir, VA: Defense Technical Information Center, November 1989. http://dx.doi.org/10.21236/ada215184.
Повний текст джерелаWang, Jingyue, and Bradley J. Lucier. Error Bounds for Finite-Difference Methods for Rudin-Osher-Fatemi Image Smoothing. Fort Belvoir, VA: Defense Technical Information Center, September 2009. http://dx.doi.org/10.21236/ada513262.
Повний текст джерела