Auswahl der wissenschaftlichen Literatur zum Thema „Segmentation texture“
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Zeitschriftenartikel zum Thema "Segmentation texture"
Volkova, Natalya P., und Viktor N. Krylov. „VECTOR-DIFFERENCE TEXTURE SEGMENTATION METHOD IN TECHNICAL AND MEDICAL EXPRESS DIAGNOSTIC SYSTEMS“. Herald of Advanced Information Technology 3, Nr. 4 (20.11.2020): 226–39. http://dx.doi.org/10.15276/hait.04.2020.2.
Der volle Inhalt der QuelleSong, Andy, und Vic Ciesielski. „Texture Segmentation by Genetic Programming“. Evolutionary Computation 16, Nr. 4 (Dezember 2008): 461–81. http://dx.doi.org/10.1162/evco.2008.16.4.461.
Der volle Inhalt der QuelleSoares, Lucas de Assis, Klaus Fabian Côco, Patrick Marques Ciarelli und Evandro Ottoni Teatini Salles. „A Class-Independent Texture-Separation Method Based on a Pixel-Wise Binary Classification“. Sensors 20, Nr. 18 (22.09.2020): 5432. http://dx.doi.org/10.3390/s20185432.
Der volle Inhalt der QuelleRosenholtz, R. „What Statistics Determine Segmentation of Orientation-Defined Textures?“ Perception 26, Nr. 1_suppl (August 1997): 331. http://dx.doi.org/10.1068/v970074.
Der volle Inhalt der QuelleWang, Guodong, Zhenkuan Pan, Qian Dong, Ximei Zhao, Zhimei Zhang und Jinming Duan. „Unsupervised Texture Segmentation Using Active Contour Model and Oscillating Information“. Journal of Applied Mathematics 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/614613.
Der volle Inhalt der QuelleKinge, Sanjaykumar, B. Sheela Rani und Mukul Sutaone. „Restored texture segmentation using Markov random fields“. Mathematical Biosciences and Engineering 20, Nr. 6 (2023): 10063–89. http://dx.doi.org/10.3934/mbe.2023442.
Der volle Inhalt der QuelleGoyal, Aparna, und Reena Gunjan. „Bleeding Detection in Gastrointestinal Images using Texture Classification and Local Binary Pattern Technique: A Review“. E3S Web of Conferences 170 (2020): 03007. http://dx.doi.org/10.1051/e3sconf/202017003007.
Der volle Inhalt der QuelleAnjaiah, P., K. Rajendra Prasad und C. Raghavendra. „Effective Texture Features for Segmented Mammogram Images“. International Journal of Engineering & Technology 7, Nr. 3.12 (20.07.2018): 666. http://dx.doi.org/10.14419/ijet.v7i3.12.16450.
Der volle Inhalt der QuelleTripathi, Bharat, Nidhi Srivastava und Amod Kumar Tiwari. „Innovative quaternion algebra-based segmentation for improved jpeg color texture analysis“. Journal of Autonomous Intelligence 7, Nr. 5 (10.04.2024): 1499. http://dx.doi.org/10.32629/jai.v7i5.1499.
Der volle Inhalt der QuelleManokhin, Denys, und Yaroslav Sokolovskyy. „Intracranial Hemorrhage Segmentation using Neural Network and Riesz Fractional Order Derivative-Based Texture Enhancement“. Computer Design Systems. Theory and Practice 6, Nr. 1 (2024): 1–16. http://dx.doi.org/10.23939/cds2024.01.001.
Der volle Inhalt der QuelleDissertationen zum Thema "Segmentation texture"
Camilleri, Kenneth P. „Multiresolution texture segmentation“. Thesis, University of Surrey, 1999. http://epubs.surrey.ac.uk/843549/.
Der volle Inhalt der QuelleReyes-Aldasoro, Constantino Carlos. „Multiresolution volumetric texture segmentation“. Thesis, University of Warwick, 2004. http://wrap.warwick.ac.uk/67756/.
Der volle Inhalt der QuelleLinnett, L. M. „Multi-texture image segmentation“. Thesis, Heriot-Watt University, 1991. http://hdl.handle.net/10399/856.
Der volle Inhalt der QuellePorter, Robert Mark Stefan. „Texture classification and segmentation“. Thesis, University of Bristol, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389032.
Der volle Inhalt der QuelleHaddad, Stephen. „Texture measures for segmentation“. Master's thesis, University of Cape Town, 2007. http://hdl.handle.net/11427/7461.
Der volle Inhalt der QuelleTexture is an important visual cue in both human and computer vision. Segmenting images into regions of constant texture is used in many applications. This work surveys a wide range of texture descriptors and segmentation methods to determine the state of the art in texture segmentation. Two types of texture descriptors are investigated: filter bank based methods and local descriptors. Filter banks deconstruct an image into several bands, each of which emphasises areas of the image with different properties. Textons are an adaptive histogram method which describes the distribution of typical feature vectors. Local descriptors calculate features from smaller neighbourhoods than filter banks. Some local descriptors calculate a scale for their local neighbourhood to achieve scale invariance. Both local and global segmentation methods are investigated. Local segmentation methods consider each pixel in isolation. Global segmentation methods penalise jagged borders or fragmented regions in the segmentation. Pixel labelling and border detection methods are investigated. Methods for measuring the accuracy of segmentation are discussed. Two data sets are used to test the texture segmentation algorithms. The Brodatz Album mosaics are composed of grayscale texture images from the Brodatz Album. The Berkeley Natural Images data set has 300 colour images of natural scenes. The tests show that, of the descriptors tested, filter bank based textons are the best texture descriptors for grayscale images. Local image patch textons are best for colour images. Graph cut segmentation is best for pixel labelling problems and edge detection with regular borders. Non-maxima suppression is best for edge detection with irregular borders. Factors affecting the performance of the algorithms are investigated.
Pongratananukul, Nattorn. „Texture Segmentation Using Fractal Features“. Honors in the Major Thesis, University of Central Florida, 2000. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/677.
Der volle Inhalt der QuelleBachelors
Engineering
Electrical Engineering
Wen, Wen. „Computational texture analysis and segmentation“. Thesis, University of Strathclyde, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.358812.
Der volle Inhalt der QuellePandit, Sanjay. „Texture segmentation by global optimization“. Thesis, University of Surrey, 1999. http://epubs.surrey.ac.uk/843855/.
Der volle Inhalt der QuelleTan, Tieniu. „Image texture analysis : classification and segmentation“. Thesis, Imperial College London, 1990. http://hdl.handle.net/10044/1/8697.
Der volle Inhalt der QuelleXie, Zhi-Yan. „Multi-scale analysis and texture segmentation“. Thesis, University of Oxford, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.260776.
Der volle Inhalt der QuelleBücher zum Thema "Segmentation texture"
Camilleri, K. P. Approaches to unsupervised texture segmentation. [Guildford: Department of Electronic & Electrical Engineering, University of Surrey, 1997.
Den vollen Inhalt der Quelle findenSpann, Michael. Texture description and segmentation in image processing. Birmingham: University of Aston. Department of Electrical and Electronic Engineering, 1985.
Den vollen Inhalt der Quelle findenFatemi-Ghomi, N. Texture segmentation using wavelet packets and c-means fuzzy clustering. Guildford: Dept. of Electronic and Electrical Engineering, 1995.
Den vollen Inhalt der Quelle findenAnil, Phatak, Chatterji Gano und Ames Research Center, Hrsg. Scene segmentation of natural images using texture measures and back-propagation. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1993.
Den vollen Inhalt der Quelle findenIntelmann, Steven S. Automated, objective texture segmentation of multibeam echosounder data: Seafloor survey and substrate maps from James Island to Ozette Lake, Washington outer coast. Silver Spring, Md: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Ocean Service, National Marine Sanctuary Program, 2007.
Den vollen Inhalt der Quelle findenJähne, Bernd. Digital image processing: Concepts, algorithms, and scientific applications. Berlin: Springer-Verlag, 1991.
Den vollen Inhalt der Quelle findenRegt, L. J. de. Linguistic coherence in biblical Hebrew texts: Arrangement of information, participant reference devices, verb forms, and their contribution to textual segmentation and coherence. Piscataway, NJ: Gorgias Press LLC, 2019.
Den vollen Inhalt der Quelle findenJähne, Bernd. Digital image processing. 6. Aufl. Berlin: Springer, 2005.
Den vollen Inhalt der Quelle findenJähne, Bernd. Digital image processing: Concepts, algorithms, and scientific applications. 4. Aufl. Berlin: Springer, 1997.
Den vollen Inhalt der Quelle findenJähne, Bernd. Digital image processing: Concepts, algorithms, and scientific applications. 3. Aufl. Berlin: Springer-Verlag, 1995.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Segmentation texture"
Dana, Kristin J. „Texture Segmentation“. In Computational Texture and Patterns, 25–37. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-01823-7_4.
Der volle Inhalt der QuelleBajcsy, P., und N. Ahuja. „Hierarchical texture segmentation“. In Computer Vision — ACCV'98, 291–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63931-4_229.
Der volle Inhalt der QuelleHaindl, Michal. „Unsupervised texture segmentation“. In Advances in Pattern Recognition, 1021–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0033333.
Der volle Inhalt der QuelleZwiggelaar, Reyer, und Erika R. E. Denton. „Texture Based Segmentation“. In Digital Mammography, 433–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11783237_58.
Der volle Inhalt der QuellePietikäinen, Matti, Abdenour Hadid, Guoying Zhao und Timo Ahonen. „Texture Classification and Segmentation“. In Computational Imaging and Vision, 69–79. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-748-8_4.
Der volle Inhalt der QuelleWang, Yuzhong, Jie Yang und Yue Zhou. „Unsupervised Color-Texture Segmentation“. In Lecture Notes in Computer Science, 106–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30125-7_14.
Der volle Inhalt der QuelleHaindl, Michal, und Stanislav Mikeš. „Model-Based Texture Segmentation“. In Lecture Notes in Computer Science, 306–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30126-4_38.
Der volle Inhalt der QuelleHung, Chih-Cheng, Enmin Song und Yihua Lan. „Image Texture, Texture Features, and Image Texture Classification and Segmentation“. In Image Texture Analysis, 3–14. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13773-1_1.
Der volle Inhalt der QuelleJenicka, S. „Supervised Texture-Based Segmentation Using Basic Texture Models“. In Land Cover Classification of Remotely Sensed Images, 73–88. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66595-1_4.
Der volle Inhalt der QuelleRuiz-del-Solar, Javier, und Daniel Kottow. „Bio-inspired Texture Segmentation Architectures“. In Biologically Motivated Computer Vision, 444–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45482-9_45.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Segmentation texture"
Turner, Mark R. „Gabor functions and textural segmentation“. In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.wj38.
Der volle Inhalt der QuelleMcKeekin, M. Sue, und William T. Rhodes. „Texture segmentation by threshold decomposition filtering“. In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/oam.1992.thr2.
Der volle Inhalt der QuelleDoretto, Cremers, Favaro und Soatto. „Dynamic texture segmentation“. In ICCV 2003: 9th International Conference on Computer Vision. IEEE, 2003. http://dx.doi.org/10.1109/iccv.2003.1238632.
Der volle Inhalt der QuelleHaindl, Michal, und Stanislav Mikes. „Texture segmentation benchmark“. In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761118.
Der volle Inhalt der QuelleBrown, C. David, Charles S. Ih, Gonzalo R. Arce und David A. Fertell. „Scene segmentation using laser projected structured light“. In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1987. http://dx.doi.org/10.1364/oam.1987.mm3.
Der volle Inhalt der QuelleWilscy, M., und Remya K. Sasi. „Wavelet based Texture Segmentation“. In 2010 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2010. http://dx.doi.org/10.1109/iccic.2010.5705877.
Der volle Inhalt der QuelleBarilla, Maria E., Manuel G. Forero und Michael Spann. „Colour-based texture segmentation“. In INFORMATION OPTICS: 5th International Workshop on Information Optics (WIO'06). AIP, 2006. http://dx.doi.org/10.1063/1.2361244.
Der volle Inhalt der QuelleZhan, De-Chen, Xuan-Jing Shen, Jing-Chun Chen und Zhong-Rong Li. „Fast texture image segmentation“. In San Diego '90, 8-13 July, herausgegeben von Andrew G. Tescher. SPIE, 1990. http://dx.doi.org/10.1117/12.23536.
Der volle Inhalt der QuelleTodorovic, Sinisa, und Narendra Ahuja. „Texel-based texture segmentation“. In 2009 IEEE 12th International Conference on Computer Vision (ICCV). IEEE, 2009. http://dx.doi.org/10.1109/iccv.2009.5459308.
Der volle Inhalt der QuelleGiraud, Remi, Vinh-Thong Ta, Nicolas Papadakis und Yannick Berthoumieu. „Texture-Aware Superpixel Segmentation“. In 2019 IEEE International Conference on Image Processing (ICIP). IEEE, 2019. http://dx.doi.org/10.1109/icip.2019.8803085.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Segmentation texture"
Du, Li-Jen. Segmentation of Synthetic Aperture Radar (SAR) Images of Ocean Surface by the Texture Energy Transform Method. Fort Belvoir, VA: Defense Technical Information Center, August 1988. http://dx.doi.org/10.21236/ada199536.
Der volle Inhalt der QuelleKoop, L., M. Snellen und D. Simons. Classifying the seafloor using a textural segmentation approach. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2017. http://dx.doi.org/10.4095/305875.
Der volle Inhalt der QuelleWard, Anderson, Gamal Seedahmend, Greg Anderson und Fred Zhang. Integration of Multi-Tension Permeametry and Photogrammetric Textural Segmentation for Estimating Directional Permeability. Fort Belvoir, VA: Defense Technical Information Center, April 2010. http://dx.doi.org/10.21236/ada571373.
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