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Статті в журналах з теми "Pattern recognition systems – Evaluation"
Elmqvist, Niklas, and Ji Soo Yi. "Patterns for visualization evaluation." Information Visualization 14, no. 3 (December 10, 2013): 250–69. http://dx.doi.org/10.1177/1473871613513228.
Повний текст джерелаDobler, Lorenz, Oganowski Marek, Eckard Rolf, Günsel Andreas, Müller Antje, Kemper Fritz Hubertus, and Wiesmüller Gerhard Andreas. "Rapid Evaluation of Human Biomonitoring Data Using Pattern Recognition Systems." Journal of Toxicology and Environmental Health, Part A 71, no. 11-12 (June 2, 2008): 816–26. http://dx.doi.org/10.1080/15287390801985778.
Повний текст джерелаCao, Runsheng, and Thomas McAvoy. "Evaluation of a pattern recognition adaptive PID controller." Automatica 26, no. 4 (July 1990): 797–801. http://dx.doi.org/10.1016/0005-1098(90)90055-m.
Повний текст джерелаNagaraju, C., D. Sharadamani, C. Maheswari, and D. Vishnu Vardhan. "Evaluation of LBP-Based Facial Emotions Recognition Techniques to Make Consistent Decisions." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 06 (August 12, 2015): 1556008. http://dx.doi.org/10.1142/s021800141556008x.
Повний текст джерелаChang, C. S. "Online transient stability evaluation of interconnected power systems using pattern recognition strategy." IEE Proceedings C Generation, Transmission and Distribution 140, no. 2 (1993): 115. http://dx.doi.org/10.1049/ip-c.1993.0016.
Повний текст джерелаBennett, Laura F. "Knowledge-based evaluation of the segmentation component in automatic pattern recognition systems." Optical Engineering 30, no. 2 (February 1, 1991): 154. http://dx.doi.org/10.1117/12.55782.
Повний текст джерелаFang, Chi, Changsong Liu, Liangrui Peng, and Xiaoqing Ding. "Automatic performance evaluation of printed Chinese character recognition systems." International Journal on Document Analysis and Recognition 4, no. 3 (March 1, 2002): 177–82. http://dx.doi.org/10.1007/s100320200068.
Повний текст джерелаKavdır, İ., and D. E. Guyer. "Evaluation of different pattern recognition techniques for apple sorting." Biosystems Engineering 99, no. 2 (February 2008): 211–19. http://dx.doi.org/10.1016/j.biosystemseng.2007.09.019.
Повний текст джерелаGao, Yuanheng, Leilei Wang, and Heqing Zhang. "Intelligent urban ecological suitability system based on pattern recognition." Journal of Intelligent & Fuzzy Systems 39, no. 4 (October 21, 2020): 5009–16. http://dx.doi.org/10.3233/jifs-179986.
Повний текст джерелаCAPORASO, J. GREGORY, WILLIAM A. BAUMGARTNER, DAVID A. RANDOLPH, K. BRETONNEL COHEN, and LAWRENCE HUNTER. "RAPID PATTERN DEVELOPMENT FOR CONCEPT RECOGNITION SYSTEMS: APPLICATION TO POINT MUTATIONS." Journal of Bioinformatics and Computational Biology 05, no. 06 (December 2007): 1233–59. http://dx.doi.org/10.1142/s0219720007003144.
Повний текст джерелаДисертації з теми "Pattern recognition systems – Evaluation"
Codrescu, Lucian. "An evaluation of the Pica architecture for an object recognition application." Thesis, Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/15483.
Повний текст джерелаEakins, John Paul. "Design and evaluation of a shape retrieval system." Thesis, University of Newcastle Upon Tyne, 1990. http://hdl.handle.net/10443/2056.
Повний текст джерелаPettersson, Johan. "Real-time Object Recognition on a GPU." Thesis, Linköping University, Department of Electrical Engineering, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10238.
Повний текст джерелаShape-Based matching (SBM) is a known method for 2D object recognition that is rather robust against illumination variations, noise, clutter and partial occlusion.
The objects to be recognized can be translated, rotated and scaled.
The translation of an object is determined by evaluating a similarity measure for all possible positions (similar to cross correlation).
The similarity measure is based on dot products between normalized gradient directions in edges.
Rotation and scale is determined by evaluating all possible combinations, spanning a huge search space.
A resolution pyramid is used to form a heuristic for the search that then gains real-time performance.
For SBM, a model consisting of normalized edge gradient directions, are constructed for all possible combinations of rotation and scale.
We have avoided this by using (bilinear) interpolation in the search gradient map, which greatly reduces the amount of storage required.
SBM is highly parallelizable by nature and with our suggested improvements it becomes much suited for running on a GPU.
This have been implemented and tested, and the results clearly outperform those of our reference CPU implementation (with magnitudes of hundreds).
It is also very scalable and easily benefits from future devices without effort.
An extensive evaluation material and tools for evaluating object recognition algorithms have been developed and the implementation is evaluated and compared to two commercial 2D object recognition solutions.
The results show that the method is very powerful when dealing with the distortions listed above and competes well with its opponents.
Orwin, Claire Nicola. "An evaluation of the performance of an optical measurement system for the three-dimensional capture of the shape and dimensions of the human body." Thesis, De Montfort University, 2000. http://hdl.handle.net/2086/4908.
Повний текст джерелаKluever, Kurt Alfred. "Evaluating the usability and security of a video CAPTCHA /." Online version of thesis, 2008. http://hdl.handle.net/1850/7886.
Повний текст джерелаAvan, Selcuk Kazim. "Feature Set Evaluation For A Generic Missile Detection System." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12608130/index.pdf.
Повний текст джерелаPattern Recognition&rsquo
problem of an MDS a hard task. Problem can be defined in two main parts such as &lsquo
Feature Set Evaluation&rsquo
(FSE) and &lsquo
Classifier&rsquo
designs. The main goal of feature set evaluation is to employ a dimensionality reduction process for the input data set, while not disturbing the classification performance in the result. In this thesis study, FSE approaches are investigated for the pattern recognition problem of a generic MDS. First, synthetic data generation is carried out in software environment by employing generic models and assumptions in order to reflect the nature of a realistic problem environment. Then, data sets are evaluated in order to draw a baseline for further feature set evaluation approaches. Further, a theoretical background including the concepts of Class Separability, Feature Selection and Feature Extraction is given. Several widely used methods are assessed in terms of convenience for the problem by giving necessary justifications depending on the data set characteristics. Upon this background, software implementations are performed regarding several feature set evaluation techniques. Simulations are carried out in order to process dimensionality reduction. For the evaluation of the resulting data sets in terms of classification performance, software implementation of a classifier is realized. Resulting classification performances of the applied approaches are compared and evaluated.
Befus, Chad R., and University of Lethbridge Faculty of Arts and Science. "Design and evaluation of dynamic feature-based segmentation on music." Thesis, Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science, c2010, 2010. http://hdl.handle.net/10133/2531.
Повний текст джерелаviii, 94 leaves : ill. ; 29 cm
Ganapathy, Priya. "Development and Evaluation of a Flexible Framework for the Design of Autonomous Classifier Systems." Wright State University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=wright1261335392.
Повний текст джерелаIhnatenko, N. V. "Systems for automatic pattern recognition." Thesis, Сумський державний університет, 2014. http://essuir.sumdu.edu.ua/handle/123456789/34837.
Повний текст джерелаFerreira, Edgar Ricardo. "Procedimentos automáticos para apoio na avaliação de pavimentos com o uso de imagens digitais." Universidade Federal de Viçosa, 2010. http://locus.ufv.br/handle/123456789/819.
Повний текст джерелаCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
Pavements surface distresses are discontinuities on the road which affect the safety and comfort of the user, as well as to possible needs for interventions on the road. The knowledge concerning the pavement layers also works, along with other structural parameters, analyzing further actions on the track. This thesis proposes the use of digital pavement images to detect surface distresses and to find out the pavement layers. This study aims a modern alternative against the current traditional technique of pavement surface defects inspections in Brazil. Firstly, under the spectroradiometer, it was analyzed several patterns relative to the conditions of the asphalt pavement surface in order to define the spectral range that best discriminate against those patterns. By the technique of automatic pattern recognition, it was analyzed the orbital images of high resolution and ground images from the asphalt pavement, using as a rule of decision the algorithm Maximum Likelihood and Artificial Neural Networks. Regarding the features discrimination it applied spectral data to the multispectral images and textural information to the monochrome images. It was worked with a Ground Penetration Radar (GPR) with the purpose of describing the pavement layers determination, thus it acquires the the subsurface profile, resulting in a continuous image of the pavement layers and the automatic classification of images, and in the automatic classification of those images, it was also used textural information to get characteristics as well as the algorithm of the Maximum Likelihood and Artificial Neural Networks as rule of decision. Subsequently, the data obtained in the classification of images, the pavement distresses and its layers in order, set out to ascertain the possibility of using them in a trade Pavement Management System (PMS). The results of classification using orbital images of high resolution were not encouraging, however, when it worked with ground images, the results were surprisingly good, so it indicated a promising possibility in this way. In the classification of the subsurface image pavement, the results were so good so far pretty satisfactory, but lower than the defects classification. Regarding the use of results in the images classification and their use as data in a trade Management System Commercial, it had no major difficulties in this mentioned proceeding.
Defeitos nas superfícies de pavimentos asfálticos são descontinuidades na pista de rolamento que afetam a segurança e o conforto do usuário, além de indicativos da necessidade de intervenções na via. O conhecimento das camadas do pavimento também é utilizado, juntamente com outros parâmetros estruturais, na análise de uma futura intervenção na via. Nesta tese, propõe-se o uso de imagens digitais do pavimento para detectar defeitos superficiais e para identificarr as camadas do pavimento. Quanto aos defeitos superficiais, a motivação é a possibilidade de se obter uma alternativa à técnica de levantamento tradicional de defeitos superficiais em uso no Brasil. Inicialmente, com o auxílio de um espectrorradiômetro, analisaram-se vários padrões correspondentes às condições da superfície do pavimento asfáltico, com o intuito de definir a faixa espectral que melhor discrimine aqueles padrões. Usando-se a técnica de reconhecimento automático de padrões, analisaram-se imagens orbitais de altíssima resolução e terrestre do pavimento asfáltico, empregando como regra de decisão o algoritmo da Máxima Verossimilhança e Redes Neurais Artificiais. Para a fase de discriminação das características, utilizou-se, nas imagens multiespectrais, dados espectrais e nas imagens monocromáticas, informações texturais. Para a determinação das camadas do pavimento usou-se um Ground Penetration Radar (GPR) na aquisição do perfil subsuperficial, obtendo-se uma imagem contínua das camadas do pavimento, e na classificação automática dessas imagens, usou-se, também, informações texturais para extrair características, o algoritmo da Máxima Verossimilhança e Redes Neurais Artificiais como regra de decisão. Posteriormente, com os dados obtidos na classificação sobre as imagens, ou seja, os defeitos superficiais do pavimento e suas camadas, verifica-se a possibilidade de usá-los em um Sistema de Gerência de Pavimentos (SGP) comercial. Os resultados da classificação com o uso de imagem orbital não foram animadores. No entanto, quando se usou imagens terrestres para a classificação, os resultados foram surpreendentemente bons, indicando ser uma possibilidade bastante promissora. Na classificação da imagem subsuperficial do pavimento, os resultados foram razoavelmente satisfatórios, porém, inferiores ao da classificação dos defeitos. Quanto ao uso dos resultados obtidos na classificação das imagens e sua utilização como dados em um Sistema de Gerência de Pavimentos comercial, verificou-se, no sistema comercial analisado, não haver grandes dificuldades neste procedimento.
Книги з теми "Pattern recognition systems – Evaluation"
A, Sadjadi Firooz, ed. Selected papers on performance evaluation of signal and image processing systems. Bellingham, Wash., USA: SPIE Optical Engineering Press, 1993.
Знайти повний текст джерелаZi dong mu biao shi bie ping gu fang fa ji ying yong: Automatic target recognition evaluation method and its application. Beijing: Ke xue chu ban she, 2013.
Знайти повний текст джерелаGrubb, Teryl G. Pattern recognition: A simple model for evaluating wildlife habitat. [Fort Collins, Colo.]: USDA Forest Service, Rocky Mountain Forest and Range Experiment Station, 1988.
Знайти повний текст джерелаRainer, Stiefelhagen, and Garofolo John S, eds. Multimodal technologies for perception of humans: First International Evaluation Workshop on Classification of Events, Activities and Relationships, CLEAR 2006, Southampton, UK, April 6-7, 2006 : revised selected papers. Berlin: Springer, 2007.
Знайти повний текст джерела1967-, Koutroumbas Konstantinos, ed. Pattern recognition. 2nd ed. Amsterdam: Academic Press, 2003.
Знайти повний текст джерела1967-, Koutroumbas Konstantinos, ed. Pattern recognition. San Diego: Academic Press, 1999.
Знайти повний текст джерелаTheodoridis, S. Pattern recognition. 2nd ed. Amsterdam: Academic Press, 2003.
Знайти повний текст джерелаJames, Mike. Pattern recognition. New York: Wiley, 1988.
Знайти повний текст джерелаMorton, Nadler. Pattern recognition engineering. New York: Wiley, 1993.
Знайти повний текст джерелаMagnini, Bernardo. Evaluation of Natural Language and Speech Tools for Italian: International Workshop, EVALITA 2011, Rome, January 24-25, 2012, Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
Знайти повний текст джерелаЧастини книг з теми "Pattern recognition systems – Evaluation"
Grother, Patrick, and Mei Ngan. "Evaluation of Face Recognition Systems." In Advances in Computer Vision and Pattern Recognition, 381–403. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74697-1_17.
Повний текст джерелаBeltrán, Viviana, Mickaël Coustaty, Nicholas Journet, Juan C. Caicedo, and Antoine Doucet. "An Extended Evaluation of the Impact of Different Modules in ST-VQA Systems." In Pattern Recognition and Artificial Intelligence, 562–74. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59830-3_49.
Повний текст джерелаSierszeń, Artur, and Łukasz Sturgulewski. "Evaluation of Reliability of a Decision-Making Process Based on Pattern Recognition." In Computer Recognition Systems 4, 109–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20320-6_12.
Повний текст джерелаKushal, Krishna, Sujala D. Shetty, and Aljo Jose. "Performance Evaluation of a Progression of Recommender System Models." In Computational Intelligence in Pattern Recognition, 245–58. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2543-5_21.
Повний текст джерелаMelton, R. B. "Knowledge Based Systems in Nondestructive Evaluation(a)." In Signal Processing and Pattern Recognition in Nondestructive Evaluation of Materials, 199–204. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-83422-6_15.
Повний текст джерелаMandayam, S., L. Udpa, S. S. Udpa, and W. Lord. "Fuzzy Inference Systems for Invariant Pattern Recognition in MFL NDE." In Review of Progress in Quantitative Nondestructive Evaluation, 805–12. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-0383-1_105.
Повний текст джерелаMarshakov, Daniil V., Vasily V. Galushka, Vladimir A. Fathi, and Denis V. Fathi. "Evaluation of Neural Network Output Results Reliability in Pattern Recognition." In Advances in Intelligent Systems and Computing, 503–10. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01818-4_50.
Повний текст джерелаBhowmik, Tanima, Rohan Mojumder, Dibyendu Ghosh, and Indrajit Banerjee. "An Evaluative Review on Various Tele-Health Systems Proposed in COVID Phase." In Computational Intelligence in Pattern Recognition, 201–10. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3089-8_20.
Повний текст джерелаLiu, Tianliang, Congcong Liang, Xiubin Dai, and Jiebo Luo. "Arithmetic Evaluation System Based on MixNet-YOLOv3 and CRNN Neural Networks." In Pattern Recognition. ICPR International Workshops and Challenges, 344–55. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68821-9_31.
Повний текст джерелаMeza, Pablo, César San Martin, Esteban Vera, and Sergio Torres. "A Quantitative Evaluation of Fixed-Pattern Noise Reduction Methods in Imaging Systems." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 285–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16687-7_40.
Повний текст джерелаТези доповідей конференцій з теми "Pattern recognition systems – Evaluation"
Minglun Ren, Juanjuan Duan, and Shanlin Yang. "Decision models evaluation using fuzzy pattern recognition." In 2007 IEEE International Conference on Grey Systems and Intelligent Services. IEEE, 2007. http://dx.doi.org/10.1109/gsis.2007.4443430.
Повний текст джерелаChen, Zhaoyang, and Guilin Zhang. "General quantitative approach to performance evaluation of automatic target recognition (ATR) systems." In Multispectral Image Processing and Pattern Recognition, edited by Yair Censor and Mingyue Ding. SPIE, 2001. http://dx.doi.org/10.1117/12.441576.
Повний текст джерелаLee, Samuel C., Elisa T. Lee, and Yiming Wang. "New scientific accuracy measure for performance evaluation of human-computer diagnostic systems." In Multispectral Image Processing and Pattern Recognition, edited by Yair Censor and Mingyue Ding. SPIE, 2001. http://dx.doi.org/10.1117/12.441586.
Повний текст джерелаHuang, Shike, Lijuan Li, Baoguo Chen, and Zhenyu Wang. "Performance evaluation system of signal processing algorithms." In Multispectral Image Processing and Pattern Recognition, edited by Yair Censor and Mingyue Ding. SPIE, 2001. http://dx.doi.org/10.1117/12.441579.
Повний текст джерела"Information Theoretic Text Classification Methods Evaluation." In 8th International Workshop on Pattern Recognition in Information Systems. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0001740200770085.
Повний текст джерелаImpedovo, Donato, and Giuseppe Pirlo. "Generating Sets of Classifiers for the Evaluation of Multi-expert Systems." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.530.
Повний текст джерелаLiew, C. K., and M. Veidt. "Optimization of Neural Network Pattern Recognition Systems for Guided Waves Damage Identification in Beams." In REVIEW OF PROGRESS IN QUANTITATIVE NONDESTRUCTIVE EVALUATION. AIP, 2007. http://dx.doi.org/10.1063/1.2718029.
Повний текст джерелаFujigaki, Motoharu, Masafumi Miwa, Atsushi Nakashima, Myung S. Kim, Masato Soga, and Hiroki Tanikawa. "Development of multispectral infrared camera system for plant health evaluation." In Multispectral Image Processing and Pattern Recognition, edited by Qingxi Tong, Yaoting Zhu, and Zhenfu Zhu. SPIE, 2001. http://dx.doi.org/10.1117/12.441398.
Повний текст джерелаRao, Pranav, and J. Manikandan. "Design and evaluation of logistic regression model for pattern recognition systems." In 2016 IEEE Annual India Conference (INDICON). IEEE, 2016. http://dx.doi.org/10.1109/indicon.2016.7839010.
Повний текст джерелаValarmathy, S., M. Arun Kumar, and R. Sangeetha. "Evaluation of face recognition using vector features in local pattern descriptors." In 2016 3rd International Conference on Devices, Circuits and Systems (ICDCS). IEEE, 2016. http://dx.doi.org/10.1109/icdcsyst.2016.7570615.
Повний текст джерелаЗвіти організацій з теми "Pattern recognition systems – Evaluation"
Slyh, Raymond E., Eric G. Hansen, and Timothy R. Anderson. AFRL/HECP Speaker Recognition Systems for the 2004 NIST Speaker Recognition Evaluation. Fort Belvoir, VA: Defense Technical Information Center, December 2004. http://dx.doi.org/10.21236/ada430750.
Повний текст джерелаBodson, D. Simulation and Evaluation of the AT&T Proposed Pattern Recognition Algorithm for Group 4 Facsimile. Fort Belvoir, VA: Defense Technical Information Center, June 1985. http://dx.doi.org/10.21236/ada160864.
Повний текст джерелаVarastehpour, Soheil, Hamid Sharifzadeh, Iman Ardekani, and Abdolhossein Sarrafzadeh. Human Biometric Traits: A Systematic Review Focusing on Vascular Patterns. Unitec ePress, December 2020. http://dx.doi.org/10.34074/ocds.086.
Повний текст джерелаSchneuwly, Sonja, and Caroline Chandler. Evaluation of transformational R&I policy: Lessons learned based on a retrospective review of food systems R&I investment in the EU. Fteval - Austrian Platform for Research and Technology Policy Evaluation, April 2022. http://dx.doi.org/10.22163/fteval.2022.549.
Повний текст джерелаRobinson, Andy. Monitoring and Evaluation for Rural Sanitation and Hygiene: Framework. Institute of Development Studies (IDS), December 2021. http://dx.doi.org/10.19088/slh.2021.027.
Повний текст джерелаRobinson, Andy. Monitoring and Evaluation for Rural Sanitation and Hygiene: Framework. Institute of Development Studies (IDS), December 2021. http://dx.doi.org/10.19088/slh.2021.025.
Повний текст джерелаPyta, V., Bharti Gupta, Shaun Helman, Neale Kinnear, and Nathan Stuttard. Update of INDG382 to include vehicle safety technologies. TRL, July 2020. http://dx.doi.org/10.58446/thco7462.
Повний текст джерела