Academic literature on the topic 'Generalized Procrustes Analysis'
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Journal articles on the topic "Generalized Procrustes Analysis"
Igual, Laura, Xavier Perez-Sala, Sergio Escalera, Cecilio Angulo, and Fernando De la Torre. "Continuous Generalized Procrustes analysis." Pattern Recognition 47, no. 2 (February 2014): 659–71. http://dx.doi.org/10.1016/j.patcog.2013.08.006.
Full textBartoli, Adrien, Daniel Pizarro, and Marco Loog. "Stratified Generalized Procrustes Analysis." International Journal of Computer Vision 101, no. 2 (September 7, 2012): 227–53. http://dx.doi.org/10.1007/s11263-012-0565-0.
Full textBennani Dosse, Mohammed, Henk A. L. Kiers, and Jos M. F. Ten Berge. "Anisotropic generalized Procrustes analysis." Computational Statistics & Data Analysis 55, no. 5 (May 2011): 1961–68. http://dx.doi.org/10.1016/j.csda.2010.11.027.
Full textAdachi, Kohei. "Generalized joint Procrustes analysis." Computational Statistics 28, no. 6 (March 27, 2013): 2449–64. http://dx.doi.org/10.1007/s00180-013-0413-x.
Full textMeyners, Michael, Joachim Kunert, and El Mostafa Qannari. "Comparing generalized procrustes analysis and statis." Food Quality and Preference 11, no. 1-2 (January 2000): 77–83. http://dx.doi.org/10.1016/s0950-3293(99)00038-5.
Full textLangron, S. P., and A. J. Collins. "Perturbation Theory for Generalized Procrustes Analysis." Journal of the Royal Statistical Society: Series B (Methodological) 47, no. 2 (January 1985): 277–84. http://dx.doi.org/10.1111/j.2517-6161.1985.tb01356.x.
Full textXiong, R., K. Blot, J. F. Meullenet, and J. M. Dessirier. "Permutation tests for Generalized Procrustes Analysis." Food Quality and Preference 19, no. 2 (March 2008): 146–55. http://dx.doi.org/10.1016/j.foodqual.2007.03.003.
Full textDijksterhuis, Garmt, and Pieter Punter. "Interpreting generalized procrustes analysis ‘analysis of variance’ tables." Food Quality and Preference 2, no. 4 (January 1990): 255–65. http://dx.doi.org/10.1016/0950-3293(90)90017-o.
Full textWilkinson, Clare, Maarten Schipper, and Tina Leguijt. "Weighted analysis for missing values in generalized procrustes analysis." Food Quality and Preference 11, no. 1-2 (January 2000): 85–90. http://dx.doi.org/10.1016/s0950-3293(99)00027-0.
Full textten Berge, Jos M. F., and Paul A. Bekker. "The isotropic scaling problem in Generalized Procrustes Analysis." Computational Statistics & Data Analysis 16, no. 2 (August 1993): 201–4. http://dx.doi.org/10.1016/0167-9473(93)90114-9.
Full textDissertations / Theses on the topic "Generalized Procrustes Analysis"
Rodrigue, Natalie. "A comparison of the performance of Generalized Procrustes analysis and the intraclass coefficient of correlation to estimate interrater reliability." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0022/MQ50868.pdf.
Full textJohnson, M. E., and n/a. "The application of statistics to the mesoscale study of wind speed and direction in the Canberra region." University of Canberra. Information Sciences, 1986. http://erl.canberra.edu.au./public/adt-AUC20060802.154807.
Full textFraine, Graham. "At home on the road?: Territoriality and driver behaviour." Thesis, Queensland University of Technology, 2003. https://eprints.qut.edu.au/36792/1/36792_Digitised%20Thesis.pdf.
Full textFont, i. Furnols Maria. "Utilització de mascles enters per a la producció de carn: avaluació sensorial i estudis de consumidors." Doctoral thesis, Universitat Politècnica de Catalunya, 2000. http://hdl.handle.net/10803/6499.
Full textBoar taint is a sensory defect mainly detected in boar meat, mainly due to two substances, androstenone and skatole. The main objectives of this study are to ascertain the androstenone and skatole distribution in the pig population of our country with respect to the group of the other European countries; to obtain the sensory characterisation of both compounds by means of a trained panel; to study the acceptability of the boar meat by the European consumers depending on the levels of androstenone and skatole; and to ascertain the sensitivity to androstenone as an influence on the acceptability of boar to Spanish consumers. The levels of androstenone and skatole in subcutaneous fat were measured in 4313 boars and 223 gilts, produced in 6 European countries during two seasons (replicates). In the sensory studies, 42 loins from gilts and 378 loins from boars were selected and the boars were classified in 9 groups depending on the levels of androstenone and skatole (high:>1.0 and >0.21 µg/g respectively, medium and low: <0.5 µg/g and <0.10 µg/g). Panellists previously selected and trained obtained the profile by the checklist method. 'Androstenone', 'skatole', 'pig', 'urine', 'sweat', 'manure', 'sweet', 'rancid' and 'abnormal' were found as odour attributes. The flavour attributes were the same except for 'manure'. Panellists evaluated in a monadic way 10 loins, cooked at 175ºC in the oven for 10 minutes, in each of the 6 sessions of odour and flavour assessment. Consumers (approximately 240 per country and replicate) evaluated 5 samples in a 7 level hedonic scale. The acceptability of the meat flavour was evaluated in loins cooked at 180ºC and re-cooked at 250ºC and the acceptability of the meat odour was evaluated in loins cooked in the same way and kept for 1 minute on a hot plate at 250ºC just before serving. In our country, consumers' sensibility to androstenone was tested. Mean levels of androstenone and skatole in our country (1.20 µg/g and 0.19 µg/g respectively) were above the European mean. The percentage of carcasses with androstenone and skatole levels higher than 1.0 µg/g and 0.21 µg/g was 17% in our country and 7% in all the European countries studied. In the sensory characterisation of the androstenona and the skatole there was a lot of confusion among attributes.. Boar loins with low and medium levels of androstenone and skatole and gilt loins were sensory characterised by the same attribute 'sweet'. Consumers mainly refused the odour of the loins with high levels of skatole, regardless of the androstenone content. The anosmics or less sensitive to androstenone Spanish consumers gave the worst note of acceptability to the loins with high skatole level, independently of the androstenone level. The androstenone level influence in the notes of acceptability given by the highly sensitive consumers to androstenone (24% men, 37% women), are worse when the level of this substance is high or medium. It can be concluded that the androstenone and skatole levels should be controlled, especially in the countries like Spain, where boar meat is consumed and where there are high levels of androstenone as well as with skatole. Boar taint has a complex sensory characterisation. Loins with boar taint have a low level of acceptability with consumers, especially if they are sensitive to androstenone and, if the sensitivity is not taken into account, the importance of androstenone is underestimated. A possible threshold that satisfies the most important part of the consumers in the conditions of this study would be the rejection of carcasses with (a) >0.21 µg/g of skatole, (b) >0.99 µg/g of androstenone or (c) >0.10 µg/g of skatole and >0.5 µg/g of androstenone.
Lima, Verônica Maria Cadena. "Resistant fitting methods for statistical shape comparison." Thesis, University of Leeds, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.275749.
Full textJaafar, Hasan Abdulhussein. "Detection and localisation of structural deformations using terrestrial laser scanning and Generalised Procrustes Analysis." Thesis, University of Nottingham, 2017. http://eprints.nottingham.ac.uk/39376/.
Full textGreen, Stephen. "Validation and extension of the Repertory Grid Technique and Generalised Procrustes Analysis in food acceptability research." Thesis, University of Reading, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.333542.
Full textPeña, Marisol Garcia. "Alternativas de análise para experimentos G × E multiatributo." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-04052016-111857/.
Full textUsually, in the experiments genotype by environment (G×E) it is common to observe the behaviour of genotypes in relation to different attributes in the environments considered. The analysis of such experiments have been widely discussed for the case of a single attribute. This thesis presents some alternatives of analysis, considering genotypes, environments and attributes simultaneously. The first, is based on the mixture maximum likelihood method - Mixclus and the three-mode principal component analysis, these two methods have been very used in the psychology and chemistry, but little in agriculture. The second, is a methodology that combines the additive main effects and multiplicative interaction models - AMMI, efficient model for the analysis of experiments (G×E) with one attribute, and the generalised procrustes analysis, which allows compare configurations of points and provide a numerical measure of how much they differ. Finally, an alternative to perform data imputation in the experiments (G×E) is presented, because, a very frequent situation in these experiments, is the presence of missing values. It is concluded that the proposed methodologies are useful tools for the analysis of experiments (G×E) multi-attribute.
Chen-WeiChou and 周辰威. "Applying Generalized Procrustes Analysis and Support Vector Machine to Develop an Automobile Front View Computer Aided Design System." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/41703634234221657463.
Full text國立成功大學
工業設計學系碩博士班
98
The purpose of this study is to verify if General Procrustes Analysis (GPA) will increase the precision of the results of the sample cluster. This study uses the front view outline of each car (total 50 cars) as a sample, and we classify the sample into nine parts. We use RHINO to draw the outline and find out the coordinate of the points. After adjusting the points using GPA, we use cluster analysis to group our samples, In the past, coordinate of the points is usually classfied through cluster anaalysis directly. However, the clustering results were not always satisfied. In this study, we try to adjust the data through GPA before applying the cluster analysis. The study also tries to adopt Support Vector Regression (SVR) method. We input the clustering results and emotional vocabulary to train the emotional model. The purpose is to verify if there are any differences between the GPA adjusted data and non-adjusted ones. The results suggest that the clustered data after using GPA indeed show lower RMSE. The trained system could be adopted as an expert system to predict the emotional vocabulary scores of new cars.
Hsin-ChengLin and 林辛承. "Applying Generalized Procrustes Analysis in Integrations of Characteristics of Consumer’s Response and Conducting Model Prediction - Using Flash Drives as Case Study." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/41052604071661744737.
Full text國立成功大學
工業設計學系碩博士班
98
This study focuses on applying Generalized Procrustes Analysis (GPA) in conducting Kansei Engineering System (KES) research on flash drives, which is hoped to introduce “flexible questionnaire assessments” as a way of evaluating product perception through the conducting of methodology. And by using of the method, it would become not only a supportive technique of the traditional fashion, but also the primarily measurements of how consumers are thinking. The study includes three aspects for comparing the differences between traditional methods and the proposed method: The extraction of individual features, sample preference mapping, and the effectiveness of clustering. Further discussion would be on the application of subjects’ responsive features inside of the KES research. The result shows that 1. Using GPA on normal questionnaires can derive results similar to the traditional analytical method. 2. GPA can even extract constructs out of flexible questionnaires for further preference mapping of the samples, which normal methods cannot. 3. Conducting flexible questionnaires assessments would have a more comprehensive feedback on sample evaluations than the normal questionnaires. 4. With the use of GPA, the subjects’ affective response can be retrieved and applied, which raises the effectiveness of clustering analysis.
Book chapters on the topic "Generalized Procrustes Analysis"
Crosilla, Fabio, Alberto Beinat, Andrea Fusiello, Eleonora Maset, and Domenico Visintini. "Generalized Isotropic Procrustes Analysis." In Advanced Procrustes Analysis Models in Photogrammetric Computer Vision, 29–43. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11760-3_3.
Full textCrosilla, Fabio, Alberto Beinat, Andrea Fusiello, Eleonora Maset, and Domenico Visintini. "3D Model Registration by Generalized Procrustes Analysis." In Advanced Procrustes Analysis Models in Photogrammetric Computer Vision, 153–72. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11760-3_9.
Full textBrombin, Chiara, and Luigi Salmaso. "Evaluating Power Behavior of Nonparametric Combination Testing Methodology After Generalized Procrustes Analysis and Under Different Correlation Structures." In Permutation Tests in Shape Analysis, 37–52. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8163-8_3.
Full textArnold, G. M. "Scaling Factors in Generalised Procrustes Analysis." In Computational Statistics, 61–66. Heidelberg: Physica-Verlag HD, 1992. http://dx.doi.org/10.1007/978-3-662-26811-7_9.
Full text"Interpreting Generalised Procrustes Analysis “Analysis of Variance” Tables." In Multivariate Data Analysis in Sensory and Consumer Science, 77–96. Trumbull, Connecticut, USA: Food & Nutrition Press, Inc., 2008. http://dx.doi.org/10.1002/9780470385050.ch3.
Full textConference papers on the topic "Generalized Procrustes Analysis"
Bartoli, Adrien, Daniel Pizarro, and Marco Loog. "Stratified Generalized Procrustes Analysis." In British Machine Vision Conference 2010. British Machine Vision Association, 2010. http://dx.doi.org/10.5244/c.24.70.
Full textPizarro, Daniel, and Adrien Bartoli. "Global optimization for optimal generalized procrustes analysis." In 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2011. http://dx.doi.org/10.1109/cvpr.2011.5995677.
Full textTian, Wei Wei, Cheng Jun Tian, and Lei Han. "An improved ICP algorithm based on Generalized Procrustes Analysis." In 2016 3rd International Conference on Mechatronics and Information Technology. Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/icmit-16.2016.160.
Full textKomalasari, Desy, M. Rahmat Widyanto, T. Basaruddin, and Dewi Yanti Liliana. "Shape analysis using generalized procrustes analysis on Active Appearance Model for facial expression recognition." In 2017 International Conference on Electrical Engineering and Computer Science (ICECOS). IEEE, 2017. http://dx.doi.org/10.1109/icecos.2017.8167123.
Full textLu, Weihua, and Jean-François Petiot. "Toward an Affective Design of Products." In ASME 2012 11th Biennial Conference on Engineering Systems Design and Analysis. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/esda2012-82486.
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