Auswahl der wissenschaftlichen Literatur zum Thema „Histogramme de forces“
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Zeitschriftenartikel zum Thema "Histogramme de forces"
Revelly, JP, F. Feihl, T. Liebling und C. Perret. „Time constant histograms from the forced expired volume signal: a clinical evaluation“. European Respiratory Journal 2, Nr. 6 (01.06.1989): 536–42. http://dx.doi.org/10.1183/09031936.93.02060536.
Der volle Inhalt der QuelleSu, Lian Cheng, Yan E. Shi und Xiao Li Li. „Fault Diagnosis of Bearing Based on the Ultrasonic Amplitude Histogram“. Advanced Materials Research 479-481 (Februar 2012): 1361–64. http://dx.doi.org/10.4028/www.scientific.net/amr.479-481.1361.
Der volle Inhalt der QuelleGurung, Ram B., Tony Lindgren und Henrik Boström. „Learning Random Forest from Histogram Data Using Split Specific Axis Rotation“. International Journal of Machine Learning and Computing 8, Nr. 1 (Februar 2018): 74–79. http://dx.doi.org/10.18178/ijmlc.2018.8.1.666.
Der volle Inhalt der QuelleBuck, Andrew R., James M. Keller und Marjorie Skubic. „A Memetic Algorithm for Matching Spatial Configurations With the Histograms of Forces“. IEEE Transactions on Evolutionary Computation 17, Nr. 4 (August 2013): 588–604. http://dx.doi.org/10.1109/tevc.2012.2226889.
Der volle Inhalt der QuelleGrepl, Jan, Karel Frydrýšek und Marek Penhaker. „A Probabilistic Model of the Interaction between a Sitting Man and a Seat“. Applied Mechanics and Materials 684 (Oktober 2014): 413–19. http://dx.doi.org/10.4028/www.scientific.net/amm.684.413.
Der volle Inhalt der QuelleNi, Jingbo, und Pascal Matsakis. „An equivalent definition of the histogram of forces: Theoretical and algorithmic implications“. Pattern Recognition 43, Nr. 4 (April 2010): 1607–17. http://dx.doi.org/10.1016/j.patcog.2009.09.020.
Der volle Inhalt der QuelleChapple, William D. „Regulation of Muscle Stiffness During Periodic Length Changes in the Isolated Abdomen of the Hermit Crab“. Journal of Neurophysiology 78, Nr. 3 (01.09.1997): 1491–503. http://dx.doi.org/10.1152/jn.1997.78.3.1491.
Der volle Inhalt der QuelleWendling, Laurent, Salvatore Tabbone und Pascal Matsakis. „Fast and robust recognition of orbit and sinus drawings using histograms of forces“. Pattern Recognition Letters 23, Nr. 14 (Dezember 2002): 1687–93. http://dx.doi.org/10.1016/s0167-8655(02)00131-9.
Der volle Inhalt der QuelleLelièvre, Tony, Lise Maurin und Pierre Monmarché. „The adaptive biasing force algorithm with non-conservative forces and related topics“. ESAIM: Mathematical Modelling and Numerical Analysis 56, Nr. 2 (28.02.2022): 529–64. http://dx.doi.org/10.1051/m2an/2022010.
Der volle Inhalt der QuelleKarel, Frydrýšek, Čepica Daniel und Halo Tomáš. „Stochastic Loading of a Sitting Human“. Strojnícky časopis - Journal of Mechanical Engineering 69, Nr. 2 (01.06.2019): 97–110. http://dx.doi.org/10.2478/scjme-2019-0020.
Der volle Inhalt der QuelleDissertationen zum Thema "Histogramme de forces"
Deléarde, Robin. „Configurations spatiales et segmentation pour la compréhension de scènes, application à la ré-identification“. Electronic Thesis or Diss., Université Paris Cité, 2022. http://www.theses.fr/2022UNIP7020.
Der volle Inhalt der QuelleModeling the spatial configuration of objects in an image is a subject that is still little discussed to date, including in the most modern computer vision approaches such as convolutional neural networks ,(CNN). However, it is an essential aspect of scene perception, and integrating it into the models should benefit many tasks in the field, by helping to bridge the “semantic gap” between the digital image and the interpretation of its content. Thus, this thesis aims to improve spatial configuration modeling ,techniques, in order to exploit it in description and recognition systems. ,First, we looked at the case of the spatial configuration between two objects, by proposing an improvement of an existing descriptor. This new descriptor called “force banner” is an extension of the histogram of the same name to a whole range of forces, which makes it possible to better describe complex configurations. We were able to show its interest in the description of scenes, by learning toautomatically classify relations in natural language from pairs of segmented objects. We then tackled the problem of the transition to scenes containing several objects and proposed an approach per object by confronting each object with all the others, rather than having one descriptor per pair. Secondly, the industrial context of this thesis led us to deal with an application to the problem of re-identification of scenes or objects, a task which is similar to fine recognition from few examples. To do so, we rely on a traditional approach by describing scene components with different descriptors dedicated to specific characteristics, such as color or shape, to which we add the spatial configuration. The comparison of two scenes is then achieved by matching their components thanks to these characteristics, using the Hungarian algorithm for instance. Different combinations of characteristics can be considered for the matching and for the final score, depending on the present and desired invariances. For each one of these two topics, we had to cope with the problems of data and segmentation. We then generated and annotated a synthetic dataset, and exploited two existing datasets by segmenting them, in two different frameworks. The first approach concerns object-background segmentation and more precisely the case where a detection is available, which may help the segmentation. It consists in using an existing global segmentation model and exploiting the detection to select the right segment, by using several geometric and semantic criteria. The second approach concerns the decomposition of a scene or an object into parts and addresses the unsupervised case. It is based on the color of the pixels, by using a clustering method in an adapted color space, such as the HSV cone that we used. All these works have shown the possibility of using the spatial configuration for the description of real scenes containing several objects, as well as in a complex processing chain such as the one we used for re-identification. In particular, the force histogram could be used for this, which makes it possible to take advantage of its good performance, by using a segmentation method adapted to the use case when processing natural images
Negri, Pablo Augusto. „Détection et reconnaissance d'objets structurés : application aux transports intelligents“. Paris 6, 2008. http://www.theses.fr/2008PA066346.
Der volle Inhalt der QuelleDeaney, Mogammat Waleed. „A Comparison of Machine Learning Techniques for Facial Expression Recognition“. University of the Western Cape, 2018. http://hdl.handle.net/11394/6412.
Der volle Inhalt der QuelleA machine translation system that can convert South African Sign Language (SASL) video to audio or text and vice versa would be bene cial to people who use SASL to communicate. Five fundamental parameters are associated with sign language gestures, these are: hand location; hand orientation; hand shape; hand movement and facial expressions. The aim of this research is to recognise facial expressions and to compare both feature descriptors and machine learning techniques. This research used the Design Science Research (DSR) methodology. A DSR artefact was built which consisted of two phases. The rst phase compared local binary patterns (LBP), compound local binary patterns (CLBP) and histogram of oriented gradients (HOG) using support vector machines (SVM). The second phase compared the SVM to arti cial neural networks (ANN) and random forests (RF) using the most promising feature descriptor|HOG|from the rst phase. The performance was evaluated in terms of accuracy, robustness to classes, robustness to subjects and ability to generalise on both the Binghamton University 3D facial expression (BU-3DFE) and Cohn Kanade (CK) datasets. The evaluation rst phase showed HOG to be the best feature descriptor followed by CLBP and LBP. The second showed ANN to be the best choice of machine learning technique closely followed by the SVM and RF.
Bui, Manh-Tuan. „Vision-based multi-sensor people detection system for heavy machines“. Thesis, Compiègne, 2014. http://www.theses.fr/2014COMP2156/document.
Der volle Inhalt der QuelleThis thesis has been carried out in the framework of the cooperation between the Compiègne University of Technology (UTC) and the Technical Centre for Mechanical Industries (CETIM). In this work, we present a vision-based multi-sensors people detection system for safety on heavy machines. A perception system composed of a monocular fisheye camera and a Lidar is proposed. The use of fisheye cameras provides an advantage of a wide field-of-view but yields the problem of handling the strong distortions in the detection stage.To the best of our knowledge, no research works have been dedicated to people detection in fisheye images. For that reason, we focus on investigating and quantifying the strong radial distortions impacts on people appearance and proposing adaptive approaches to handle that specificity. Our propositions are inspired by the two state-of-the-art people detection approaches : the Histogram of Oriented Gradient (HOG) and the Deformable Parts Model (DPM). First, by enriching the training data set, we prove that the classifier can take into account the distortions. However, fitting the training samples to the model, is not the best solution to handle the deformation of people appearance. We then decided to adapt the DPM approach to handle properly the problem. It turned out that the deformable models can be modified to be even better adapted to the strong distortions of the fisheye images. Still, such approach has adrawback of the high computation cost and complexity. In this thesis, we also present a framework that allows the fusion of the Lidar modality to enhance the vision-based people detection algorithm. A sequential Lidar-based fusion architecture is used, which addresses directly the problem of reducing the false detections and computation cost in vision-based-only system. A heavy machine dataset have been also built and different experiments have been carried out to evaluate the performances of the system. The results are promising, both in term of processing speed and performances
Martínez-García, Marina. „Statistical analysis of neural correlates in decision-making“. Doctoral thesis, Universitat Pompeu Fabra, 2014. http://hdl.handle.net/10803/283111.
Der volle Inhalt der QuelleDurant aquesta tesi hem investigat els processos neuronals que es pro- dueixen durant tasques de presa de decisions, tasques basades en un ju- dici l ogic de classi caci o perceptual. Per a aquest prop osit hem analitzat tres paradigmes experimentals diferents (somatosensorial, visual i auditiu) en dues espcies diferents (micos i rates), amb l'objectiu d'il.lustrar com les neurones codi quen informaci on referents a les t asques. En particular, ens hem centrat en com certes informacions estan cod- i cades en l'activitat neuronal al llarg del temps. Concretament, com la informaci o sobre: la decisi o comportamental, els factors externs, i la con- ana en la resposta, b e codi cada en la mem oria. A m es a m es, quan el paradigma experimental ens ho va permetre, com l'atenci o modula aquests aspectes. Finalment, hem anat un pas m es enll a, i hem analitzat la comu- nicaci o entre les diferents arees corticals, mentre els subjectes resolien una tasca de presa de decisions.
Nien, Chi-chiao, und 粘智超. „Using Force Histogram in Retrieving Fuzzy Spatial Relationship“. Thesis, 2005. http://ndltd.ncl.edu.tw/handle/00509881555053676889.
Der volle Inhalt der Quelle淡江大學
資訊管理學系碩士班
93
With the popularity of digital image generation and processing tools, huge miscellaneous rich image data have been produced. How to effectively retrieve images from the huge image databases has become an important subject. In the early stage, image retrieval was achieved by matching keywords with image description text. However, the manual input of image description is not only too subjective, but also spends a lot of time, money and manpower. Thus, several researchers proposed successively retrieval methods based on the image content, such as color, texture, shapes of objects, spatial relationships of objects, etc. To obtain a better matching of spatial relationships, we propose several fuzzy spatial relationship characteristic values based on the force histograms among the objects in the images. These values are further used to compute the similarity of two images. This method, compared with direct histogram matching, has the following advantages: (1) It has better computational efficiency; (2) It could precompute the characteristic values of the spatial relationships and and store them in the database, which tremendously saves time in retrieving similar images; (3) These characteristic values are associated with more human-reasonable semantic meanings. Lastly, we demonstrate the use of fuzzy directional, surrounding and distance spatial relationships in image retrieval. The results illustrate that these fuzzy spatial relationships can extract the difference of the spatial relationship among the images more completely. We hope this system could be applied to semantic retrieval of the images in the future.
Buchteile zum Thema "Histogramme de forces"
Santosh, K. C., und Laurent Wendling. „Automated Chest X-ray Image View Classification using Force Histogram“. In Communications in Computer and Information Science, 333–42. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4859-3_30.
Der volle Inhalt der QuelleMatsakis, Pascal. „Understanding the Spatial Organization of Image Regions by Means of Force Histograms: A Guided Tour“. In Applying Soft Computing in Defining Spatial Relations, 99–122. Heidelberg: Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1752-2_5.
Der volle Inhalt der QuelleDebled-Rennesson, Isabelle, und Laurent Wendling. „Extraction of Successive Patterns in Document Images by a New Concept Based on Force Histogram and Thick Discrete Lines“. In Image Analysis and Processing — ICIAP 2015, 387–97. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23231-7_35.
Der volle Inhalt der QuelleVerma, Jyoti, Isha Kansal, Renu Popli, Vikas Khullar, Daljeet Singh, Manish Snehi und Rajeev Kumar. „A Hybrid Images Deep Trained Feature Extraction and Ensemble Learning Models for Classification of Multi Disease in Fundus Images“. In Communications in Computer and Information Science, 203–21. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59091-7_14.
Der volle Inhalt der QuelleHuo, Jing, Matthew S. Brown und Kazunori Okada. „CADrx for GBM Brain Tumors“. In Machine Learning in Computer-Aided Diagnosis, 297–314. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0059-1.ch014.
Der volle Inhalt der Quelle„Force Histograms and Radial Density for Invariant Image Retrieval“. In International Conference on Advanced Computer Theory and Engineering (ICACTE 2009), 95–102. ASME Press, 2009. http://dx.doi.org/10.1115/1.802977.paper11.
Der volle Inhalt der QuelleZaibi, Ghada, Fabrice Peyrard, Abdennaceur Kachouri, Danièle Fournier-Prunaret und Mounir Samet. „A New Encryption Algorithm based on Chaotic Map for Wireless Sensor Network“. In Architectures and Protocols for Secure Information Technology Infrastructures, 103–23. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4514-1.ch004.
Der volle Inhalt der QuelleGoldman, N., und R. J. Saykally. „Elucidating the role of many-body forces in liquid water. I. Simulations of water clusters on the VRT(ASP-W) potential surfaces“. In Quantum Monte Carlo, 148. Oxford University PressNew York, NY, 2007. http://dx.doi.org/10.1093/oso/9780195310108.003.00152.
Der volle Inhalt der QuelleLortie, Christopher J., Joseph Lau und Marc J. Lajeunesse. „Graphical Presentation of Results“. In Handbook of Meta-analysis in Ecology and Evolution. Princeton University Press, 2013. http://dx.doi.org/10.23943/princeton/9780691137285.003.0021.
Der volle Inhalt der QuelleGong, Xiaoliang, Ruiyi Yuan, Hui Qian, Yufei Chen und Anthony G. Cohn. „Emotion Regulation Music Recommendation Based on Feature Selection“. In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210047.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Histogramme de forces"
Kim, K. L., und J. E. Huber. „Observation of the Poling Process in Ferroelectric Ceramics Using Piezoresponse Force Microscopy“. In ASME 2012 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/smasis2012-8037.
Der volle Inhalt der QuelleJazouli, M., J. Wadsworth und P. Matsakis. „Normalization of the Histogram of Forces“. In 8th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007397406300639.
Der volle Inhalt der QuelleJingbo Ni und Pascal Matsakis. „Force histograms computed in O(NlogN)“. In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761010.
Der volle Inhalt der QuelleTateishi, Atsushi, Toshinori Watanabe, Takehiro Himeno, Mizuho Aotsuka und Takeshi Murooka. „Statistical Sensitivity Study of Frequency Mistuning on the Prediction of the Flutter Boundary in a Transonic Fan“. In ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/gt2016-57295.
Der volle Inhalt der QuelleKimpan, Somchok, Noppadol Maneerat und Chom Kimpan. „Diabetic retinopathy image analysis using radial inverse force histograms“. In 2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS). IEEE, 2017. http://dx.doi.org/10.1109/iciibms.2017.8279708.
Der volle Inhalt der QuelleWilkinson, Timothy S., und Joseph W. Goodman. „Slope histogram detection of forged handwritten signatures“. In Fibers '91, Boston, MA, herausgegeben von Michael J. W. Chen. SPIE, 1991. http://dx.doi.org/10.1117/12.25331.
Der volle Inhalt der QuellePutina, Andrian, Mauro Sozio, Dario Rossi und Jose Manuel Navarro. „Random Histogram Forest for Unsupervised Anomaly Detection“. In 2020 IEEE International Conference on Data Mining (ICDM). IEEE, 2020. http://dx.doi.org/10.1109/icdm50108.2020.00154.
Der volle Inhalt der Quelle„A GENERAL ALGORITHM FOR CALCULATING FORCE HISTOGRAMS USING VECTOR DATA“. In International Conference on Pattern Recognition Applications and Methods. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003781600860092.
Der volle Inhalt der QuelleBuck, Andrew R., James M. Keller und Marjorie Skubic. „A modified genetic algorithm for matching building sets with the histograms of forces“. In 2010 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2010. http://dx.doi.org/10.1109/cec.2010.5585935.
Der volle Inhalt der QuelleDebled-Rennesson, Isabelle, und Laurent Wendling. „Combining Force Histogram and Discrete Lines to Extract Dashed Lines“. In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.389.
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