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Статті в журналах з теми "Robot vision Mathematical models"
Khodabandehloo, K. "Robotic handling and packaging of poultry products." Robotica 8, no. 4 (October 1990): 285–97. http://dx.doi.org/10.1017/s0263574700000321.
Повний текст джерелаZou, Yanbiao, Jinchao Li, and Xiangzhi Chen. "Seam tracking investigation via striped line laser sensor." Industrial Robot: An International Journal 44, no. 5 (August 21, 2017): 609–17. http://dx.doi.org/10.1108/ir-11-2016-0294.
Повний текст джерелаPanarin, R. N., А. А. Soloviev, and Любовь Анатольевна Хворова. "Application of Artificial Intelligence and Computer Vision Technologies in Solving Problems of Automation of Processing and Recognition of Biological Objects." Izvestiya of Altai State University, no. 1(123) (March 18, 2022): 101–7. http://dx.doi.org/10.14258/izvasu(2022)1-16.
Повний текст джерелаUršič, Peter, Aleš Leonardis, Danijel Skočaj, and Matej Kristan. "Learning part-based spatial models for laser-vision-based room categorization." International Journal of Robotics Research 36, no. 4 (April 2017): 379–402. http://dx.doi.org/10.1177/0278364917704707.
Повний текст джерелаZhang, Xiaoyue, and Liang Huo. "A Vision/Inertia Integrated Positioning Method Using Position and Orientation Matching." Mathematical Problems in Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/6835456.
Повний текст джерелаVarlashin, V. V., and A. V. Lopota. "Optimization of Surround-View System Projection Parameters using Fiducial Markers." Mekhatronika, Avtomatizatsiya, Upravlenie 23, no. 2 (February 6, 2022): 97–103. http://dx.doi.org/10.17587/mau.23.97-103.
Повний текст джерелаSolovyeva, Elena, and Ali Abdullah. "Controlling system based on neural networks with reinforcement learning for robotic manipulator." Information and Control Systems, no. 5 (October 20, 2020): 24–32. http://dx.doi.org/10.31799/1684-8853-2020-5-24-32.
Повний текст джерелаMata, M., J. M. Armingol, J. Fernández, and A. de la Escalera. "Object learning and detection using evolutionary deformable models for mobile robot navigation." Robotica 26, no. 1 (January 2008): 99–107. http://dx.doi.org/10.1017/s0263574707003633.
Повний текст джерелаTrabasso, Luis Gonzaga, and Cezary Zielinski. "Semi-automatic calibration procedure for the vision-robot interface applied to scale model decoration." Robotica 10, no. 4 (July 1992): 303–8. http://dx.doi.org/10.1017/s0263574700008134.
Повний текст джерелаSteiger-Carçao, Adolfo, and L. M. Camarinha-Matos. "Concurrent Pascal as a robot level language – a suggestion." Robotica 4, no. 4 (October 1986): 269–72. http://dx.doi.org/10.1017/s0263574700009966.
Повний текст джерелаДисертації з теми "Robot vision Mathematical models"
Entschev, Peter Andreas. "Efficient construction of multi-scale image pyramids for real-time embedded robot vision." Universidade Tecnológica Federal do Paraná, 2013. http://repositorio.utfpr.edu.br/jspui/handle/1/720.
Повний текст джерелаInterest point detectors, or keypoint detectors, have been of great interest for embedded robot vision for a long time, especially those which provide robustness against geometrical variations, such as rotation, affine transformations and changes in scale. The detection of scale invariant features is normally done by constructing multi-scale image pyramids and performing an exhaustive search for extrema in the scale space, an approach that is present in object recognition methods such as SIFT and SURF. These methods are able to find very robust interest points with suitable properties for object recognition, but at the same time are computationally expensive. In this work we present an efficient method for the construction of SIFT-like image pyramids in embedded systems such as the BeagleBoard-xM. The method we present here aims at using computationally less expensive techniques and reusing already processed information in an efficient manner in order to reduce the overall computational complexity. To simplify the pyramid building process we use binomial filters instead of conventional Gaussian filters used in the original SIFT method to calculate multiple scales of an image. Binomial filters have the advantage of being able to be implemented by using fixed-point notation, which is a big advantage for many embedded systems that do not provide native floating-point support. We also reduce the amount of convolution operations needed by resampling already processed scales of the pyramid. After presenting our efficient pyramid construction method, we show how to implement it in an efficient manner in an SIMD (Single Instruction, Multiple Data) platform -- the SIMD platform we use is the ARM Neon extension available in the BeagleBoard-xM ARM Cortex-A8 processor. SIMD platforms in general are very useful for multimedia applications, where normally it is necessary to perform the same operation over several elements, such as pixels in images, enabling multiple data to be processed with a single instruction of the processor. However, the Neon extension in the Cortex-A8 processor does not support floating-point operations, so the whole method was carefully implemented to overcome this limitation. Finally, we provide some comparison results regarding the method we propose here and the original SIFT approach, including performance regarding execution time and repeatability of detected keypoints. With a straightforward implementation (without the use of the SIMD platform), we show that our method takes approximately 1/4 of the time taken to build the entire original SIFT pyramid, while repeating up to 86% of the interest points found with the original method. With a complete fixed-point approach (including vectorization within the SIMD platform) we show that repeatability reaches up to 92% of the original SIFT keypoints while reducing the processing time to less than 3%.
Nikolaidis, Stefanos. "Mathematical Models of Adaptation in Human-Robot Collaboration." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1121.
Повний текст джерела朱國基 and Kwok-kei Chu. "Design and control of a six-legged mobile robot." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31225895.
Повний текст джерелаLandecker, Will. "Interpretable Machine Learning and Sparse Coding for Computer Vision." PDXScholar, 2014. https://pdxscholar.library.pdx.edu/open_access_etds/1937.
Повний текст джерелаChoy, Siu Kai. "Statistical histogram characterization and modeling : theory and applications." HKBU Institutional Repository, 2008. http://repository.hkbu.edu.hk/etd_ra/913.
Повний текст джерелаEhtiati, Tina. "Strongly coupled Bayesian models for interacting object and scene classification processes." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=102975.
Повний текст джерелаROC curves depicting the scene classification performance of the two models also show that the likelihood coupling model achieves a higher detection rate compared to the prior coupling model. We have also computed the average rise times of the models' outputs as a measure of comparing the speed of the two models. The results show that the likelihood coupling model outputs have a shorter rise time. Based on these experimental findings one can conclude that imposing constraints on the likelihood models provides better solutions to the scene classification problems compared to imposing constraints on the prior models.
We have also proposed an attentional feature modulation scheme, which consists of tuning the input image responses to the bank of Gabor filters based on the scene class probabilities estimated by the model and the energy profiles of the Gabor filters for different scene categories. Experimental results based on combining the attentional feature tuning scheme with the likelihood coupling and the prior coupling methods show a significant improvement in the scene classification performances of both models.
Ngan, Yuk-tung Henry, and 顏旭東. "Motif-based method for patterned texture defect detection." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B40203608.
Повний текст джерелаNifong, Nathaniel H. "Learning General Features From Images and Audio With Stacked Denoising Autoencoders." PDXScholar, 2014. https://pdxscholar.library.pdx.edu/open_access_etds/1550.
Повний текст джерелаNorth, Ben. "Learning dynamical models for visual tracking." Thesis, University of Oxford, 1998. http://ora.ox.ac.uk/objects/uuid:6ed12552-4c30-4d80-88ef-7245be2d8fb8.
Повний текст джерелаBernier, Thomas. "Development of an algorithmic method for the recognition of biological objects." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ29656.pdf.
Повний текст джерелаКниги з теми "Robot vision Mathematical models"
Ji suan zhi neng ji qi zai san wei biao mian sao miao ji qi ren xi tong zhong de ying yong. Dalian Shi: Dalian hai shi da xue chu ban she, 2012.
Знайти повний текст джерелаBochsler, Daniel C. Robotic space simulation: Integration of vision algorithms into an orbital operations simulation. [Houston, Tex.]: Research Institute for Computing and Information Systems, University of Houston--Clear Lake, 1987.
Знайти повний текст джерелаJaklič, Aleš. Segmentation and recovery of superquadrics. Dordrecht: Kluwer Academic Publishers, 2000.
Знайти повний текст джерелаInternational Workshop on Visuomotor Coordination in Amphibians: Experiments, Comparisons, Models, and Robots (1987 Kassel, Germany). Visuomotor coordination: Amphibians, comparisons, models, and robots. New York: Plenum Press, 1989.
Знайти повний текст джерелаParagios, Nikos, Yunmei Chen, and Olivier Faugeras, eds. Handbook of Mathematical Models in Computer Vision. Boston, MA: Springer US, 2006. http://dx.doi.org/10.1007/0-387-28831-7.
Повний текст джерелаMegahed, Saïd M. Principles of robot modelling and simulation. Chichester: Wiley, 1993.
Знайти повний текст джерелаMegahed, Saïd M. Principles of robot modelling and simulation. Chichester: J. Wiley, 1993.
Знайти повний текст джерелаStefan, Türk, ed. The DFVLR models 1 and 2 of the Manutec r3 robot. Oberpfaffenhofen: DFVLR, Institut für Dynamik der Flugsysteme, 1988.
Знайти повний текст джерелаJepson, Allan D. Mixture models for optical flow computation. Toronto: University of Toronto, Dept. of Computer Science, 1993.
Знайти повний текст джерелаJepson, Allan D. What is a percept? Toronto: University of Toronto, Dept. of Computer Science, 1993.
Знайти повний текст джерелаЧастини книг з теми "Robot vision Mathematical models"
Luck, Jason, Dan Small, and Charles Q. Little. "Real-Time Tracking of Articulated Human Models Using a 3D Shape-from-Silhouette Method." In Robot Vision, 19–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44690-7_3.
Повний текст джерелаWeik, Sebastian, and C. E. Liedtke. "Hierarchical 3D Pose Estimation for Articulated Human Body Models from a Sequence of Volume Data." In Robot Vision, 27–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44690-7_4.
Повний текст джерелаCohen, Laurent D. "On Active Contour Models." In Active Perception and Robot Vision, 599–613. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77225-2_31.
Повний текст джерелаJain, Ramesh. "Environment Models and Information Assimilation." In Active Perception and Robot Vision, 217–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77225-2_10.
Повний текст джерелаMundy, Joseph L. "Symbolic Representation of Object Models." In Active Perception and Robot Vision, 189–215. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77225-2_9.
Повний текст джерелаRomero, Pantaleón D., and Vicente F. Candela. "Mathematical Models for Restoration of Baroque Paintings." In Advanced Concepts for Intelligent Vision Systems, 24–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11864349_3.
Повний текст джерелаLegeveldt, B., A. Frangi, S. Mitchell, H. van Assen, S. Ordas, J. Reiber, and M. Sonka. "3D Active Shape and Appearance Models in Cardiac Image Analysis." In Handbook of Mathematical Models in Computer Vision, 471–85. Boston, MA: Springer US, 2006. http://dx.doi.org/10.1007/0-387-28831-7_29.
Повний текст джерелаBudden, David, Shannon Fenn, Alexandre Mendes, and Stephan Chalup. "Evaluation of Colour Models for Computer Vision Using Cluster Validation Techniques." In RoboCup 2012: Robot Soccer World Cup XVI, 261–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39250-4_24.
Повний текст джерелаGu, Xianfeng, Na Lei, and Shing-Tung Yau. "Optimal Transport for Generative Models." In Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging, 1–48. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-03009-4_105-1.
Повний текст джерелаLanza, Alessandro, Serena Morigi, Ivan W. Selesnick, and Fiorella Sgallari. "Convex Non-convex Variational Models." In Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging, 1–57. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-03009-4_61-1.
Повний текст джерелаТези доповідей конференцій з теми "Robot vision Mathematical models"
Mikawa, Masahiko, Masahiro Yoshikawa, and Takeshi Tsujimura. "Memory System Controlled by Mathematical AIM Model for Robot Vision Equipped with Sleep and Wake Functions." In 2006 SICE-ICASE International Joint Conference. IEEE, 2006. http://dx.doi.org/10.1109/sice.2006.315242.
Повний текст джерелаPing, Guiju, Mahdi Abolfazli Esfahani, and Han Wang. "Unsupervised 3D Primitive Shape Detection using Mathematical Models." In 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE, 2020. http://dx.doi.org/10.1109/icarcv50220.2020.9305494.
Повний текст джерелаPing, Guiju, Mahdi Abolfazli Esfahani, and Han Wang. "Unsupervised 3D Primitive Shape Detection using Mathematical Models." In 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE, 2020. http://dx.doi.org/10.1109/icarcv50220.2020.9305494.
Повний текст джерелаYeromina, Nataliia, and Oleksii Lukashyn. "Basic Classes Of Mathematical Models Used In Machine Vision Problems." In COMPUTER AND INFORMATION SYSTEMS AND TECHNOLOGIES. Kharkiv, Ukraine: Press of the Kharkiv National University of Radioelectronics, 2020. http://dx.doi.org/10.30837/ivcsitic2020201372.
Повний текст джерелаZhou, Guangbing, Baosheng Shen, and Jie Yan. "Research on the Algorithm for Solving the Indoor Vision Positioning Model of Mobile Robot." In 2018 International Conference on Mathematics, Modelling, Simulation and Algorithms (MMSA 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/mmsa-18.2018.4.
Повний текст джерела"Person Following through Appearance Models and Stereo Vision Using a Mobile Robot." In International Workshop on Robot Vision. SciTePress - Science and and Technology Publications, 2007. http://dx.doi.org/10.5220/0002069800460056.
Повний текст джерелаWang, Zheng, and Faisal Z. Qureshi. "Topic Models for Image Localization." In 2013 International Conference on Computer and Robot Vision (CRV). IEEE, 2013. http://dx.doi.org/10.1109/crv.2013.36.
Повний текст джерелаLeitner, Jurgen, Alexander Forster, and Jurgen Schmidhuber. "Improving robot vision models for object detection through interaction." In 2014 International Joint Conference on Neural Networks (IJCNN). IEEE, 2014. http://dx.doi.org/10.1109/ijcnn.2014.6889556.
Повний текст джерелаKeselman, Leonid, and Martial Hebert. "Direct Fitting of Gaussian Mixture Models." In 2019 16th Conference on Computer and Robot Vision (CRV). IEEE, 2019. http://dx.doi.org/10.1109/crv.2019.00012.
Повний текст джерелаAndersen, Christensen, and Ravn. "Augmented models for improving vision control of a mobile robot." In Proceedings of IEEE International Conference on Control and Applications CCA-94. IEEE, 1994. http://dx.doi.org/10.1109/cca.1994.381270.
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