[{"command":"insert","method":null,"selector":".course-modal-ajax-placeholder-963039","data":"\u003Cdiv class=\u0022container content-rte\u0022\u003E\n  \u003Cdiv class=\u0022row\u0022\u003E\n    \u003Cdiv class=\u0022box col-md-6 bg-primary text-white\u0022\u003E\n      \u003Ch2 class=\u0022h1 text-left\u0022\u003E  Advanced artificial perception\n\u003C\/h2\u003E\n                  \u003Cdiv class=\u0022field-spacing\u0022\u003E\n        \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EECTS :\n          3\n        \u003C\/div\u003E\n      \u003C\/div\u003E\n                  \u003Cdiv class=\u0022field-spacing\u0022\u003E\n        \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ESemestre calendaire :\n          Semestre 1\n        \u003C\/div\u003E\n      \u003C\/div\u003E\n            \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ED\u00e9tail du volume horaire\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                          \u003Cspan class=\u0022field__label h4 mt-5 \u0022\u003ECours magistraux :\u003C\/span\u003E\n                                      \u003Cspan\u003E12\u003C\/span\u003E\n                      \u003C\/div\u003E\n                                \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                          \u003Cspan class=\u0022field__label h4 mt-5 \u0022\u003ETravaux pratiques :\u003C\/span\u003E\n                                      \u003Cspan\u003E8\u003C\/span\u003E\n                      \u003C\/div\u003E\n                                \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                          \u003Cspan class=\u0022field__label h4 mt-5 \u0022\u003ETravaux dirig\u00e9s :\u003C\/span\u003E\n                                      \u003Cspan\u003E10\u003C\/span\u003E\n                      \u003C\/div\u003E\n                                                            \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ELangue d\u0026#039;enseignement\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003EAnglais\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EEnseignement \u00e0 distance\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003Enon\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n      \u003C\/div\u003E\n    \u003Cdiv class=\u0022box col-md-6\u0022\u003E\n            \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EPr\u00e9requis\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003ESignal and image processing\u003C\/p\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EProgramme \/ plan \/ contenus\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003E\u003Cstrong\u003ECourse description:\u003C\/strong\u003E\u003C\/p\u003E\n\u003Cp\u003EPerception is one of the key processes in autonomous systems. It is based on the integration of perceptual information into a coherent description of the world. Perception consists of collecting, processing, as well as combining data that provide from embedded sensors in order to perform a given task (obstacle detection, scene reconstruction, object recognition, etc.). The objective of the course is to focus on the two main current challenges in perception: vision and fusion. First, the course discusses various sensors and sensor technologies relevant to intelligent and autonomous systems, modern industries and smart product. Then, the course will particularly focus on high level vision algorithms that are based on stereovision or motion analysis. Finally, it discusses the concept and techniques of multi sensor fusion.\u003C\/p\u003E\n\u003Cp\u003E\u003Cstrong\u003ECourse contents:\u003C\/strong\u003E\u003C\/p\u003E\n\u003Cp\u003E\u003Cem\u003EGeneral concepts about perception for autonomous systems\u003C\/em\u003E\u003C\/p\u003E\n\u003Cp\u003EHuman versus artificial perception.\u003C\/p\u003E\n\u003Cp\u003EPerception + decision + action paradigm.\u003C\/p\u003E\n\u003Cp\u003ESensor and sensing. Sensor characteristics and technologies.\u0026nbsp;\u003C\/p\u003E\n\u003Cp\u003EThe role of sensors and categorization.\u003C\/p\u003E\n\u003Cp\u003E\u003Cem\u003EAdvanced Vision concepts for autonomous systems\u003C\/em\u003E\u003C\/p\u003E\n\u003Cp\u003EBasic image analysis (enhancement, edge detection, segmentation).\u003C\/p\u003E\n\u003Cp\u003EFeatures (SIFT, SURF, etc.) for image matching.\u003C\/p\u003E\n\u003Cp\u003EMatching algorithms.\u003C\/p\u003E\n\u003Cp\u003E3D vision and theory of stereovision.\u0026nbsp;\u003C\/p\u003E\n\u003Cp\u003E2D and 3D Motion analysis: optical flow, egomotion estimation.\u003C\/p\u003E\n\u003Cp\u003ETracking of moving objects\u003C\/p\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n                \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EOrganisation g\u00e9n\u00e9rale et modalit\u00e9s p\u00e9dagogiques\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003EThe course is provided through lectures, tutorials, and practical sessions.\u003C\/p\u003E\n\u003Cp\u003EThe practical sessions are graded. The final grade for the module is calculated as follows:\u003C\/p\u003E\n\u003Cp\u003ESession 1: Continuous assessment grade: 30% practical work + 70% exam.\u003C\/p\u003E\n\u003Cp\u003ESession 2 (retake): 30% of the practical work grade from Session 1 + 70% retake exam.\u003C\/p\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n                        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EBibliographie\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003ESome of the references of interest are (but not limited to):\u003C\/p\u003E\n\u003Cul\u003E\n\u003Cli\u003EH.R Everett, \u003Cem\u003ESensors for Mobile Robots Theory and Applications\u003C\/em\u003E, A.K Peters, Ltd., 1995.\u003C\/li\u003E\n\u003Cli\u003ER. Joshi, and A. C. Sanderson, \u003Cem\u003EMultisensor Fusion: A Minimal Representation Framework\u003C\/em\u003E, World Scientific, 1999.\u003C\/li\u003E\n\u003Cli\u003ED. L. Hall, \u003Cem\u003EMathematical techniques in multisensor data fusion\u003C\/em\u003E. Boston: Artech House, 1992.\u0026nbsp;\u003C\/li\u003E\n\u003Cli\u003EBrian D. O. Anderson, John B. Moore, \u003Cem\u003EOptimal Filtering\u003C\/em\u003E. Information and System Science Series. Thomas Kailath Editor, 2005.\u003C\/li\u003E\n\u003Cli\u003ER. Szeliski_, Computer Vision: Algorithms and Applications_, springer, 2010.\u003C\/li\u003E\n\u003Cli\u003ET. Watanabe. \u003Cem\u003EHigh-Level Motion Processing\u003C\/em\u003E, MIT Press, 1998.\u003C\/li\u003E\n\u003C\/ul\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ENature de l\u0026#039;\u00e9valuation\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003EEvaluation Continue non Int\u00e9grale\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n      \u003C\/div\u003E\n  \u003C\/div\u003E\n\u003C\/div\u003E\n","settings":null}]