PPT-Computer and Robot Vision

I Chapter 8 The Facet Model pptccC8SJx Presented by 陳毅 b03202042ntuedutw 指導 教授 傅楸善 博士 Digital Camera and Computer Vision Laboratory Department of Computer Science and Information Engineering. Robot Obstacle domainofresponsibility (a)Robottoblame Robot Obstacle (b)Bothtoblame Robot domainofresponsibility (c)Sensor-dependentdo-mainofresponsibility Robot Obstacle b Collision (d)Collision,butb T. . Bajd. and M. . Mihelj. Proprioceptive sensors. position . velocity . joint torques. Exteroceptive. sensors. force sensors . tactile sensors . proximity sensors. distance sensors. Robot sensors. 2011/12/08. Robot Detection. Robot Detection. Better Localization and Tracking. No Collisions with others. Goal. Robust . Robot . Detection. Long . Range. Short. . Range. Long Range. C. urrent . M. ethod. Chapter 5 . The Normal Distribution. Univariate. Normal Distribution. For short we write:. Univariate. normal distribution describes single continuous variable.. Takes 2 parameters . m. and . s. 2. Embedded Systems . Design Though Curriculum. Jacqueline Bannister. Luke Harvey. Jacob . Holen. Jordan Petersen. Client: Computer Engineering Department. Advisors: . Akhilesh. . Tyagi. – Jason Boyd. Daniele . Mazzei. , . Abolfazl. . Zaraki. , Nicole . Lazzeri. and . Danilo. De Rossi. Presentation by: Kaixi Wu. “Social Robots”. Humans are fascinated by robots that can understand and express emotions. Matanya Elchanani and Tarek Sobh. University of Bridgeport. Department of Computer Science and Engineering. Robotics, Intelligent Sensing and Control. RISC Laboratory. The Basic Idea. Robots are controlled locally. Chapter . 2 . Introduction to probability. Please send errata to s.prince@cs.ucl.ac.uk. Random variables. A random variable . x. denotes a quantity that is uncertain. May be result of experiment (flipping a coin) or a real world measurements (measuring temperature). Activity (60 minutes). 1. Summary so far. The parts of a robot are. A computer that needs to be programmed . (to make decisions). Inputs . (to ‘sense’ via sensors). Outputs . (to ‘act’, e.g., via motors). Activity (60 minutes). 1. Summary so far. The parts of a robot are. A computer that needs to be programmed . (to make decisions). Inputs . (to ‘sense’ via sensors). Outputs . (to ‘act’, e.g., via motors). 1. Image Resampling. Example: . Downscaling from 5×5 to 3×3 pixels. Centers of output pixels mapped onto input image. February 8, 2018. Computer Vision Lecture 4: Color. Walter J. . Scheirer. , . Samuel . E. . Anthony, Ken Nakayama & David . D. . Cox. IEEE Transactions on Pattern Analysis and Machine Intelligence (2014), 36(8), 1679-1686. Presented by: Talia Retter. About the class. COMP 648: Computer Vision Seminar. Instructor: . Vicente. . Ordóñez. (Vicente . Ordóñez. Román). Website: . https://www.cs.rice.edu/~vo9/cv-seminar. Location: Zoom – Keck Hall 101. Goal: . Perform kinematic calibration of Galen surgical robot and integrate results into software to improve its accuracy. Background: . Robot is very precise, but manufacturing tolerances reduce accuracy..