Camera Models class 8

Camera Models class 8 Multiple View Geometry Comp 290-089 Marc Pollefeys Multiple View Geometry course schedule (subject to change) X X Error in two images N measurements (independent Gaussian noise s 2 ) model with d essential parameters (use s= d and s=( N-d ))

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More Probabilistic Models

More Probabilistic Models. Introduction to Artificial Intelligence COS302 Michael L. Littman Fall 2001. Administration. 2/3, 1/3 split for exams Last HW due Wednesday Wrap up Wednesday Sample exam questions later… Example analogies, share, etc. Topics.

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More on Role Models

More on Role Models. Guessing games on Role Models. Guessing game 1. He is a Chinese. He was a gold medal winner in the Olympic Game. He can run fast. He is a hurdler. Liu Xiang. Guessing game 2. He is an American. He is good at making speeches. He has got a prize recently.

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Essentials of class models

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Lecture 13 – More on Matched Models

Lecture 13 – More on Matched Models. Outline Interactions with the matching variables Additive models Diagnostics Efficiency. Leisure world data. Neither ignoring the matching nor fitting individual α j for each matched set is a good idea.

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Class 40: Alternate Computing Models

Class 40: Alternate Computing Models. cs1120 Fall 2009 University of Virginia David Evans. Reminders. To qualify for a presentation your team must send me an email containing the URL of your site (with some working basic functionality) before 4:59pm Sunday

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13. Latent Class Logit Models

13. Latent Class Logit Models. Discrete Parameter Heterogeneity Latent Classes. Latent Class Probabilities. Ambiguous – Classical Bayesian model? The randomness of the class assignment is from the point of view of the observer, not a natural process governed by a discrete distribution.

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Class 4 – More Classifiers

Class 4 – More Classifiers. Ramoza Ahsan, Yun Lu, Dongyun Zhang, Zhongfang Zhuang, Xiao Qin, Salah Uddin Ahmed. Lesson 4.1. Classification Boundaries. Classification Boundaries.

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Class Hidden Markov Models

Class Hidden Markov Models. Markov Chains. First order Markov process set of states, S i transition probabilities, a ij the probability of each state is dependent only on the previous state trivial constraints (transitions must be true probabilities). a AC. A. C. a AA. a AT. a AG. G.

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More on Class Diagrams

More on Class Diagrams. Visibility. Both attributes’ and methods’ visibility can be defined in the class diagram: + public / derived - private underlined static # protected. Ex: All three of these a ttributes are public. Repression tweet + user + when (datetime)

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Machine Learning in Natural Language More on Discriminative models

Machine Learning in Natural Language More on Discriminative models. Dan Roth University of Illinois, Urbana-Champaign danr@cs.uiuc.edu http://L2R.cs.uiuc.edu/~danr. How to Compare?. Generalization (since the representation is the same) How many examples are needed

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Lecture 15: Hierarchical Latent Class Models Based ON

COMP 538 Introduction of Bayesian networks. Lecture 15: Hierarchical Latent Class Models Based ON N. L. Zhang (2002). Hierarchical latent class models for cluster analysis. Journal of Machine Learning Research , to appear. Outline. Motivation Application of LCA in medicine

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Chapter 6 More on Marginal (GEE) Models

Longitudinal Data Fall 2006. Chapter 6 More on Marginal (GEE) Models. Instructors Alan Hubbard Nick Jewell. CD4 versus Time. HIV+ (CD4 Count) Data – some simple analyses using only 2 observations per person.

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Chapter 7 More on Mixed Models

Longitudinal Data Fall 2006. Chapter 7 More on Mixed Models. Instructors Alan Hubbard Nick Jewell. General Method for Choosing Random Effects/Correlation Model. Choose fixed effects model – more elaborate the better.

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More on single-view geometry class 10

More on single-view geometry class 10. Multiple View Geometry Comp 290-089 Marc Pollefeys. Multiple View Geometry course schedule (subject to change). Single view geometry. Camera model Camera calibration Single view geom. Gold Standard algorithm. Objective

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More on Ocean-Atmosphere (General Circulation) Models

More on Ocean-Atmosphere (General Circulation) Models. Outline. Overview of the “fluid dynamics” part of Climate models Two applications of climate models to non Global Warming problems…. !! Not examinable !!. Model (prognostic) variables. Newton’s law+mass conservation

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Last Class: Consistency Models

Last Class: Consistency Models. Need for replication Data-centric consistency Strict, linearizable, sequential, causal, FIFO. Today: Implementation Issues. Replica placement Use web caching as an illustrative example Distribution protocols Invalidate versus updates Push versus Pull

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More on Randomized Class

More on Randomized Class. Def: A language L is in BPP c,s ( 0 s(n)c(n)1, nN) if there exists a probabilistic poly-time TM M s.t. : 1. wL, Pr[M accepts w]  c(|w|) , 2. wL, Pr[M(x) accepts]  s(|w|) . Thm : (Amplification of BPP)

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More progress on ‘ linking together ’ models

More progress on ‘ linking together ’ models. Nick Achilleos Lecturer, Department of Physics University College London With thanks to Patrick Guio, Dugan Witherick and William Nicholson. Report from UCL

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More SD Models

More SD Models. James R. Burns Fall 2010. Today. Recitation Lecture Hands-on. Recitation. In addition to the escalation archetype, what other archetype comes to mind when you consider Barry Richmond’s model of terrorism. Lecture. Deficits & Tax cuts Guns at school. Hands-on.

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More General Transfer Function Models

More General Transfer Function Models. Poles and Zeros: The dynamic behavior of a transfer function model can be characterized by the numerical value of its poles and zeros. General Representation of ATF: There are two equivalent representations:. Chapter 6.

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More complex material models

More complex material models. Ignore subsurface scattering (x,y) in = (x,y) out. Ignore dependence on position. Bidirectional Texture Function (BTF) Spatially-varying BRDF (SVBRDF) = 6D. Bidirectional Subsurface Scattering Distribution Function (BSSRDF) = 6D.

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