Reconstructing Mirror Symmetric Scenes From a Single View Using 2 ...

Reconstructing Mirror Symmetric Scenes From a Single View Using 2 ...

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  • fiche de synthèse - matière potentielle : the contribution
Abstract We address the problem of 3-D reconstruction from a sin- gle perspective view of a mirror symmetric scene. We establish the fundamental result that it is geometrically equivalent to observing the scene with two cameras, the cameras being symmetrical with respect to the unknown 3- D symmetry plane. All traditional tools of classical 2-view stereo can then be applied, and the concepts of fundamen- tal/essential matrix, epipolar geometry, rectification and disparity hold.
  • view stereo tools
  • symmetric view
  • symmetry lines
  • epipolar geometry
  • camera
  • image
  • mirror
  • point
  • model

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PAD 6705 – Analytical Techniques for Public Administration Spring 2012Robert J Eger IIICourse time: Tuesday & Thursday 4:00 – 5:15pm 650 Bellamy BuildingCourse location: HCB 207 (850) 645-1914 Officehours: Tuesday and Thursday 12:30-1:30pm, reger@fsu.eduby appointment and Introduction This is a required methods course for public administration doctoral students. It stresses quantitative techniques with cross-sectional data. The course emphasizes understanding of the theory underlying the technique, the application of the technique, and the interpretation of the analytical results. The analytical results are placed in a context for an academic audience (presentation and publication). We will cover all the assumptions of the techniques and consequences for violating each technique’s assumptions. Prerequisites and Assumptions 1)A well grounded knowledge of the statistical material from PAD 5701 or equivalent which includes t-tests, correlation, and simple regression. 2)College-level algebra skills. Students with weak math backgrounds are strongly encouraged to seek help from the FSU Math Center or a math tutor. 3)Basic microcomputer skills, an active email account, understanding of how to use the Internet to access class materials, working knowledge of SAS, SPSS, or STATA for data manipulation (e.g., variable recoding, dataset creation), and data dictionary/codebook development, management, and utilization. Learning Objectives 1)Explain the underlying theoretical basis for causal and statistical inference. 2)To recognize and explain how to choose among a variety of common techniques based on a research question and available data. 3)Enhancement of applied skills in using computer program such as SAS, SPSS, or STATA to conduct multivariate analyses. 4)Development of the ability to interpret analytical results and write clear, accurate findings for publication and conference presentation. TextbooksRequired: nd Statistical Models - Theory and Practice, 2edition by David A. Freedman. ISBN:9780521743853. Statistical Models and Causal Inference - A Dialogue with the Social Sciences by David A. Freedman. ISBN:9780521123907. th Statistics, 4edition by Freedman, Pisani, and Purves.ISBN: 9780393929720. Suggested: Namboodiri, Krishnan.Matrix Algebra An Introduction. Fox, John.A Mathematical Primer for Social Statistics. Mitchell, Michael.A Visual Guide to Stata Graphics. Kutner, Michael H., Christopher J Nachtsheim, John Neter, William Li.Applied th Linear Statistical Models 5Edition. nd  Lohr,Sharon L.Sampling: Design and Analysis 2Edition
Long, J. Scott, and Jeremy Freese.Regression Models for Categorical Dependent nd Variables Using StataEdition.. 2  Kennedy,Peter.A Guide to Econometrics. Gujarati,Damodar.Basic Econometrics.  Miller,Jane E.The Chicago Guide to Writing about Numbers. I will also post lecture notes on the class Blackboard site or hand them out in class. Grading Midtermexamination 25%  Paper45%  Manuscriptreview 10%  Homework& class participation20% Midterm Exam The midterm exam is a take-home exam.It is composed of theory questions and applied analyses. You will be given the exam via Blackboard on Friday March 3.The exam is due on March 13 at the beginning of class. Manuscript Review The manuscript review is an important process in the development of Ph.D. students. This is due to the fact that all of you will be asked to review papers from scholarly journals (practitioner or academic) in your fields of interest. You will be graded on thoroughness and approach to the manuscript review. I encourage you to read the manuscript through once and then on your second reading to begin the review. Remember that the goal here is to review the manuscript on its face, that is to review based on how well the author(s) conveys the research question, how the hypotheses relate to that research question, then the appropriateness and correct use of the methods in the manuscript, and the final aspect is whether or not the conclusions are supported in the manuscript. Be thorough and considerate of the fact that this manuscript is providing a limited amount of information on the subject. This review should be a critical analysis of the paper. There is no set format or “correct format.” There are no page limits or stylized specifics, however all reviews I have ever read have always been single spaced. Paper This is an individual paper that is of the quality to send to a peer reviewed journal.The paper may not exceed 35 double spaced pages in 12 point font which is inclusive of the title page, the abstract, and references.All charts, tables, and graphs must be embedded in the body of the paper. Homework Since homework is a critical aspect of your learning experience, I encourage you to work with your classmates.Due Dates All due dates are listed on the course outline below.If you do not provide the homework, paper, manuscript review, or exam on the due dates, the penalty is a 10% point reduction per assignment per day late.After 3 days, I will not accept the late assignment. Attendance PolicyExcused absences include documented illnesses, deaths in the immediate family and other documented crises, call to active military or jury duty, religious holy days, and official University activities. Accommodationsfor these excused absences will be made in a way that does not penalize
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students who have a valid excuse.Consideration will also be given to students whose dependent children experience serious illness.Academic Honor Policy The Florida State University Academic Honor Policy outlines the University’s expectations for the integrity of students’ academic work, the procedures for resolving alleged violations of those expectations, and the rights and responsibilities of students and faculty members throughout the process. Studentsare responsible for reading the Academic Honor Policy and for living up to their pledge to. . . be honest and truthful and . . . [to] strive for personal and institutional integrity at Florida State University.(Florida State University Academic Honor Policy, found at http://dof.fsu.edu/honorpolicy.htm.) Americans With Disabilities Act Students with disabilities needing academic accommodation should: (1) register with and provide documentation to the Student Disability Resource Center; and (2) bring a letter to the instructor indicating the need for accommodation and what type.This should be done during the first week of class. This syllabus and other class materials are available in alternative format upon request.For more information about services available to FSU students with disabilities, contact the: Student Disability Resource Center(850) 644-9566 (voice) 97 Woodward Avenue, South(850) 644-8504 (TDD) 108 Student Services Buildingsdrc@admin.fsu.edu Florida State Universityhttp://www.disabilitycenter.fsu.edu/Tallahassee, FL 32306-4167
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January 5
January 10 & 12
January 17 & 19
January 24
Introduction & Review Statistical Concepts
Observational Studies and Experiments
Freedman et al. – Parts I & II
Freedman – Statistical Models Part 1
Statistical Assumptions
Freedman- Dialogue Parts I & II
Quantitative Reasoning
Freedman- Dialogue Part IV
January 26 & 31Correlation Freedman et al. – Part III Chapters 8 &9 February 2, 7, 9, 14Regression & 16 Freedman et al. – Part III Chapters 10 through 12 Freedman – Statistical Models Parts 2, 3, & 4 February 21, 23,Multiple Regression: Special Topics 28, & March 1 Freedman- Dialogue Part II Freedman – Statistical Models Part 5
March 6 & 8
March 13, 15, & 20
March 22, 27,& 29
April 3 & 5
April 10 & 12
SpringBreak
Path models
Freedman – Statistical Models Part 6
Maximum likelihood
Freedman – Statistical Models Part 7
Bootstrap Freedman – Statistical Models Part 8
Simultaneous Equations
Freedman – Statistical Models Part 9 April 17 & 19Progress or Regress? Freedman- Dialogue Part III April 24 at 5:30pmRESEARCH PAPER DUE
Notes on Blackboard
Homework #1 Due January 26
Homework #2 Due February 14
Homework #3 Due March 1
Take-Home Midterm Due March 13 @ 4:00pm
Manuscript Review Due April 3
Homework #4 Due April 10