My reading list for topics in Computer Vision
This list is divided into two main sections, viz. Geometry-based Methods in Vision and Learning-based Methods in Vision.
- Multi-stage SfM: A Coarse-to-Fine Approach for 3D Reconstruction
- Metrics for 3D Rotation: Comparison and Analysis
- Analyzing 3D Objects in Cluttered Images - NRSfM applied on Cars
- NRSfM Tutorial
- Shape and motion from image streams under orthography: A factorization method - Seminal Work on Factorization based Approaches for Structure Recovery
- Recovering non-rigid 3D shape from image streams - Seminal work on representing non-rigid structure as a combination of basis
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[Computer Vision: Models, Learning, and Inference] (http://web4.cs.ucl.ac.uk/staff/s.prince/book/book.pdf)
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[Computer Vision: Models, Learning, and Inference (Algorithms Booklet)] (http://www0.cs.ucl.ac.uk/external/s.prince/book/Algorithms.pdf)
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[Computer Vision: Models, Learning, and Inference (Answers Booklet for Students)] (http://www0.cs.ucl.ac.uk/external/s.prince/book/AnswerBookletStudents.pdf)
- Robert Collin's lectures
a. [Gaussian Mixtures and the EM Algorithm] (http://www.cse.psu.edu/~rtc12/CSE586/lectures/EMLectureFeb3.pdf)
b. [EM Clarification] (http://www.cse.psu.edu/~rtc12/CSE586/lectures/EMclarifyPXZ.pdf)
c. [EM Derivation, Proof that EM works] (http://www.cse.psu.edu/~rtc12/CSE586/lectures/cse586emDerivation.pdf)
d. [GMM and K-Means] (http://www.cse.psu.edu/~rtc12/CSE586/lectures/cse586gmmemPart1.pdf)
e. [GMM and EM Intro] (http://www.cse.psu.edu/~rtc12/CSE586/lectures/cse586gmmemPart2.pdf)
f. [Mixture of Gaussians Lecture] (http://www.cse.psu.edu/~rtc12/CSE586/lectures/lectureMixGauIntro.pdf)
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[Mixture of Gaussians Tutorial - Reynolds] (http://www.ee.iisc.ernet.in/new/people/faculty/prasantg/downloads/GMM_Tutorial_Reynolds.pdf)
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[Mixture Models and the EM Algorithm - C. Bishop] (http://mlg.eng.cam.ac.uk/tutorials/06/cb.pdf)
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[Estimating Gaussian Mixture Densities with EM: A Tutorial - Tomasi] (http://www.cse.psu.edu/~rtc12/CSE586/papers/emTomasiTutorial.pdf)
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[A Short Tutorial on GMMs] (http://www.computerrobotvision.org/2010/tutorial_day/GMM_said_crv10_tutorial.pdf)
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[A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for GMMs and HMMs] (http://lasa.epfl.ch/teaching/lectures/ML_Phd/Notes/GP-GMM.pdf)
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[Tutorial on Mixture Models] (http://www.homepages.ucl.ac.uk/~ucakche/presentations/cladagtutorial.pdf)
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[Mixture Models and EM] (http://www.cs.toronto.edu/~kyros/courses/2503/Handouts/mixtureModel.pdf)
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[Mixture of Gaussians Tutorial] (https://www.spsc.tugraz.at/system/files/mixtgaussian.pdf)
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[An Introduction to Mixture Models - Frank Picard] (http://www.informatica.uniroma2.it/upload/2009/IM/mixture-tutorial.pdf)
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[Mixture Models] (http://www.stat.cmu.edu/~cshalizi/uADA/12/lectures/ch20.pdf)
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[A Unifying Review of Linear Gaussian Models] (http://mlg.eng.cam.ac.uk/zoubin/papers/lds.pdf)
- Robert Collin's lectures
a. [Introduction to Graphical Models, Belief Propagation] (http://www.cse.psu.edu/~rtc12/CSE586/lectures/cse586GMplusMP.pdf)