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Intelligent Vision Systems

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Day Time Where Lecture
Feb 28 2-4pm Eng.408 1-2) IntroductionImage segmentation-1
Feb 29 3-4pm Eng.408 3) Image segmentation-2 example
March 6 2-4pm Eng.408 4-5) Image segmentation-2 [1pp] [2pp]Image_Filtering Image_features [1pp] [2pp]
March 7 3-4pm Eng.408 6) Homogeneous coordinates [1pp] ;camera calibration [1pp]
March 9 11am-12pm Case room 2 Extra) Exercises [1pp] ;Trigonometry [1pp] ; Voice mp4 Link
March 13 2-4pm Eng.408 7-8) Tsai calibration: summaryTsai’s paperStereo web cameraCamera distortion
March 14 3-4pm Eng.408 9) Zhang calibration
March 16 11am-12pm Case room 2 Extra 2) Zhang calibration workshop
March 20 2-4pm Eng.408 10-11) Stereo vision: Basics of 3D reconstruction – Epipolar Geometry
March 21 3-4pm Eng.408 12) Basics of 3D Reconstruction: Essential and Fundamental Matrix
March 27 2-4pm Eng.408 13-14) Basics of 3D Reconstruction (cont.): 8-Point Algorithm
March 28 3-4pm Eng.408 15) Basics of 3D Reconstruction (cont.): Image Rectification
April 3 2-4pm Eng.408 16-17) Correlation Based Matching: 26 slides
April 4

 

3-4pm Eng.408 18) Stereo Matching: Illposedness and Regularisation
Mid-semester break: April 7 – April 22
April 24 2-4pm Eng.408 19-20) Dynamic Programming Based Matching: 34 slides
April 25   Eng.408 ANZAC Day
May 1 2-4pm Eng.408 21-22) Belief Propagation Based Matching: 28 slides
May 2 3-4pm Eng.408 Graduation-No class
May 8 2-4pm Eng.408 23-24) Assignment 2 description/PCA
May 9 3-4pm Eng.408 25) PCA-LDA
May 17 11am-12pm Case room 2 Extra 3) PCA-LDA Workshop
May 15 2-4pm Eng.408 26-27) 2D-3D face database generation and assesment
May 16 3-4pm Eng.408 28) CUDA lecture (by Edwin)
May 22 2-4pm Next to Patrice’s office 29-30) Assignment 2 rectification/stereo-matching demos
May 23 3-4pm Eng.408 31)Project – results assessment, reporting guidelines
May 29 2-4pm Eng.408 32-33) Exam preparation
May 30 3-4pm Eng.408 34) Project demo – final wrap-up
01 June 3-4pm Next to Patrice’s office Extra 4) Project demo – final wrap-up

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Lecture notes and additional materials

Recommended reading:

  • R. Hartley and A. Zisserman: Multiple View Geometry in Computer Vision. 2nd edition. Cambridge University Press: Cambridge, UK, 2006.
    • Part 0: Projective geometry, 2D/3D transformations.
    • Part I: Camera geometry and single view geometry.
    • Part II: Two-view geometry: epipolar geometry, fundamental matrix, 3D reconstruction.
  • N. Efford: Digital Image Processing: A Practical Introduction Using JavaTM. Pearson Education Ltd: Essex, UK, 2000.
    • Chapters 1,3: Digital images.
    • Chapters 6, 7: Image filtering and enhancement.
    • Chapter 9: Geometric operations.
    • Chapter 10: Segmentation.
  • C. M. Bishop: Pattern Recognition and Machine Learning. Springer Science+Business Media, LLC: New York, NY, USA, 2006.
    • Chapter 8: Graphical models: inference, factor graphs, sum-product and max-sum algorithms, dynamic programming, loopy belief propagation.

Source:

http://www.cs.auckland.ac.nz/courses/compsci773s1c/lectures/

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