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--- ece4580:questionsformative [2017/01/31 10:23] – [Topic 3: Camera Calibration] pvela
+++ ece4580:questionsformative [2024/08/20 21:38] (current) – external edit 127.0.0.1
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 ====== ECE4580: Formative Questions ======
-The questions below are meant to highlight key aspects or principles related to the reading.  Being able to answer them prior to the lectures of that week should provide you with some nominal uderstanding of what will be covered so that you may focus on understanding the material and listening, as opposed to the written material on the board.  Some of the questions may be slight variations on each other.
+The questions below are meant to highlight key aspects or principles related to the reading.  Being able to answer them prior to the lectures of that week should provide you with some nominal uderstanding of what will be covered so that you may focus on understanding the material and listening, as opposed to the written material on the board.  Some of the questions may be slight variations on each other. Many involve answering multiple questions, so maybe we should think of each question more as a //question set//, where sometime the set consists of an individual element.
-==== Topic 1: Image Formation ====
+===== Topic 1: Image Formation =====
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 equations? (related to Question 4.3 part 3)
-==== Topic 2: Camera Geometry ====
+===== Topic 2: Camera Geometry =====
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-==== Topic 3: Camera Calibration ====
+===== Topic 3: Camera Calibration =====
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 The recommended reading for the document is just up to Sections 2 and 3. Feel free to skim the remainder of the document since it has an interesting application (3D reconstruction of a face from two structured light views). There is a 3D scanner in the Inventure studio that uses a similar strategy (e.g., structured light) to generate 3D models of small objects.  The early version of the Kinect depth sensor and several other depth sensors created since utilize this form of 3D measurement.
+**Question 3:** Read up on QR decomposition (also called QR factorization).  You can use
+[[https://en.wikipedia.org/wiki/QR_decomposition|wikipedia]] or the Appendix of [[http://szeliski.org/Book/|Szeleski]].
+  - Given a real-valued matrix A, explain what the QR decomposition of the matrix A is.
+  - How does the decomposition relate to the camera projection matrix M?
+  - What utility would knowing about the QR decomposition have?
+In my notes, I use the symbol M, however Szeleski (Sec 2.1.5) and the DLT reading use the symbol P to denote the camera (projection) matrix.
-==== Topic 4: Stereo and Multiview Geometry ====
+===== Topic 4: Stereo and Multiview Geometry =====
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-==== Topic 5: Images as Functions ====
+===== Topic 5: Images as Functions =====
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-==== Topic 6: Optimization in Computer Vision ====
+**Question 1:** What is a convolution kernel? What relationship is there with Fourier analysis? What purpose would a convolution kernel serve in image processing?
+===== Topic 6: Optimization in Computer Vision =====
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+**Question 1:** The simplest and most brute-force method for performing classification is known as nearest neighbor search.  Given a collection of data with labels, how does nearest neighbor determine the labels of a new set of data? \\
+There is an extension called k-nn (of k nearest neighbors).  How does it differ from nearest neighbor?
+**Question 2:** What is a Voronoi diagram (or Voronoi partition)?  How does it relate to Lloyd's algorithm?  What is Lloyd's algorithm?
+===== Topic 7: Bayesian Statistics in Computer Vision =====
+------------
+**Question 1:** What is Bayes' Rule? Why is it important to be aware of Bayes' rule when making decisions based on binary tests?
+===== Topic 8: Optical Flow =====
+------------
+**Question 1:** What is the optical flow constraint? Why is it ill-posed (e.g., degenerate)?
+**Question 2:** What favorite trick do we apply to the optical flow constraint to obtain a well-posed optimization problem for dense optical flow?
+**Question 3:** //Optional:// Sparse optical flow doesn't have this problem.  Why not? (We covered it earlier in class)