From 54dfa345e2dd3939dffea5666656730717f2cb2c Mon Sep 17 00:00:00 2001 From: Andrew Date: Fri, 29 Nov 2024 14:16:46 +0000 Subject: [PATCH] [CT404]: Add exam notes --- .../exam.md | 64 +++++++++++++++++++ 1 file changed, 64 insertions(+) create mode 100644 year4/semester1/CT404: Graphics & Image Processing/exam.md diff --git a/year4/semester1/CT404: Graphics & Image Processing/exam.md b/year4/semester1/CT404: Graphics & Image Processing/exam.md new file mode 100644 index 00000000..ff9d4385 --- /dev/null +++ b/year4/semester1/CT404: Graphics & Image Processing/exam.md @@ -0,0 +1,64 @@ +## Pre-Revision Hints +- Write the pseudocode or steps to find the HOG feature. +- What happens if you change the HOG feature from 2x2 to 4x4. + +## Exam Info +- Don't be misled by previous years. +- Previously 2/3, now 4/5. +- More questions, same time, less time per question. +- Q1 is compulsory. +- More options as there is more to cover. +- Do one of two coding tasks. +- More graphics on 2021/2022 paper than our one. + +## Exam hints +- Probably won't be asked for algorithms, but could be asked for general steps. +- What are steps when detecting faces, steps to follow to compute HOG of descriptor, steps for camera calibration. +- Very small computations, like how to compute the gradient for a particular pixel in x direction, y direction, etc. +- One could be canvas 2d, one could be threejs, no guarantee that there won't be two on the same technology. + - Can't get away with just studying one technology. +- Theory questions and application questions. + - Doing analysis on an image. +- Won't be too theoretical, going into mathematics, will be more applied. +- One is around camera calibration and the other around feature and descriptors. + - We did basics like calculating differentials. + - These will be the ideas around Q4 and Q5. + - Won't be calculating eigenvalues. + - Basic mathematics. + - Compute the total amount of features you have in HOG. +- Don't focus on solving mathematics for camera calibration, just need to understand how you create an intrinsic matrix given focal length etc. + - Construct K-matrix. + - Anatomy of the camera matrix. + - What the rows of the P-matrix means. +- Feature side: + - Focus on steps need to follow, why you need to. + - Benefit of principal components. +- Just have to do one image analysis question. +- Try not to just pick one, the other one could be difficult. +- Exam won't be easy. +- Make sure you understand concepts covered in class. +- Waqar's part is more novel, so he has given us extra info about exam. +- Nasre's stuff is more overlapping from previous years. +- Will be given useful methods to refer to, like in Sam's papers. + - Answer can be derived from methods given. + - For both Canvas and ThreeJS. +- Definition of surface normal for example, can give formula or describe. +- Nasre tends to be more applied: might ask more applied questions that theoretical questions. + - Analyse this image, write this code. +- Perfect answer for erosion: no equation, description, example. +- We didn't do image de-calibration. +- Hough transform? + - Or adaptive thresholding? +- Think in transforms, draw transforms. + - Nasre really likes transforms. +- It's good to look at previous exam papers. + - Possibly taken question from previous years??? + - Was answering question. +- Look at last 3/4 exam papers. +- Wouldn't pass if you only studied last year's paper. +- Try to remember kernel matrices for filters, e.g., smoothing etc. + - Nasre wants us to know the matrix values. + - Can learn how to develop kernel from first principles. + - Here is a one directional filter, make a two directional one. + - like how you make laplacian of gaussian. + - How to convolve filters.