Fundamentals of Computer Vision - Spring 2021
Course Logistics
- Live Remote Lectures:
Saturday 1:30-3:30 PM / 5:30-7:30 PM
Lecture Videos:
Will be available shortly after each lecture on VC Portal
Office:
Room 402
Contact:
nasihatkon@kntu.ac.ir
Teaching
| Week | Topic | Lab | Course Material | |
|---|---|---|---|---|
Part 1 - Image Processing |
||||
| 1 | Introduction to computer vision and its applications. Image representation, sampling and quantization, light spectrum, visual perception, Color |
Lab 0 : Introduction to Python Lab 0 - Instructions |
Week 1 - Course Files | |
| 2 | Pixel-wise operations, brightness, contrast, Histograms Histogram equalization, Color Histograms |
Lab 1 : Introduction to numpy, scipy and matplotlib; Reading and displaying images with scipy and
matplotlib Lab 1 - Instructions+Files |
||
| 3 | Noise, Gaussian Noise, Linear filtering, convolution, blurring 2D Fourier transform, DFT, FFT |
Lab 2 : Introduction to OpenCV; Reading, writing and displaying images; Image blending Lab 2 - Instructions+Files Lab 3 : Working with Videos; Histograms Lab 3 - Instructions+Files |
||
| 4 | Normalized correlation, template matching Other types of noise, median filtering, Bilateral filtering |
Lab 4 : Noise, blurring, filtering, Gaussian filtering Lab 4 - Instructions+Files |
||
| 5 | Image, Gradients, Edge Detection, 2D edge operators Laplacian of Gaussian, Canny Edge detector |
Lab 5 : Reading from camera devices, edge detection Lab 5 - Instructions+Files |
||
Part 2 - Computer Vision |
||||
| 6 | Image Thresholding, Binary Images, Connected Components, Morphology | Lab 6 : Binary Images, Connected Components, Thresholding, Morphology Lab 6 - Instructions+Files |
||
| 7 | Hough Transforms, Line Hough transform, Circle Hough Transform | Lab 7 : Hough Transforms Lab 7 - Instructions+Files |
Midterm Exam 2021 | |
| 8 | Introduction to features and feature matching, Corner Detection, Harris corner detector, Multiscale corner detection | Lab 8 : Corner Detection Lab 8 - Instructions+Files |
||
| 9 | Image Pyramid, Scale invariance, Scale-space analysis Introduction to SIFT, SIFT detection |
Lab 9: Image Pyramid, Multiscale Corner detection Lab 9 - Instructions+Files |
||
| 10 | SIFT description SIFT matching, KD-trees Other types of features (SURF, ORB, etc.) |
Lab 10: SIFT detection, description and matching Lab 10 - Instructions+Files |
||
| 11 | Geometric Image Transformation, Homography & Perspective Image Registration and alignment Robust alignment, RANSAC |
Lab 11: Geometric Image Transformations, Perspective Correction Lab 11 - Instructions+Files |
||
| 12 | Introduction to Video analysis, background subtraction Introduction to image recognition, Bayesian classification |
Lab 12: Feature-based Image Alignment, RANSAC, feature-based object detection Lab 12 - Instructions+Files |
||
| 13 | More of Bayesian Classification, Feature extraction, Nearest neighbour, KNN Support Vector Machines |
Lab 13: Image Classification Lab 13 - Instructions+Files |
Project Phase 1 | |
| 14 | Haar features, Integral Images HoG features, Local Binary Patterns Object Detection, Sliding window, Cascade Detection |
Lab 14: Object Detection Lab 14 - Instructions+Files |
Project Phase 2 Project Dataset |
|
| 15 | Introduction to Neural Networks Convolutional Neural Networks | Lab 15: Neural Nets, CNNs Lab 15 - Instructions+Files | Final Exam 2021 | |