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Fundamentals of Computer Vision - Spring 2021

People



...

Dr. Behrooz Nasihatkon

Instructor

...

Mohammad Amin Parchami

Head TA

...

Parsa Mazaheri

Teaching Assistant

...

Behnam Esmaeilbeigi

Teaching Assistant

...

Behzad Mckizade

Teaching Assistant

...

Shakib Karami

Teaching Assistant

...

Kasra Eskandari

Teaching Assistant


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