Topic

Lab

Course Material

PART I - Image Processing

Week 1

• Introduction to computer vision and its applications.

• Image representation, sampling and quantization, light spectrum, visual perception, Color

Week 2

• Pixel-wise operations,  brightness, contrast, Histograms,

• Histogram equalization, Color Histograms

Lab1: Introduction to numpy, scipy, and matplotlib, Reading and displaying images with scipy and matplotlib

Week 3

• Noise, Gaussian Noise, Linear filtering, convolution, blurring,

• 2D Fourier transform, DFT, FFT

Lab2: Introduction to OpenCV, reading, writing and displaying images. image blending

Lab 2 - Instructions
Lab 2 - Instructions+Files

Week 4

• Normalized correlation, template matching,

• Other types of noise, median filtering, Bilateral filtering

Lab3: Working with Videos, histograms

Lab 3 - Instructions
Lab 3 - Instructions+Files

Week 5

• Image, Gradients, Edge Detection, 2D edge operators,

• Laplacian of Gaussian, Canny Edge detector.

Lab4: Noise, blurring, filtering, Gaussian filtering,

Lab 4 - Instructions
Lab 4 - Instructions+Files

Week 6

• Image Thresholding, Binary Images, Connected Components, Morphology

Lab5: Reading from camera devices, edge detection

Lab 5 - Instructions
Lab 5 - Instructions+Files

PART II - Computer Vision

Week 7

• Hough Transforms, Line Hough transform, Circle Hough Transform

Lab6: Binary Images, Connected Components, Thresholding, Morphology

Lab 6 - Instructions
Lab 6 - Instructions+Files

Week 8

• Introduction to features and feature matching, Corner Detection, Harris corner detector, Multiscale corner detection

Lab7: Hough Transforms

Lab 7 - Instructions
Lab 7 - Instructions+Files

Week 9

• Image Pyramid, Scale invariance, Scale-space analysis,

• Introduction to SIFT, SIFT detection

Lab8: Corner Detection

Lab 8 - Instructions
Lab 8 - Instructions+Files

Week 10

• SIFT description

• SIFT matching, KD-trees

• Other types of features (SURF, ORB, etc.)

Lab9: Image Pyramid, Multiscale Corner detection

Lab 9 - Instructions
Lab 9 - Instructions+Files

Week 11

• Geometric Image Transformation, Homography & Perspective,

• Image Registration and alignment

• Robust alignment, RANSAC

Lab10: SIFT detection, description and matching

Lab 10 - Instructions
Lab 10 - Instructions+Files

Week 12

• Introduction to Video analysis, background subtraction.

• Introduction to image recognition, Bayesian classification

Lab11: Geometric Image Transformations, Perspective Correction

Lab 11 - Instructions
Lab 11 - Instructions+Files

Week 13

• More of Bayesian Classification, Feature extraction, Nearest neighbour, kNN

• Support Vector Machines

Lab12: Feature-based Image Alignment, RANSAC, feature-based object detection

Lab 12 - Instructions
Lab 12 - Instructions+Files

Week 14

• HoG features, Local Binary Patterns

• Object Detection, Sliding window

Lab13: Image Classification

Lab 13 - Instructions
Lab 13 - Instructions+Files

Week 15

• Haar Cascades & face detection, Integral Images

• CNNs
  

Lab14: Object Detection

Lab 14 - Instructions
Lab 14 - Instructions+Files