مبانی بینایی کامپیوتر (کارشناسی)

بهار ۹۸

Introduction to computer vision (Undergraduate Course)
Spring 2019

Instructor:

Teaching Assistants:

References:

No specific reference is needed. The following are recommended:

Tools and Platforms:


Schedule, and Lab instructions




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

Lab 0 (informal): introudction to python



Install OpenCV 3.4 + opencv_contrib + Python/numpy on Windows


Install OpenCV 3.4 + opencv_contrib + Python/numpy on Ubuntu

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


Lab 1 - Instructions
Lab 1 - Instructions+Files

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

Midterm Exam

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


Final Project: Face Detection and Facial Expression Classification using Cascade Detectors and Convolutional Neural Networks

Week 15

  • Haar Cascades & face detection, Integral Images

  • CNNs

Lab14: Object Detection


Lab 14 - Instructions
Lab 14 - Instructions+Files

Final Exam