Computer Vision (CV) Exercises

Computer Vision Course Exercises at Amirkabir University of Technology - Fall 2022

By Gholamreza Dar

Content

• Table of Contents
  • Exercise 1
  • Exercise 2
  • Exercise 3
  • Exercise 4
  • Exercise 5
  • Exercise 6
  • Exercise 7
  • Exercise 8

Exercise 1

• Fundamental image manipulations
• Extract colors using HSV colorspace and inRange() function
• Align and Combine misaligned channels

- Canny edge detection practice - Histogram Equalization using RGB vs YUV color spaces

Exercise 2

• Road Lane Detection
  • Detect road lanes using Canny, Hough, Clustering lines by angle, and Linear regression

• Road Lane Detection on video
  • Added smoothing features to maintain the lanes over time (Some frames were very blurry or foggy, but we could assume that the car doesn't suddenly change directions).

video

video

• Crack Segmentation using Active Contours

Exercise 3

• Implement Otsu Thresholding
• Implement Iterative Thresholding
• Match Template Exercise
  • Detect the template in various orientations by rotating the template each time.

Exercise 4

• Colored-Image Segmentation by Clustering

• Image Segmentation using GLCM

• Image Segmentation using Filter Banks

Exercise 5

• Feature Matching using SIFT
  • Recover a rotated Image using feature matching

• Compare SIFT and FREAK

Exercise 6

• Object Detection on the Counter-Strike dataset (R-CNN method)
  • Classes are: W(eapon), T(errorist), C(ounter terrorist), D(ead), bg
  • using mean (average) of a patch as the feature and KNN as classifier

• using LBP (Local Binary Pattern) of a patch as the feature and KNN as classifier

• using Neural Network (EfficientNetb0) as the feature extractor and classifier

• Some of the results (Can definitely be improved but due to the end-of-semester time limits didn't try to improve it).

Exercise 7

• 3D Vision
  • Plotting LiDAR data on the image using the camera parameters

* Using LiDAR data to estimate the depth map

* Using Linear Interpolation to overcome the sparsity of LiDAR data

* Using stereo matching to estimate the depth map

* 3D reconstruction of the scene using the estimated depth map

Exercise 8

• Detect Corners using Shi-Tomas Algorithm

• Optical Flow using the Lukas-Kanade Algorithm

• Dense Optical Flow using the Gunnar-Farneback Algorithm