Computer Vision Fundamentals - Lesson 4: Feature Detection and Description

Overview:

This lesson focuses on detecting and describing features within images, which is crucial for various computer vision tasks such as object recognition, image stitching, and motion tracking. Students will learn about key feature detection algorithms and how to use them effectively.

Objectives:

By the end of this lesson, students should be able to:

• Understand the importance of feature detection and description in computer vision.
• Implement key feature detection algorithms and extract feature descriptors.
• Match features between images for tasks such as object recognition and image stitching.

Topics Covered:

1. Introduction to Feature Detection:

   • Definition and significance of features in images.
   • Types of features: Corners, edges, and blobs.

2. Key Feature Detection Algorithms:

   • Harris Corner Detector:

     • Theory and application.
     • Code implementation and example.

     Code Example:

     import cv2
     import numpy as np
     
     # Load an image in grayscale
     image = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)
     
     # Apply Harris Corner Detection
     dst = cv2.cornerHarris(image, 2, 3, 0.04)
     dst = cv2.dilate(dst, None)
     
     # Threshold to mark the corners
     image[dst > 0.01 * dst.max()] = [0, 0, 255]
     
     # Display results
     cv2.imshow('Harris Corners', image)
     cv2.waitKey(0)
     cv2.destroyAllWindows()
     

   • Shi-Tomasi Corner Detector:

     • Comparison with Harris Corner Detector.
     • Code implementation and example.

     Code Example:

     # Load an image in grayscale
     image = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)
     corners = cv2.goodFeaturesToTrack(image, 100, 0.01, 10)
     corners = np.int0(corners)
     
     # Draw corners on the image
     for corner in corners:
         x, y = corner.ravel()
         cv2.circle(image, (x, y), 3, 255, -1)
     
     # Display results
     cv2.imshow('Shi-Tomasi Corners', image)
     cv2.waitKey(0)
     cv2.destroyAllWindows()
     

   • SIFT (Scale-Invariant Feature Transform):

     • Theory behind SIFT and its advantages.
     • Code implementation and example.

     Code Example:

     # Load an image
     image = cv2.imread('image.jpg')
     gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
     
     # Initialize SIFT detector
     sift = cv2.SIFT_create()
     keypoints, descriptors = sift.detectAndCompute(gray, None)
     
     # Draw keypoints on the image
     image_with_keypoints = cv2.drawKeypoints(image, keypoints, None)
     
     # Display results
     cv2.imshow('SIFT Keypoints', image_with_keypoints)
     cv2.waitKey(0)
     cv2.destroyAllWindows()
     

   • ORB (Oriented FAST and Rotated BRIEF):

     • Understanding ORB and its efficiency.
     • Code implementation and example.

     Code Example:

     # Load an image
     image = cv2.imread('image.jpg')
     gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
     
     # Initialize ORB detector
     orb = cv2.ORB_create()
     keypoints, descriptors = orb.detectAndCompute(gray, None)
     
     # Draw keypoints on the image
     image_with_keypoints = cv2.drawKeypoints(image, keypoints, None)
     
     # Display results
     cv2.imshow('ORB Keypoints', image_with_keypoints)
     cv2.waitKey(0)
     cv2.destroyAllWindows()
     

3. Feature Matching:

   • Introduction to feature matching.
   • Using brute-force matcher and FLANN-based matcher.
   • Example of feature matching between two images.

   Code Example:

   # Load two images
   img1 = cv2.imread('image1.jpg', cv2.IMREAD_GRAYSCALE)
   img2 = cv2.imread('image2.jpg', cv2.IMREAD_GRAYSCALE)
   
   # Initialize ORB detector
   orb = cv2.ORB_create()
   kp1, des1 = orb.detectAndCompute(img1, None)
   kp2, des2 = orb.detectAndCompute(img2, None)
   
   # Use BFMatcher to find matches
   bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
   matches = bf.match(des1, des2)
   
   # Draw matches on the image
   img_matches = cv2.drawMatches(img1, kp1, img2, kp2, matches, None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
   
   # Display results
   cv2.imshow('Feature Matches', img_matches)
   cv2.waitKey(0)
   cv2.destroyAllWindows()
   

4. Applications of Feature Detection:

   • Object recognition and tracking.
   • Image stitching and panorama creation.
   • Augmented reality.

5. Challenges in Feature Detection:

   • Dealing with scale variations, rotations, and occlusions.

Activities and Quizzes:

• Activity: Implement feature detection and matching between two images using ORB or SIFT. Visualize and interpret the matches.
• Quiz: Multiple-choice questions on feature detection algorithms and their applications.

Assignments:

• Assignment 4: Create a project where you use feature detection and matching to recognize and track objects in a video. Submit the code along with a report describing the methods used and the performance of the system.

This lesson equips students with essential skills for detecting and describing features in images, which are crucial for many advanced computer vision applications.