How Computer Vision Works in AI: Unlocking the Power of Machines to See and Understand

📘 Chapter 2: Core Algorithms and Feature Extraction

Topic: How Computer Vision Works in AI

🧠 Overview

Once images are acquired and preprocessed, the next phase in the computer vision pipeline is feature extraction — the heart of how machines learn to interpret and analyze visual data. This chapter dives into the core algorithms used in computer vision, both traditional methods and deep learning-based techniques, explaining how features are detected, extracted, and used to represent images for classification, detection, or recognition.

📌 1. What is Feature Extraction?

Feature extraction is the process of identifying distinct, informative elements in an image, such as:

• Edges
• Textures
• Corners
• Blobs
• Keypoints

These features help an algorithm understand patterns, differentiate between objects, and ultimately make sense of visual input.

Think of it as reducing the image’s complexity by pulling out only the essential data that matters.

📌 2. Traditional Computer Vision Algorithms

Before the rise of deep learning, feature extraction was dominated by handcrafted techniques. These approaches use mathematical filters and image operations to identify specific features.

🔹 2.1 Edge Detection

Edge detection identifies boundaries within images. Common operators include:

Code: Canny Edge Detection (OpenCV)

python

import cv2

import matplotlib.pyplot as plt

img = cv2.imread("sample.jpg", cv2.IMREAD_GRAYSCALE)

edges = cv2.Canny(img, 100, 200)

plt.imshow(edges, cmap='gray')

plt.title('Canny Edges')

plt.axis('off')

plt.show()

🔹 2.2 Corner Detection (Harris)

Corners are points where two edges meet — very useful for motion tracking and matching.

Code: Harris Corner Detection

python

gray = np.float32(img)

dst = cv2.cornerHarris(gray, 2, 3, 0.04)

img[dst > 0.01 * dst.max()] = [255]

plt.imshow(img)

plt.title("Harris Corners")

plt.axis('off')

plt.show()

🔹 2.3 Blob Detection

Blobs are regions in an image that differ in properties (brightness, color).

🔹 2.4 Feature Descriptors

These algorithms identify and describe keypoints in images:

• SIFT (Scale-Invariant Feature Transform)
• SURF (Speeded-Up Robust Features)
• ORB (Oriented FAST and Rotated BRIEF)

Code: ORB Feature Detection

python

orb = cv2.ORB_create()

keypoints, descriptors = orb.detectAndCompute(img, None)

img_kp = cv2.drawKeypoints(img, keypoints, None, color=(0, 255, 0))

plt.imshow(img_kp)

plt.title("ORB Keypoints")

plt.axis('off')

plt.show()

📌 3. Deep Learning for Feature Extraction

While traditional methods were manually designed, deep learning models now automatically learn features from images.

🔹 3.1 CNNs (Convolutional Neural Networks)

CNNs extract spatial hierarchies from visual data using convolutional layers that scan over input images to detect:

• Edges (early layers)
• Shapes and patterns (middle layers)
• Full object features (later layers)

CNN Architecture:

Code: Simple CNN Feature Extractor (TensorFlow)

python

import tensorflow as tf

from tensorflow.keras import layers, models

model = models.Sequential([

    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)),

    layers.MaxPooling2D((2, 2)),

    layers.Conv2D(64, (3, 3), activation='relu'),

    layers.MaxPooling2D((2, 2)),

    layers.Flatten(),

    layers.Dense(64, activation='relu'),

    layers.Dense(10, activation='softmax')

])

model.summary()

🔹 3.2 Feature Maps and Filters

Each convolutional layer outputs a feature map, highlighting patterns like lines, textures, or corners. As we go deeper:

• Low-level features → High-level semantics
• More filters → More visual complexity

🔹 3.3 Transfer Learning for Feature Extraction

Use pre-trained CNNs like VGG16, ResNet, or MobileNet as feature extractors:

python

from tensorflow.keras.applications import VGG16

model = VGG16(include_top=False, weights='imagenet', input_shape=(224,224,3))

# Freeze layers and extract features

features = model.predict(processed_image)

📊 Comparison: Traditional vs Deep Learning-Based Feature Extraction

🔍 Feature Matching & Tracking (Bonus)

Feature extraction is often followed by matching (e.g., for panorama stitching, motion detection).

Code: Feature Matching using ORB and BFMatcher

python

bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)

matches = bf.match(des1, des2)

matches = sorted(matches, key=lambda x: x.distance)

matched_img = cv2.drawMatches(img1, kp1, img2, kp2, matches[:10], None, flags=2)

plt.imshow(matched_img)

plt.title("Feature Matching")

plt.axis('off')

plt.show()

🎯 Applications of Feature Extraction

🧠 Conclusion

Feature extraction is the core translator between raw pixels and intelligent decisions in computer vision. Whether you use handcrafted descriptors or powerful CNNs, the goal is the same: extract the most meaningful information from images and pass it to a model that can make sense of it.

Traditional methods are lightweight and interpretable, while deep learning-based methods are powerful, flexible, and scale well to complex tasks.

Understanding both helps you build smarter, more adaptable vision systems — whether you're classifying animals, guiding a robot, or building the next face ID app.