Artificial Intelligence: Computer Vision

This tutorial provides a beginner-friendly introduction to Computer Vision (CV), a subfield of artificial intelligence (AI) that enables machines to interpret and process visual data, such as images and videos. CV is a rapidly evolving area with applications in autonomous vehicles, medical imaging, and augmented reality. This tutorial covers the basics of CV, key concepts, techniques, tools, a hands-on example, and resources for further learning, tailored to the context of AI research areas. No prior CV experience is assumed, but basic Python knowledge is helpful for the coding section.


What is Computer Vision?

Computer Vision involves teaching computers to "see" and understand visual data by extracting meaningful information from images or videos. It combines machine learning (ML), deep learning (DL), and image processing to perform tasks like object detection, image classification, and facial recognition.

Examples of CV Tasks:

  • Image Classification: Labeling an image (e.g., "cat" vs. "dog").
  • Object Detection: Identifying and localizing objects (e.g., detecting cars in a street image).
  • Image Segmentation: Dividing an image into regions (e.g., separating foreground from background).
  • Facial Recognition: Identifying faces in images or videos.
  • Image Generation: Creating new images (e.g., AI art via Stable Diffusion).


Key Concepts in Computer Vision

  1. Images as Data:
    • Images are represented as arrays of pixel values (e.g., 0–255 for grayscale, RGB for color).
    • Example: A 28x28 grayscale image is a 2D array; a 224x224 RGB image is a 3D array (224x224x3).
  2. Feature Extraction:
    • Identifying patterns like edges, textures, or shapes.
    • Traditional methods: SIFT, HOG.
    • Modern methods: Convolutional Neural Networks (CNNs) learn features automatically.
  3. Convolutional Neural Networks (CNNs):
    • DL models designed for image data, using layers like convolutions, pooling, and fully connected layers.
    • Example: ResNet, VGG for classification.
  4. Transformers:
    • Vision Transformers (ViTs) apply transformer architectures (from NLP) to images, treating patches as tokens.
    • Example: ViT, Swin Transformer for advanced tasks.
  5. Preprocessing:
    • Techniques like resizing, normalization, or augmentation (e.g., flipping, rotating) prepare images for models.
  6. Evaluation Metrics:
    • Accuracy: For classification.
    • Intersection over Union (IoU): For object detection and segmentation.
    • Mean Average Precision (mAP): For detection tasks.


Tools & Frameworks for Computer Vision

These open-source tools, widely used as of 2025, simplify CV development:

  1. OpenCV:
    • Best for: Image processing, real-time CV.
    • Strengths: Lightweight, extensive functions (e.g., edge detection, filtering).
  2. TensorFlow & PyTorch:
    • Best for: Building and training DL models (e.g., CNNs, ViTs).
    • Strengths: GPU support, scalable for research and production.
  3. Hugging Face Transformers:
    • Best for: Pre-trained vision models (e.g., ViT, CLIP).
    • Strengths: Easy fine-tuning, multimodal (text+image) support.
  4. YOLO (You Only Look Once):
    • Best for: Real-time object detection (e.g., YOLOv8).
    • Strengths: Fast, accurate, edge-friendly.
  5. Albumentations:
    • Best for: Image augmentation.
    • Strengths: Fast, customizable data augmentation.
  6. Matplotlib & PIL:
    • Best for: Visualizing and manipulating images.


Hands-On Tutorial: Image Classification with Python

Let’s build a simple image classification model to classify cats vs. dogs using a pre-trained CNN (ResNet50) in TensorFlow/Keras. This example uses a small dataset and runs on a standard laptop (GPU optional).

Step 1: Set Up Environment

Install required libraries:

pip install tensorflow opencv-python matplotlib numpy
 
 
Step 2: Prepare Dataset

For this tutorial, download a small subset of the Cats vs. Dogs dataset from Kaggle or use a public dataset like TensorFlow’s tf.keras.datasets. Alternatively, create folders train/cats, train/dogs, test/cats, and test/dogs with a few labeled images (e.g., 100 per class).

Step 3: Write the Code

Below is a Python script to load data, preprocess images, and train a model.

import tensorflow as tf from tensorflow.keras.applications import ResNet50 from tensorflow.keras.models import Model from tensorflow.keras.layers import Dense, GlobalAveragePooling2D from tensorflow.keras.preprocessing.image import ImageDataGenerator import matplotlib.pyplot as plt # Define paths (update with your dataset paths) train_dir = 'path/to/train' # Folder with 'cats' and 'dogs' subfolders test_dir = 'path/to/test' # Image parameters img_height, img_width = 224, 224 batch_size = 32 # Data augmentation and preprocessing train_datagen = ImageDataGenerator( rescale=1./255, # Normalize pixel values rotation_range=20, width_shift_range=0.2, height_shift_range=0.2, horizontal_flip=True ) test_datagen = ImageDataGenerator(rescale=1./255) # Load and preprocess images train_generator = train_datagen.flow_from_directory( train_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode='binary' # Cats (0) vs. Dogs (1) ) test_generator = test_datagen.flow_from_directory( test_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode='binary' ) # Load pre-trained ResNet50 base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(img_height, img_width, 3)) # Add custom layers x = base_model.output x = GlobalAveragePooling2D()(x) x = Dense(128, activation='relu')(x) predictions = Dense(1, activation='sigmoid')(x) # Binary classification model = Model(inputs=base_model.input, outputs=predictions) # Freeze pre-trained layers for layer in base_model.layers: layer.trainable = False # Compile model model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # Train model history = model.fit( train_generator, epochs=5, # Increase for better results validation_data=test_generator ) # Plot training results plt.plot(history.history['accuracy'], label='Training Accuracy') plt.plot(history.history['val_accuracy'], label='Validation Accuracy') plt.title('Model Accuracy') plt.xlabel('Epoch') plt.ylabel('Accuracy') plt.legend() plt.show() # Save model model.save('cats_vs_dogs_model.h5')
 
 
Step 4: Test the Model

Use the trained model to classify a new image:

from tensorflow.keras.preprocessing.image import load_img, img_to_array import numpy as np # Load and preprocess a test image img_path = 'path/to/test_image.jpg' # Path to a cat or dog image img = load_img(img_path, target_size=(img_height, img_width)) img_array = img_to_array(img) / 255.0 img_array = np.expand_dims(img_array, axis=0) # Add batch dimension # Predict prediction = model.predict(img_array) if prediction[0] > 0.5: print("Dog") else: print("Cat")
 
 
Step 5: Interpret Results
  • Training Output: The script prints training and validation accuracy per epoch. Expect 70–90% accuracy with a small dataset after 5 epochs.
  • Plot: Visualizes accuracy trends to check for overfitting (e.g., high training accuracy but low validation accuracy).
  • Prediction: The model labels a test image as "Cat" or "Dog" based on the sigmoid output.
Tips for Improvement:
  • More Data: Use a larger dataset (e.g., full Cats vs. Dogs from Kaggle).
  • Fine-Tuning: Unfreeze some ResNet layers and train with a lower learning rate.
  • Augmentation: Add more augmentation (e.g., zoom, shear) via ImageDataGenerator.
  • Hyperparameters: Adjust batch size, epochs, or optimizer (e.g., RMSprop).

Challenges in Computer Vision

  1. Data Bias:
    • Models trained on biased datasets (e.g., mostly light-skinned faces) perform poorly on diverse data.
    • Mitigation: Use diverse datasets, fairness tools (e.g., AI Fairness 360).
  2. Computational Costs:
    • Training CV models (e.g., ViTs) requires GPUs/TPUs, limiting access.
    • Mitigation: Model compression, cloud platforms (e.g., Google Colab).
  3. Robustness:
    • Models fail under adversarial attacks or poor conditions (e.g., low light).
    • Mitigation: Adversarial training, robust datasets.
  4. Interpretability:
    • CNNs and ViTs are hard to interpret, reducing trust.
    • Mitigation: Grad-CAM, attention maps for visualization.
  5. Real-Time Processing:
    • Applications like autonomous driving need fast inference.
    • Mitigation: Lightweight models (e.g., YOLOv8), edge AI.


Recent Trends in Computer Vision (2025)

  1. Vision Transformers (ViTs):
    • Outperform CNNs in tasks like classification and segmentation (e.g., Swin Transformer).
  2. Generative Vision:
    • Diffusion models (e.g., Stable Diffusion, DALL·E 3) lead in image generation.
  3. Multimodal Models:
    • CLIP and GPT-4o combine text and images for tasks like visual question answering.
  4. Real-Time CV:
    • YOLOv8 and EfficientDet enable fast object detection on edge devices.
  5. Ethical CV:
    • Focus on bias mitigation (e.g., fair facial recognition) and transparency.
  6. 3D Vision:
    • Advances in 3D reconstruction and NeRFs for AR/VR and robotics.


Applications of Computer Vision

  • Healthcare: Tumor detection, X-ray analysis.
  • Automotive: Autonomous driving, pedestrian detection.
  • Retail: Inventory tracking, cashierless stores (e.g., Amazon Go).
  • Security: Facial recognition, surveillance.
  • Entertainment: AR filters, AI-generated art.
  • Agriculture: Crop monitoring, pest detection.


Resources for Further Learning

  1. Courses:
    • Coursera: Deep Learning Specialization by Andrew Ng (includes CV).
    • Fast.ai: Practical Deep Learning for Coders (free, hands-on).
  2. Books:
    • Deep Learning by Goodfellow, Bengio, and Courville.
    • Computer Vision: Algorithms and Applications by Richard Szeliski.
  3. Tutorials & Documentation:
  4. Datasets:
    • ImageNet: Large-scale classification dataset.
    • COCO: For object detection and segmentation.
    • Cats vs. Dogs: Kaggle dataset for binary classification.
  5. Communities:
    • Kaggle for competitions and datasets.
    • Reddit (r/computervision), X posts for discussions (I can search for recent trends if needed).


Conclusion

Computer Vision is a transformative AI field, enabling machines to interpret visual data with applications from healthcare to autonomous driving. This tutorial introduced CV basics, demonstrated a hands-on image classification task using ResNet50, and highlighted tools, challenges, and trends. By experimenting with the code and exploring resources, you can build on this foundation to tackle more advanced CV projects.