AI Lesson 87 – Image Segmentation (U-Net) | Dataplexa

AI Course

I. AI Foundations
1. Introduction to AI 2. History of AI 3. AI vs ML vs DL 4. Search Algorithms 5. Intelligent Agents 6. Informed & Uninformed Search 7. Constraint Satisfaction 8. Adversarial Search 9. Logic in AI 10. Planning in AI 11. Knowledge Representation 12. Probabilistic AI 13. Bayesian Networks 14. Markov Models 15. AI Ethics 16. AI Applications 17. Intro to PyTorch 18. Intro to TensorFlow 19. AI Workflows 20. Data for AI
II. Machine Learning
21. Introduction to ML 22. ML Workflow 23. Supervised Learning 24. Unsupervised Learning 25. Reinforcement Learning 26. Linear Regression 27. Logistic Regression 28. SVM 29. Decision Trees 30. Random Forest 31. Gradient Boosting 32. XGBoost 33. K-Means 34. Hierarchical Clustering 35. Dimensionality Reduction 36. Feature Engineering 37. Feature Selection 38. Model Evaluation 39. Overfitting vs Underfitting 40. Cross Validation
III. Deep Learning
41. Intro to Deep Learning 42. Neural Networks 43. Activation Functions 44. Backpropagation 45. Loss & Optimizers 46. Regularization 47. CNN Basics 48. CNN Architectures 49. RNN, LSTM, GRU 50. Seq2Seq 51. Transformers 52. Attention 53. Positional Encoding 54. Training DL 55. Hyperparameter Tuning 56. Transfer Learning 57. Autoencoders 58. GAN Basics 59. GAN Applications 60. Model Serving
IV. Natural Language Processing
61. Intro to NLP 62. Text Processing 63. Tokenization 64. Stopwords & Lemmatization 65. BoW & TF-IDF 66. Word Embeddings 67. Sentence Embeddings 68. NLP Classification 69. Sequence Modeling 70. RNN in NLP 71. Transformers in NLP 72. BERT Basics 73. BERT Fine-tuning 74. RoBERTa & DistilBERT 75. Sentiment Analysis 76. NER 77. Machine Translation 78. Text Generation 79. NLP Evaluation 80. NLP Use Cases
V. Computer Vision
81. Intro to CV 82. Image Processing 83. Filters & Edges 84. Convolutions 85. Object Detection 86. Object Tracking 87. Image Segmentation 88. Face Recognition 89. Feature Detection 90. OCR 91. Image Augmentation 92. Generative Vision 93. Vision Transformers 94. Multimodal Models 95. CV Use Cases
VI. LLM Engineering
96. Intro to LLMs 97. Tokenization 98. LLM Architecture 99. Pretraining 100. Fine-tuning 101. RLHF 102. Prompt Engineering 103. Embedding Models 104. Vector Databases 105. LLM Agents 106. Guardrails & Safety 107. AI in Production 108. End-to-End Project

Lesson 87: Image Segmentation

Image segmentation is a computer vision technique that divides an image into meaningful regions. Instead of detecting objects with boxes, segmentation works at the pixel level and tells exactly which pixels belong to which object or region.

This makes segmentation more precise than object detection and extremely useful for tasks that require fine-grained understanding of images.

Real-World Connection

Image segmentation is widely used in medical imaging to identify tumors, in satellite imagery to analyze land use, in autonomous driving to separate roads, vehicles, and pedestrians, and in photo editing apps to remove backgrounds accurately.

Any application that needs pixel-level accuracy depends on segmentation.

What Is Image Segmentation?

Image segmentation assigns a label to every pixel in an image so that pixels with the same label belong to the same object or region.

  • Works at pixel level
  • Produces masks instead of boxes
  • Provides high precision

Types of Image Segmentation

There are three main types of segmentation:

  • Semantic Segmentation: Labels each pixel by class (road, car, person)
  • Instance Segmentation: Separates individual objects of the same class
  • Panoptic Segmentation: Combines semantic and instance segmentation

How Image Segmentation Works

Segmentation models use convolutional neural networks to extract features and then predict class labels for each pixel.

  • Image passes through convolution layers
  • Features are learned at different scales
  • Final output is a segmentation mask

Simple Image Segmentation Example

Below is a simple example using OpenCV to perform basic threshold-based segmentation. 


import cv2

image = cv2.imread("image.jpg", cv2.IMREAD_GRAYSCALE)

_, segmented = cv2.threshold(
    image, 127, 255, cv2.THRESH_BINARY
)

cv2.imshow("Original", image)
cv2.imshow("Segmented", segmented)
cv2.waitKey(0)
cv2.destroyAllWindows()

What This Code Is Doing

The image is converted to grayscale and then segmented using a threshold. Pixels above the threshold become white, and pixels below become black.

Although simple, this demonstrates the idea of separating regions based on pixel values.

Understanding the Output

The output image contains two regions: foreground and background. This is a basic form of segmentation.

Advanced AI models produce multi-class segmentation masks instead of binary results.

Popular Segmentation Models

  • U-Net: Common in medical imaging
  • Mask R-CNN: Instance segmentation
  • DeepLab: Semantic segmentation

Why Image Segmentation Matters in AI

Segmentation gives machines a detailed understanding of visual scenes. It allows precise measurements, accurate object boundaries, and better decision-making.

Many advanced vision systems rely on segmentation for safety-critical tasks.

Practice Questions

Practice 1: Image segmentation works at which level?



Practice 2: What type of segmentation labels each pixel by class?



Practice 3: What is the output of a segmentation model called?



Quick Quiz

Quiz 1: Which task labels each pixel in an image?





Quiz 2: Which model is popular in medical image segmentation?





Quiz 3: Segmentation provides accuracy at which level?





Coming up next: Face Recognition — identifying and verifying human faces using AI.

← Previous Course Index Next →