AI Lesson 81 – Introduction to Computer Vision | 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 81: Introduction to Computer Vision

Computer Vision is the field of Artificial Intelligence that enables machines to see, interpret, and understand visual information such as images and videos.

Humans understand visuals naturally. Computer vision teaches machines to do the same using mathematics, statistics, and deep learning.

Real-World Connection

Face unlock on smartphones, self-driving cars detecting pedestrians, medical scans identifying diseases, and social media apps tagging people in photos all rely on computer vision.

Whenever a system extracts meaning from images or video frames, computer vision is involved.

What Is Computer Vision?

Computer vision is the process of acquiring, processing, analyzing, and understanding images to extract useful information.

  • Recognizing objects
  • Detecting faces
  • Understanding scenes
  • Tracking motion in videos

How Machines See Images

To a computer, an image is not a picture. It is a grid of numbers representing pixel intensity values.

Each pixel contains information about color and brightness. Computer vision models learn patterns from these numbers.

Basic Image Representation Example


import cv2

image = cv2.imread("image.jpg")
print(image.shape)
(480, 640, 3)

Understanding the Output

The output represents image height, width, and color channels. The value 3 indicates RGB color channels.

Every computer vision model ultimately works on this numeric representation.

Key Tasks in Computer Vision

  • Image Classification: Identifying what is in an image
  • Object Detection: Locating objects within an image
  • Image Segmentation: Understanding pixel-level regions
  • Video Analysis: Understanding motion and events

Why Deep Learning Is Important for Vision

Traditional image processing relied on handcrafted rules. Modern systems use deep learning models, especially Convolutional Neural Networks (CNNs), to automatically learn visual patterns.

These models outperform traditional methods in accuracy and scalability.

Where Computer Vision Is Used

  • Autonomous vehicles
  • Medical imaging
  • Security and surveillance
  • Retail analytics
  • Augmented reality

Practice Questions

Practice 1: What type of data does computer vision work with?



Practice 2: How does a computer represent an image internally?



Practice 3: Which neural network is commonly used in computer vision?



Quick Quiz

Quiz 1: Which AI field focuses on understanding images?





Quiz 2: What are images made of for computers?





Quiz 3: Which task finds objects inside an image?





Coming up next: Image Processing Basics — how images are enhanced, filtered, and transformed.

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