Introduction to Computer Vision | Lecture 1 | CV from scratch series

Описание: Miro notes: https://miro.com/app/board/uXjVIXe5cI...

Computer Vision: From Rule-Based Systems to Deep Learning

Imagine looking at an apple and instantly recognizing it. Teaching a computer to do the same—say, to identify a cat—has been the long-standing goal of computer vision. Over the past decade, the field has transitioned from painstaking manual filters to powerful machine learning models that learn patterns independently.

Early Days: Rule-Based Systems
Before 2010, computer vision relied heavily on handcrafted logic. Engineers manually designed filters—small matrices that detect edges or shapes by scanning across an image. Complex rule-based heuristics were also common. Though effective for narrow tasks, these methods struggled with real-world complexity.

Shift to Machine Learning
As data and computing power grew, so did machine learning (ML). Instead of coding every condition—like cat ears or whiskers—engineers began feeding models large datasets of labeled examples. The models learned to identify objects without explicitly being told how. This approach extended to tasks like Optical Character Recognition (OCR), where traditional heuristics (detecting loops or descenders) gave way to models trained on varied handwriting and fonts.

Rise of Deep Learning
The real transformation arrived with deep learning, a subset of ML that uses multi-layered neural networks to learn abstract features from raw data. While more powerful, these models also require large amounts of labeled data and significant computational power (typically GPUs).

AlexNet: A Turning Point (2012)
A landmark moment was the introduction of AlexNet, a deep convolutional neural network created by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton. When AlexNet won the 2012 ImageNet challenge by a wide margin, it proved deep learning’s superiority over traditional methods. Crucially, it also showcased GPU-based training for faster processing and introduced innovations like ReLU activations, dropout regularization, and data augmentation.

ML vs. DL
Machine Learning is a broad area focusing on algorithms that learn from data, often requiring handcrafted features.
Deep Learning is a specialized subset that uses many-layered (deep) neural networks to automatically learn features. It usually excels when large datasets and high computational power are available.
Why “Deep”?
A shallow neural network has only a few hidden layers, while a deep network can contain dozens or even hundreds. Each layer detects increasingly complex patterns, allowing deep models to handle challenging tasks, from object detection to language translation.

Conclusion and Further Resources
Today, deep learning drives breakthroughs in self-driving cars, medical imaging, robotics, and beyond. If you’d like to learn more, check out my lecture on Vizuara’s YouTube channel, where I cover the evolution from rule-based vision to cutting-edge deep learning in more detail.

By embracing data-driven models instead of manually engineering features, computer vision has become far more adaptable and accurate—an evolution that continues to reshape the AI landscape.