Summary  
This chapter introduces fundamental computer vision techniques for processing and interpreting visual data, including image classification, object detection, segmentation, recognition, edge detection, restoration, scene reconstruction, and video motion analysis.

General domain of usage  
Autonomous vehicles

**Computer vision** is a field of **artificial intelligence** that enables machines to interpret and understand **visual data** from the world, similar to how humans perceive and process visual information. It involves acquiring, processing, and analyzing images or videos to make decisions or gain insights.

Definition

**Real-world** applications of computer vision include:

**Computer vision** encompasses a variety of tasks that enable machines to understand and process **visual data** effectively:
- Image classification;
- Object detection;
- Image segmentation;
- Face & person recognition;
- Edge detection;
- Image restoration;
- Matching scene reconstruction;
- Video motion analysis.

Which statement best defines computer vision?

Dive deep into computer vision by mastering convolutional neural networks (CNNs) with TensorFlow and Keras. This advanced course covers essential concepts and practical skills, including image preprocessing, convolution and pooling operations, building and training CNN architectures, implementing object detection, and applying transfer learning to tackle real-world visual challenges. Gain hands-on experience in solving complex vision problems using industry-standard tools and techniques.

What Is Computer Vision?