Computer vision is a field of artificial intelligence (AI) that focuses on enabling computers to interpret and understand visual information from the world. It is a multidisciplinary area involving machine learning, deep learning, image processing, and other techniques to extract meaningful insights from visual data, such as images and videos.

Here are the key approaches in computer vision:

1. Traditional Computer Vision Methods (Pre-Deep Learning)

These approaches were more rule-based and relied on hand-crafted features and algorithms.

2. Machine Learning-Based Approaches

Before deep learning became widespread, machine learning algorithms were used for image classification, segmentation, and object recognition.

3. Deep Learning Approaches

Deep learning has revolutionized computer vision with the development of neural networks capable of handling large, complex datasets.

4. Transformer-Based Approaches

Recently, transformers, originally developed for NLP, have been applied to computer vision tasks.

5. Other Advanced Approaches

Key Challenges in Computer Vision:

Applications of Computer Vision:

The field is continually advancing, with a growing focus on improving the efficiency, scalability, and generalization of models for a wider range of real-world applications.

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