Deep learning has not killed traditional image processing or classical computer vision techniques. Instead, it has enhanced and, in many cases, complemented them. Deep learning excels in tasks like object detection, semantic segmentation, and image classification, where learning complex patterns from large datasets is crucial. However, traditional image processing techniques, such as edge detection, histogram equalization, and contour extraction, remain valuable for simpler tasks or preprocessing. In many practical applications, a combination of classical methods and deep learning provides the best results. For example, classical techniques are often used to preprocess images or reduce computational complexity before applying deep learning models. While deep learning has revolutionized the field of computer vision, traditional image processing methods are still widely used and relevant.
Is deep learning killing image processing/computer vision?

- Getting Started with Milvus
- AI & Machine Learning
- Natural Language Processing (NLP) Basics
- Advanced Techniques in Vector Database Management
- How to Pick the Right Vector Database for Your Use Case
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
How does vector search compare to keyword search?
Vector search and keyword search are two distinct methodologies for retrieving information, each with its unique strengt
What are the challenges of VR content streaming?
Streaming virtual reality (VR) content presents several challenges that developers must address to ensure a seamless exp
What are the benefits of zero-shot learning?
Zero-shot learning (ZSL) is an approach that allows machine learning models to make predictions about classes they have