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?
- Accelerated Vector Search
- Large Language Models (LLMs) 101
- Optimizing Your RAG Applications: Strategies and Methods
- Evaluating Your RAG Applications: Methods and Metrics
- The Definitive Guide to Building RAG Apps with LlamaIndex
- 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
What policies govern the deployment of federated learning?
Federated learning is governed by a set of policies that focus on data privacy, security, and collaboration among differ
How do you evaluate the quality of embeddings?
Evaluating the quality of embeddings involves assessing how well the embeddings capture the underlying structure and rel
How is SSL applied in computer vision tasks?
SSL, or semi-supervised learning, is applied in computer vision tasks to enhance model performance by utilizing both lab