Classical Image Processing
Classical Image Processing refers to traditional, non-deep-learning techniques for analyzing, enhancing, and manipulating digital images using mathematical and algorithmic methods. It involves operations like filtering, edge detection, segmentation, and morphological processing to extract features or improve image quality. These methods are often based on signal processing principles and are computationally efficient for many real-world applications.
Developers should learn classical image processing for tasks where interpretability, low computational cost, or limited data availability are priorities, such as in medical imaging, industrial inspection, or embedded systems. It provides a foundational understanding of image manipulation that complements modern deep learning approaches, and is essential for preprocessing steps in computer vision pipelines.