Computer vision is not strictly a subset of machine learning (ML), but it heavily relies on ML techniques to solve many of its core problems. Computer vision focuses on enabling machines to interpret and understand visual data, such as images or videos, while machine learning provides tools to build models that learn patterns from data. Though ML is a critical tool in modern computer vision, the field also incorporates non-ML methods, such as traditional image processing algorithms. For example, edge detection or noise reduction often use predefined mathematical operations rather than learned models. This blend of approaches means computer vision is a multidisciplinary field that intersects with ML but isn’t confined to it.
Machine learning plays a central role in solving complex computer vision tasks that require recognizing patterns or making decisions from visual data. Convolutional neural networks (CNNs), a type of ML model, are widely used for tasks like object detection or image classification. For instance, a CNN trained on labeled images can identify cats in photos by learning hierarchical features like edges, textures, and shapes. Similarly, support vector machines (SVMs) or decision trees might classify images based on extracted features like color histograms. These ML-based approaches automate tasks that are difficult to program with rigid rules, especially when dealing with variability in lighting, angles, or object appearances. ML’s adaptability makes it indispensable for modern computer vision systems, such as facial recognition or autonomous vehicles.
However, computer vision also includes techniques that don’t involve ML. Traditional algorithms, like the Hough Transform for detecting geometric shapes or optical flow for tracking motion in videos, rely on signal processing and mathematical transformations. OpenCV, a popular computer vision library, provides many such non-ML tools for tasks like image filtering or feature matching. Even in ML-driven applications, preprocessing steps (e.g., resizing images or normalizing pixel values) often use deterministic methods. This combination of ML and classical approaches highlights that computer vision is a broader field. While ML has expanded its capabilities, the discipline remains rooted in principles from mathematics, physics, and engineering, making it a distinct area that leverages ML rather than being subsumed by it.