Computer vision is a field that uses machine learning (ML) but is not strictly a subset of it. While machine learning provides critical tools for solving computer vision problems, computer vision encompasses a broader set of techniques, including traditional image processing algorithms and domain-specific methods. Think of machine learning as one of the primary tools in the computer vision toolbox, especially for tasks that require learning patterns from data, like object detection or image classification. However, computer vision also involves non-ML approaches, such as edge detection filters or optical flow calculations, which rely on mathematical models rather than trained algorithms.
A key area where machine learning and computer vision intersect is in training models to interpret visual data. For example, convolutional neural networks (CNNs), a type of ML model, are widely used in computer vision to recognize patterns in images. Tasks like facial recognition, medical image analysis, or autonomous vehicle navigation rely on CNNs trained on large datasets. These models learn features directly from pixel data, eliminating the need for manual feature engineering. However, computer vision systems often combine ML with non-ML components. For instance, a self-driving car might use traditional computer vision techniques (like lane detection via Canny edge detection) alongside ML models (like a CNN for identifying pedestrians) to build a complete solution.
It’s important to distinguish between the goals of the two fields. Machine learning focuses on creating algorithms that learn from data, while computer vision aims to extract meaningful information from visual inputs. For example, image preprocessing steps like resizing, noise reduction, or color space conversion are fundamental to computer vision but don’t inherently involve ML. Similarly, classic algorithms like the Hough Transform (for detecting geometric shapes) or SIFT (for feature matching) are staples of computer vision that predate modern ML. That said, ML has become central to advancing computer vision—modern frameworks like PyTorch or TensorFlow include libraries (e.g., TorchVision, TF Vision) specifically for vision tasks. This integration highlights how ML augments computer vision, even though the fields remain distinct in scope and methodology.