How is pattern recognition different from computer vision?

Pattern recognition and computer vision are related but distinct fields. Pattern recognition focuses on identifying regularities or patterns in data using statistical and machine learning techniques. It applies to any type of data—text, audio, images—and aims to classify or categorize information based on learned features. For example, spam filters use pattern recognition to detect email spam by analyzing word frequencies. Computer vision, however, deals specifically with visual data (images or videos) and aims to enable machines to interpret and act on visual information. It involves tasks like object detection, image segmentation, and scene reconstruction, often requiring understanding spatial relationships and context. While pattern recognition is a tool used within computer vision, the latter encompasses broader processes like preprocessing images (e.g., noise reduction) and reconstructing 3D scenes.

The scope and techniques differ significantly. Pattern recognition algorithms, such as k-nearest neighbors (KNN) or support vector machines (SVM), work across domains. For instance, speech recognition systems use pattern recognition to map audio signals to words. In contrast, computer vision relies heavily on techniques tailored to visual data, such as convolutional neural networks (CNNs) for detecting edges or textures, and methods like optical flow for tracking motion in videos. A key distinction is that computer vision often requires handling raw pixel data and transforming it into meaningful representations (e.g., extracting features like corners or gradients) before applying pattern recognition. For example, a facial recognition system might first use computer vision to align a face in an image, then apply pattern recognition to match it against a database.

Applications highlight their differences. Pattern recognition is used in diverse scenarios like medical diagnosis (e.g., classifying tumors from lab results) or financial fraud detection. Computer vision, however, is domain-specific: autonomous vehicles use it to detect pedestrians, while augmented reality apps rely on it to overlay digital objects onto real-world scenes. The two fields intersect when visual data requires classification—for instance, a computer vision pipeline might use pattern recognition to label objects detected in an image. However, computer vision often involves additional layers, such as handling varying lighting conditions or camera angles, which are less relevant in general pattern recognition. In summary, pattern recognition is a foundational technique applicable across data types, while computer vision is a specialized discipline focused on solving visual interpretation problems.