Introduction

Artificial intelligence has achieved remarkable advancements in enabling machines to see, interpret, and interact with the visual world. Computer vision now rivals or exceeds human performance at complex visual tasks like object recognition, image captioning, and scene understanding. In this post, we’ll explore the technical innovations behind AI’s visual perception capabilities.

Capturing Visual Data

A computer vision system starts by capturing visual stimuli from the environment through cameras and sensors. Built-in smartphone cameras or advanced multi-lens camera rigs with RGB and depth sensing sample physical scenes and converts them into digital image data.

The pixel intensity values in image matrices encode visual information like colour, edges, textures, and objects. Pre-processing techniques like noise filtering, distortion correction, and contrast normalisation modify the raw images before analysis by machine learning models.

Recognising Patterns with CNNs

At the core of modern computer vision are Convolutional Neural Networks (CNNs), which are specialised deep learning models inspired by biology and designed to recognize spatial patterns in image data.

CNNs apply a series of trainable filters to the input image to extract hierarchical feature representations. The filters detect low-level features like edges, textures, and object parts in the initial processing layers. Later layers then assemble these into higher-level features such as faces, objects, and scenes.

Stacking many convolutional layers enables increasingly abstract pattern recognition, given enough labelled training data through tools such as ImageNet. CNN breakthroughs have driven massive performance gains in computer vision over the last decade and have been pivotal in this area.

Understanding Context and Relationships

Identifying objects is only the first step toward holistic scene understanding. The goal is to also determine spatial relationships between objects, infer depth, lighting, material properties, and overall context.

Approaches such as Graph Neural Networks and Capsule Networks develop structured representations of object relationships. CNNs then aggregate global context across image regions and Generative AI models fill in any missing visual details.