How Does Computer Vision Work?
Computer vision is at the heart of some of the most impressive artificial intelligence (AI) applications, from self-driving cars that navigate complex environments to quality control systems that can identify microscopic defects in manufacturing. It is bridging the physical world to the world of software. But how does it actually work?
Approximately 90% of the information transmitted to the human brain comes from our sense of vision, making it our most important sensory channel. Given this, it’s no surprise that vision is also a critical mode for AI systems trying to replicate human perception.
In this article, we’ll break down the step-by-step process behind computer vision, reveal why it’s more than just machine learning, and highlight some real-world applications.
What Is Computer Vision?
Computer vision is the science of teaching machines to interpret and understand the visual world. It’s a critical part of AI, blending the power of deep learning, image processing, and pattern recognition to enable machines to make sense of images and videos.
How Does Computer Vision Work?
When we look at an image, our brains instantly recognize objects, colors, and patterns. But for a computer, seeing an image means processing a grid of pixels.
Images: Each pixel in an image is a tiny dot that holds color information represented as numerical values, typically in the form of RGB (Red, Green, Blue) values that are numbered between 0 and 255.
Why this number? Because each color channel is typically using 8 bits, which allows for 256 distinct values (or 0-255). A grayscale image has a simpler structure than an RGB image, as it uses only a single color channel to represent intensity, rather than three.
Videos: A video is essentially a sequence of individual images, or frames, played back rapidly to create the illusion of motion. In an RGB video, each pixel contains three color channels (Red, Green, and Blue) each using 8 bits of data, resulting in 24 bits per pixel.
For a Full HD video with a resolution of 1920x1080 pixels, each frame contains 2,073,600 pixels. Grayscale videos are even smaller, as each pixel is represented by just 8 bits instead of 24, reducing the file size by about two-thirds.
Computer Vision Models
When an image is fed into a computer vision model, it first undergoes preprocessing to adjust size, normalize color values, and remove noise. Next, the model uses convolutional layers to detect features such as edges, textures, or shapes by applying filters that scan across the pixel grid. These filters highlight the important visual elements that make objects distinct.
The data then passes through deeper neural network layers where more complex patterns emerge. This process of hierarchical feature extraction allows the model to recognize increasingly abstract concepts, from basic shapes to entire objects. Finally, the output is analyzed, classified, or used to make decisions, such as identifying a face or detecting a potential hazard.
At their core, these models rely on mathematical operations like convolution, matrix multiplications, and non-linear activations to transform raw pixel values into high-level features for tasks like classification, object detection, and segmentation.
Types of Computer Vision Models
Convolutional Neural Networks (CNNs) are the foundational architecture for most computer vision tasks. They use a series of convolutional layers to extract spatial hierarchies of features, starting with simple edges and textures and moving up to complex shapes and patterns. Popular CNN architectures include AlexNet, VGGNet, ResNet, and EfficientNet, each with unique design choices that balance accuracy and computational efficiency.
Vision Transformers (ViTs) are a newer approach inspired by the transformers used in natural language processing (NLP). Unlike CNNs, ViTs process images as sequences of fixed-size patches, treating each patch like a word in a sentence. This approach allows the model to capture long-range dependencies in the data using self-attention mechanisms, making them highly flexible and effective for large-scale image datasets. ViTs excel at capturing global context but often require substantial computational power without significant pre-training.
Hybrid models such as Convolutional Vision Transformers (CvT) and ConvNext combine the strengths of CNNs and transformers. These architectures use convolutions for early feature extraction and transformers for deeper, long-range feature interactions, providing a balance of efficiency and performance.
Other specialized models include object detection architectures like YOLO (You Only Look Once), Faster R-CNN, SSD (Single Shot MultiBox Detector), and Detectron2, which are designed not just to identify objects but also to locate them within an image using bounding boxes.
Additionally, models like Recurrent Neural Networks (RNNs) are sometimes used for video analysis, while Generative Adversarial Networks (GANs) generate synthetic images by learning the distribution of real-world data. Graph Neural Networks (GNNs) are also gaining popularity for understanding relationships between objects in images.
Different computer vision tasks require different models. CNNs are often ideal for image classification, ViTs are better for capturing long-range relationships, and object detection models excel at both classification and localization, making each type suited to specific challenges in computer vision.
Computer Vision Tasks
Computer vision encompasses a wide range of project types, each designed to tackle specific visual tasks.
Object detection
One of the most common approaches is object detection, which focuses on identifying objects and their positions within an image. This involves drawing bounding boxes around objects, counting instances, and tracking their movement over time. Object detection is widely used in applications such as surveillance, autonomous vehicles, and retail analytics, where precise localization and identification are critical.
Classification
Another key project type is classification, where the goal is to assign labels to entire images based on their content. This can range from simple single-label classification, like identifying whether an image contains a cat or a dog, to multi-label classification, where an image can contain multiple objects or concepts. This approach is often used in medical image diagnosis, species identification, and quality control in manufacturing. Advanced options like content moderation and image filtering are also part of this category, allowing for more nuanced interpretations of image content.
Instance segmentation
For more detailed analysis, instance segmentation provides a way to detect multiple objects and capture their exact shapes within an image. Unlike object detection, which uses bounding boxes, instance segmentation identifies precise object boundaries using polygons or freeform masks, making it ideal for tasks like medical imaging, autonomous driving, and agricultural monitoring, where exact shape information is crucial.
Keypoint detection
Keypoint detection is another specialized project type, focused on identifying specific points or "skeletons" on subjects, such as the joints in a human body. This approach is commonly used in pose estimation, motion capture, and facial landmark detection, where the precise positions of joints or facial features are essential for understanding movement and expression.
Multimodal
Multimodal approaches combine image data with other forms of input, such as text, to create a richer understanding of context and meaning. This is at the cutting edge of AI research, enabling applications like visual question answering, image captioning, and cross-modal retrieval, where both visual and textual information are analyzed together to generate deeper insights.
Semantic segmentation
Semantic segmentation offers a pixel-level understanding of an image by assigning a class label to every pixel. This approach is essential for tasks where precise spatial information is required, such as autonomous driving, medical imaging, and aerial drone mapping. Unlike instance segmentation, which separates individual objects, semantic segmentation groups all pixels of the same class into a single category, making it ideal for applications like road scene understanding or agricultural crop monitoring.
What Are the Steps Involved in Computer Vision?
Building a computer vision system involves a series of well-defined steps that transform raw visual data into meaningful insights.
1. Data collection
The first stage is data collection, where high-quality, representative images or videos are gathered. This data can come from public sources such as ImageNet, COCO, MNIST, or datasets on Universe, or be captured directly from cameras, medical imaging devices, or synthetic simulations. The diversity, quality, and volume of this data are critical, as models trained on narrow data often struggle to generalize to new scenarios. For instance, a facial recognition model trained only on a limited demographic may perform poorly on a broader population, leading to biased or inaccurate predictions.
2. Standardize and preprocess
Once collected, the data needs to be standardized and preprocessed to ensure consistency and improve model performance. This step typically includes resizing images to a fixed resolution, normalizing pixel values to a standard range, and removing noise. For tasks like edge detection, converting images to grayscale can simplify processing by reducing the number of color channels and focusing the model on intensity changes. Data augmentation, such as rotating, flipping, or cropping images, can artificially expand the training set, improving the model’s robustness to variations in the real world.
3. Data labeling
Labeling the data is a critical step in supervised learning, as it provides the ground truth the model uses to learn. In image classification, this might mean assigning a single category to each image, such as "cat" or "dog." In object detection, it requires drawing bounding boxes around objects within an image, while semantic segmentation involves pixel-level labeling. More complex tasks, like human pose estimation, require identifying key points, such as joints or facial landmarks. Accurate and consistent labeling is essential, as errors at this stage can significantly degrade model performance, leading to poor predictions and increased error rates in production.
4. Split data
After preprocessing and labeling, the data is split into training, validation, and test sets. The training set, often 60-80% of the total data, is used to teach the model to recognize patterns. The validation set, typically 10-20%, is used to tune hyperparameters like learning rate, batch size, and dropout rates, helping to prevent overfitting. The test set, a similarly sized subset, provides an unbiased measure of the model’s generalization ability, ensuring it can handle new, unseen data. This separation is critical, as models that perform well on training data alone may fail in real-world applications if they cannot generalize effectively.
5. Train a model
During training, the model iteratively adjusts its weights based on errors it makes, gradually improving its ability to make accurate predictions. This process often involves backpropagation and gradient descent, where the model learns to minimize a loss function that measures its prediction error. Regularization techniques, such as dropout and batch normalization, are often used to prevent overfitting and improve generalization.
Once trained, the model undergoes validation to fine-tune hyperparameters and optimize performance. Common evaluation metrics include accuracy, precision, recall, and F1-score, each offering a different perspective on model performance beyond simple accuracy. Precision measures the proportion of true positive predictions among all positive predictions, while recall captures the proportion of true positives among all actual positive instances. The F1-score balances these two metrics, providing a single measure of performance that is particularly useful when dealing with imbalanced datasets.
6. Deploy
If the results are satisfactory, the final step is deployment, where the model is integrated into real-world applications like factory inspection systems, smartphone apps, or autonomous vehicles. This stage may involve additional optimization for speed and efficiency, such as model pruning, quantization, or converting the model to a lighter format like TensorRT or ONNX for faster inference. Monitoring and maintenance are also crucial, as real-world data can differ significantly from training data, requiring periodic updates and retraining to maintain performance.
Each of these steps is essential to building a reliable computer vision system. No matter the computer vision software you use, poor-quality data, inconsistent preprocessing, or improper training/test splits can lead to inaccurate models, wasted resources, and failed projects.
At Roboflow, we make it easier to build, train, and deploy these systems. Get started free or talk to an AI expert about your unique use cases.
Is Computer Vision Artificial Intelligence Or Machine Learning?
Artificial Intelligence (AI) and Machine Learning (ML) are closely related but distinct fields. AI refers to the broader concept of creating machines capable of performing tasks that typically require human intelligence, such as problem-solving, understanding language, and recognizing images.
Machine learning is a subset of AI that focuses on algorithms and statistical models that enable machines to learn from data and improve their performance over time without being explicitly programmed for every task.
Computer vision, which allows machines to interpret and understand visual information, can fall under both AI and ML, depending on the approach used. At its core, computer vision is an AI field, as it aims to replicate human visual perception. However, most modern computer vision systems rely heavily on ML techniques to identify patterns, recognize objects, and classify images.
Deep learning, a specialized subset of ML, has become the dominant approach in computer vision today. Deep learning has become so prevalent in computer vision that it is often assumed to be the default approach for tasks like image classification, object detection, and facial recognition. This widespread use is driven by the availability of massive labeled datasets, powerful GPUs, and optimized libraries like TensorFlow and PyTorch, making deep learning not just an option but a critical component of modern computer vision systems.
Ultimately, whether computer vision is classified as AI or ML depends on the specific models and techniques being used. In most real-world applications today, computer vision is primarily driven by deep learning, leveraging large datasets and sophisticated algorithms to achieve human-level visual understanding.
Using Computer Vision
Computer vision connects the digital and physical, enabling software to understand, interact with, and improve real-world processes. From preprocessing raw images to deploying models in production, each step in the computer vision pipeline plays an important role in building intelligent, adaptable systems that can impact diverse industries. Understanding how computer vision works is the first step toward creating powerful visual solutions.
Written by Nick Davis. Nick is a doctor of artificial intelligence and has worked in healthcare, space and aerospace, and clean energy.