Table of Contents
Computer Vision: How Machines Learn to See
TL;DR
Computer vision enables machines to understand images and videos using machine learning algorithms. The workflow involves data collection, preprocessing, model selection, and training. CNNs extract features through convolution and pooling layers, while transformers are gaining popularity for image tasks.
Computer vision powers numerous applications from medical diagnosis and autonomous vehicles to retail automation and robotics. Popular tools include TensorFlow, PyTorch, OpenCV, and Keras. As data quality and models improve, vision systems continue to become more accurate and widespread in daily life.
1. What Is Computer Vision?
Computer vision is an area of AI that enables machines to visually understand images and videos, using machine learning algorithms that find insightful information in visual data. In summary, this is at least a three-tier process: what is in it (recognition), what does the 3D shape look like, and how do the things relate to each other.
2. How Computer Vision Works
A common example is the detection of pneumonia using chest X-rays. The reading of X-rays is usually slow and at times difficult for doctors since signs are subtle. In this case, a computer vision system may assist by learning from many labeled X-rays and proposing likely diagnoses.
Typical workflow:
- 1. Data collection
- 2. Preprocessing
- 3. Model selection
- 4. Model training
3. Data Collection
First you collect images or video for the task. For medical tasks, hospitals can use past X-rays labeled as "normal" or "pneumonia". For other problems you might use camera or sensor feeds. Public datasets such as COCO, ImageNet and Open Images also help, providing many labeled images to train models.
4. Preprocessing
Models are as good as the data they're trained on. Preprocessing sanitizes and perfects images—say, changing brightness, resizing, smoothing, or removing noise. The datasets need to be large and diverse, so the models generalize well.
You can increase size and variety through a technique called data augmentation: rotate, flip, crop, and change contrast. In medical imaging, simple transforms—like small rotations—help the model learn the same condition from slightly different angles.
5. Model Selection
Choosing the right model affects both speed and accuracy. CNNs are still widely used for image tasks, but RNNs help when image frames are sequential, like in video. Recently, transformer-based models (especially ViT) have become popular. They divide an image into patches, treat them like tokens, and apply a self-attention mechanism across patches. They often match or beat CNNs on tasks such as image classification.
6. Model Training
Training includes running the selected model on labeled data, estimating the errors, and tuning the parameters for better performance. A typical CNN consists of convolution layers that enable feature extraction, pooling layers to reduce size, and fully connected layers that enable final classification.
Feature Extraction
Filters or kernels slide over the image, performing dot products with pixel values to produce feature maps. Each filter would respond to patterns such as edges, shapes, or textures. For X-rays, these could be cloudy areas, fluid pockets, or irregular contours of the lungs.
Pooling
After convolution, pooling reduces the size of the feature map by taking maximum or average values in regions. This retains the most salient signals and reduces computation.
Forward Pass and Loss
The model predicts outputs in a forward pass. A loss function measures the error between prediction and true label.
Backpropagation and Optimization
Backpropagation computes the gradients of the loss w.r.t. each weight. Optimizers such as gradient descent update the weights with the goal of reducing the loss over time.
Final Output
The fully connected layer provides class probabilities. For a chest X-ray, it could give the probability of pneumonia; if above a certain threshold, it flags the image for review.
7. Computer Vision Tasks
Different tasks that involve computer vision include:
Image Recognition
That is the broad idea of finding and naming objects, people, places or text in an image. It underlies many other tasks like classification and detection.
Image Classification
Classification assigns a label either to the entire image or to objects within it. The pneumonia X-ray example is a classic example of image classification; the model decides whether an X-ray shows pneumonia or not.
Object Detection
Object detection finds where the objects are in an image and labels them; it combines localization—which is drawing bounding boxes—with classification. Examples include traffic footage systems that can detect and locate cars, bikes, and pedestrians.
The popular models for detection include the R-CNN (two-stage detector) and YOLO (single-stage, real-time detector). For video detection, transformer models are often mixed with RNNs to handle time sequences, such as LSTMs.
Image Segmentation
Segmentation labels each pixel in the image. It gives finer detail than detection by drawing exact shapes and boundaries. Types include:
- Semantic segmentation: label each pixel with a class, not distinguishing instances (e.g., "road," "car")
- Instance segmentation: Label pixels for each object instance separately
- Panoptic segmentation: This combines semantic and instance segmentation to provide a full view of the scene
Segmentation is useful when objects are fragmented or exacting boundaries are important, to identify organ shapes in medical images.
Object Tracking
Tracking involves establishing feature correspondence between object identities across video frames. Applications include surveillance, sports analytics, and driver-assist systems.
Scene Understanding
Scene understanding goes beyond the identification of objects; it infers relations and events. Graph neural networks model spatial relationships between objects, such as a car in front of a taxi. VLMs combine image understanding with text to describe scenes in context.
Facial Recognition
It analyzes facial geometry and patterns of the face, including eye distance, the shape of the nose, and the jawline to identify individuals. Facial recognition is used in the unlocking of devices and in security systems.
Pose Estimation
Estimating pose provides the position of body parts with respect to body gestures and motion. Applications include sports analysis, gaming, and robot operation, for example, by keeping a robotic arm in proper alignment with objects in space.
Optical Character Recognition (OCR)
OCR extracts text from scanned pages or images and converts it into machine-readable text. Image acquisition commonly takes place along with preprocessing like deskewing, and character or word recognition. Modern CNNs and transformers increase accuracy in character and word recognition for OCR.
Image Generation
Generative models create new images. Common types are:
- Diffusion models: learn to remove noise from images to generate new samples
- GANs: These consist of a generative adversarial network where a generator creates images and a discriminator tries to tell real from fake. They train together
- Variational autoencoders: compress and reconstruct images to produce variations
These models can also generate images from text descriptions.
Visual Inspection
Computer vision inspects items for defects, spots corrosion, or finds faulty parts in manufacturing and infrastructure. It helps detect issues with much greater precision and speed through segmentation and detection.
8. Computer Vision Applications
Practical applications include:
Agriculture
Drones and cameras capture images of crops. Vision models analyze plant health, spot pests, and guide targeted treatments.
Autonomous Vehicles
Self-driving cars combine cameras with lidar, radar, and sensors. The entire vision tasks of detection, segmentation, and scene understanding assist the car to navigate safely.
Healthcare
Imaging for medical diagnosis employs mainly detection and segmentation to identify the markers of diseases from X-rays, CT scans, and MRIs. These tools support diagnosis and treatment planning.
Manufacturing
Automatic vision systems track inventory, scan items, and check product quality faster and more consistently than manual inspection.
Retail and E-commerce
Systems like Amazon's Just Walk Out rely on vision for tracking of items taken by customers, while augmented reality and pose estimation enable virtual try-ons for clothes and eyewear.
Robotics
Robots use vision in navigation, picking and placing of objects, and safely interacting with people and the environment.
9. Computer Vision Tools
Popular tools and libraries include:
Keras
Keras is a simple deep learning API that runs on top of frameworks like TensorFlow or PyTorch. It includes a variety of tutorials and examples dealing with images.
OpenCV
OpenCV is a general library of open-source image processing, comprising a great number of filtering, detection, and video analysis algorithms. It contains Python, Java, and C++ bindings.
Scikit-image
Scikit-image is a Python package with easy-to-use image processing functions useful for preprocessing and simple feature extraction.
TensorFlow
TensorFlow is a general deep learning platform from Google. It contains tools and datasets that can be used for image classification, segmentation, and detection.
Torchvision
Torchvision is a part of PyTorch and includes common image transforms, datasets, and prebuilt models for vision tasks.
10. A Brief History of Computer Vision
Computer vision started in the 1950s and the 1960s. Early experiments with animals revealed that they detect simple shapes, like lines, first. At the same time, computers gained ways to scan and digitize images. Over decades, methods evolved from simple shape detection through modern neural networks, which can recognize complex scenes and generate images.
Conclusion
Computer vision converts visual data from images and video into useful information. From medical image analysis to self-driving cars, computer vision solves many applications by harnessing data, preprocessing, model choice, and training. Thus, as the quality of data and models improves, vision systems are getting both more accurate and much more pervasive in daily life.