Computer Vision

Computer vision enables machines to interpret and extract meaning from images and video. By applying machine‑learning algorithms to visual data, computers can see and understand the world around them.

What is computer vision used for?

• Image classification: Identifying the main subject of an image, such as recognising whether a photo contains a cat, a plant or a manufacturing defect.
• Object detection: Locating and labelling multiple objects in an image, which is critical for self‑driving cars (pedestrians, traffic signs) and automated surveillance.
• Face recognition & biometrics: Verifying identities at border control, unlocking smartphones or tagging friends in social media.
• Medical imaging: Analysing X‑rays, MRIs and CT scans for diagnosis and treatment planning.
• Quality control: Inspecting products in manufacturing to detect defects and ensure standards are met.
• Augmented & virtual reality: Mapping the physical environment to overlay digital content or enable immersive experiences.

Key Concepts

• Convolutional Neural Networks (CNNs): Deep neural networks that learn spatial hierarchies of features from images. Layers of filters detect edges, textures and shapes.
• Transfer learning: Reusing models pre‑trained on large datasets (ImageNet) and fine‑tuning them on your own images to save time and improve accuracy.
• Image classification: Assigning an entire image to a class. Common architectures include ResNet, VGG, Inception and EfficientNet.
• Object detection & localization: Algorithms like YOLO, SSD and Faster R‑CNN identify multiple objects and their bounding boxes in real time.
• Segmentation: Dividing an image into segments or classes. Semantic segmentation labels each pixel (e.g., road, vehicle), while instance segmentation distinguishes between individual objects.

Challenges & Considerations

• Data quality & annotation: Computer vision requires large datasets with accurate labels; collecting and labeling data can be time‑consuming.
• Hardware requirements: Training vision models often requires GPUs or specialised accelerators due to high computational demands.
• Generalisation: Models may perform poorly when exposed to lighting changes, occlusions or different camera angles; augmentation and diverse data improve robustness.
• Privacy & ethics: Applications like facial recognition raise concerns about surveillance and consent; implement safeguards and follow regulations.

Free Resources

• Keras Applications – Pre‑trained CNNs for transfer learning.
• OpenCV – Open‑source library for image processing and computer vision.
• ImageNet – Large‑scale dataset used to train and benchmark vision models.