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.
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