Introduction to Computer Vision

Computer vision is the technology that teaches computers to understand pictures and videos. Look at a photo of a coffee cup and you know right away what it is. You may even guess the coffee is hot. A computer sees none of that. It sees only numbers, and computer vision is the set of ideas that turns those numbers into meaning. What a computer really sees You can think of a colour image as three grids stacked on top of each other: one for red, one for green and one for blue. A red cup looks bright in the red grid and dark in the blue grid. Three questions every vision system answers Humans and computers see differently Humans Years of seeing the world 2.Understanding context, learning from few examples 3.Getting tired Computers Millions of labelled images 2.Speed and working at large scale 3.Things that look different from their training images A computer can check thousands of X-rays in a day without getting tired. But it can easily fail on something it has never seen before. We will come back to this later. Five core tasks Classification: give one label to the whole image. Detection: draw a box around each object. Segmentation: give a label to every single pixel. OCR: read the text inside an image. Tracking: follow objects from one video frame to the next. Here is an example. Take one photo of a street. A classifier may say "minibus" and be 57% sure. That is not wrong, but it is not very useful. A detector will find one bus and four people and draw boxes around them. A segmenter will outline exactly which pixels belong to each object. The more detail you want, the more detailed the training labels must be, and the more it costs to create them. How a computer finds patterns Letting the computer learn its own filters Finding objects fast with YOLO Why computer vision is growing now Computer vision across Africa When computer vision fails Tools to get started Key takeaways • Images are just numbers: height × width × 3. • Filters find patterns, and CNNs learn the filters by themselves. • The main tasks are classification, detection and segmentation. • Computer vision is already used in farming, health, conservation, finance, mapping and accessibility across Africa. • Models fail on things they have not seen, so local data matters. A good way to start is to take 5 to 10 photos of your own surroundings, such as a street, a market stall or a crop leaf, and run them through a pretrained model. Notice what it gets right and what it misses. That is the best way to understand both the power and the limits of computer vision.