When I started learning Computer Vision, I thought it was just about teaching computers to recognize pictures. But honestly, I discovered that there is so much more behind it! Through my lessons and practical notebook, I started understanding how computers process images and how we can use Python to help them recognize different things.
what i have got from this is;
first, I learned that computers see images as numbers called pixels. Every pixel contains information about brightness or colour. I also learned about RGB colour channels and how a colour image can be represented as three grids of numbers: red, green, and blue. This helped me understand that computers don't see images exactly like humans do.
One of the topics I found interesting was image filters and convolution. I learned that filters use small grids of numbers called kernels to process images. For example, a blur filter smooths an image, while the Sobel filter helps detect edges by finding changes in brightness.
Here is one of the filter examples I learned:
import numpy as np
blur = np.ones((5, 5)) / 25
sobel_x = np.array([
[-1, 0, 1],
[-2, 0, 2],
[-1, 0, 1]
])
sobel_y = sobel_x.T
The blur kernel averages nearby pixels to smooth the image. sobel_x helps detect vertical boundaries, while sobel_y helps detect horizontal boundaries. At first, these numbers looked confusing, but understanding what each filter does made the concept easier for me. 🥹
I also learned about Convolutional Neural Networks (CNNs). Unlike filters that we design ourselves, CNNs can learn their own filters from training images. They can learn simple features such as edges and colours, then combine them into more complex features like textures, object parts, and complete objects.
Understanding Different Computer Vision Tasks
Through the PPT and notebook, I learned that Computer Vision has different tasks:
Image classification: Predicts what an image contains, usually by giving it one main label.
Object detection: Identifies objects and shows where they are using bounding boxes.
Image segmentation: Identifies which pixels belong to different objects or regions.
OCR: Helps computers read text inside images.
Object tracking: Follows objects across video frames.
I also practised working with pretrained YOLO models in the notebook. I learned that YOLO means You Only Look Once. It detects objects in one main pass through an image, making it useful for fast predictions. We also explored how pretrained models can classify images, detect objects, and perform segmentation without building a model completely from scratch.
Another concept I learned was Vision Transformers (ViTs). They divide an image into smaller patches and process them like tokens in a language model. I found this interesting because it connects Computer Vision to the Transformer concepts I learned in NLP.
What Surprised Me Most
I learned that AI models are not always correct. In our lesson, we explored how changes such as dark lighting, turning an image upside down, or reducing its resolution can affect predictions. A model might miss an object or recognize something incorrectly because it has not learned enough from similar examples.
This taught me why good training data matters, why models need proper testing, and why using images that represent our own environment is important. I also discovered how Computer Vision can help in African communities, from identifying crop diseases to supporting healthcare and accessibility.
My Final Thoughts
Honestly, I am still learning, and some concepts were confusing at first. But this journey has helped me understand that Computer Vision is more than just making computers recognize pictures. It involves pixels, filters, mathematics, neural networks, and learning from data.
This is just the beginning of my Computer Vision journey, and I can't wait to use what I have learned to build something meaningful!
Top comments (2)
Great explanation of how computers see images and represent them as pixel values! You made the basics of computer vision easy to understand. I’d love to see you continue exploring with Python and OpenCV, experimenting with grayscale, blurring, edge detection, and filters, and gradually diving into CNNs and image classification. You’re building a great foundation—keep learning, experimenting, and growing! 🚀👏
Woww. This was an amazing read because you mentioned what surprised you the most and and you're so honest on how it's just the beginning of your computer vision journey and there is no doubt that you're going to keep learning more and more. Keep up the spirit of being eager to learn !!