Computer vision

Celia Kaligirwa — Tue, 13 Oct 2026 14:12:08 +0000

INTRODUCTION

Humans use their eyes and brain to recognize objects and notice details whereby computers do not see in the same way. They receive images as numbers and use mathematical operations and trained models to identify objects and make predictions.

Computer vision is simply a way computers use to process and understand visual information.

IMAGE PROCESSING

This is a way the image is changed, improved, analyzed by a computer to get useful information from it.
It comprises processes like;

Sharpening : making image more clear and identifying details
Grayscale : removing colors from an image and replaing it with shades of gray, black and white.
Blurring : making an image smoother by removing sharp details
Edge detection : help a computer to analyse the boundaries and shape of an image

COMPUTER VISION TASKS

Image classification : identifies what an image contains or what is in an image
Object detection : identifies an object in an image and shows where they are located using boxes and confidence
Image segmentation : identifies the number of pixels of each object

COMPUTER VISION MODELS FAILURES AND IMPERFECTION

Things such as poor lighting, blurry images, and unusual angles can affect their results. This is because the results depend on how much the model was trained. So the model needs to be tested with a variety of images so that it gets familiar.

This image shows how some details where not mentioned maybe because the model was not trained enough to know about those predictions. Some of the predictions are right others are wrong and some of them are not even in the image.

This image shows different predictions of the same image just because it was it was slightly changed and the model was not trained enough to predict same output.

CHALLENGES I FACED WITH THIS UNIT

Some challenges i faced where that the tasks are all relating to the image yet they perform differently. One notices an object another where its located and the other one the number of pixels belonging to it.

COMPUTER VISION APPLICATIONS IN REAL LIFE

It can help identify objects in security systems, support medical image analysis, and allow self-driving cars to recognize their surroundings without causing accidents. Also, the way our phones recognise our faces with the FaceID feature.

CONCLUSION

This unit helped me understand more of how computers see and make sense of what they see by analysing and making predictions from what they are seeing yet they've not seen it before. It's just a matter of training the model.

DEV Community: Celia Kaligirwa

Computer vision