Computer Vision — Why does it matter?
Look at the picture below. What do you see? If I had asked you to name a few objects, you would say “Eiffel Tower“, “girls“, “ food “,” handbag”, ”man”, “grass” and much more. If I had asked you to describe the scene, you might say this as a picture depicting a friends’ get together at the Eiffel tower without giving much thought.
How is it possible to name and describe every object there? Human vision involving our eyes identifies every object because of the millions and billions of interactions and encounters with these objects physically or mentally aiding to a conceptual understanding. But for a computer, the above image is just an array of pixels and values representing colors. And this is where computer vision comes into the picture.
Computer vision, a field of computer science that trains computers to have a higher-level understanding of digital images and videos thereby identifying, analyzing, and classifying them the same way humans do. This field has even been able to surpass humans in tasks like detection and labeling objects. The driving force behind the leap of computer vision is the advancement of artificial intelligence, innovation in deep learning, and the tremendous amount of data generated today viable to train the computer and make it better.
Firstly, many images and other data for a particular object are fed in, as an input to make an accurate model, making detection of that particular object errorless and accurate, giving the desired output. Remember, the higher the amount of data, the higher the accuracy.
Working of Computer Vision
Vision AI, otherwise called Computer Vision works in three basic steps.
- Image Acquisition
- Image Processing
- Image Classification
Image Acquisition
Images can be obtained through video, photos, 3D technologies for analysis.
Image Processing
Deep learning models are trained by feeding many labeled images/data and these models are often automated.
Image Interpretation
Classification or detection of an object by understanding it. AI takes desirable action by understanding the image and this can be done in many ways.
Image Segmentation — Partitions an image into multiple pieces or regions to analyze
Object Detection — Identifying a specific object in an image
Face Detection — Higher level object detection that detects only human faces in an image as well as specific individuals.
Pattern Detection — Identification of repeated shapes, colors, and other indicators in single or multiple images.
Image Classification — Classifies or groups objects/images into different categories.
Feature Matching — Finding similarities in images and matching them thereby classifying.
Object Tracking — Tracking/following a specific object or multiple objects.
Single or multiple techniques listed above can be used in order to accomplish the user’s goal.
Application of Computer Vision
Agriculture: Enables farmers to adopt efficient growth strategies, increase yields and profits.
Automotive: CV can be used to add safety features to our vehicles detecting objects like road signs, traffic lights enabling self-driving.
Biotechnology: The biological functions of molecules, say proteins can be predicted from their atomic structure.
HealthCare: CV enables the detection of illness with high precision and accuracy minimizing false positives and many more.
Biometrics: CV is used in biometric identification such as fingerprint, face matching.
Conclusion
One of the major challenges of vision AI is that a large amount of data needs to be scaled and trained. But with better resources and algorithms to train models, computer vision paves way for endless possibilities and opportunities in diversified fields like medicine, military, automobile, and a lot more.
That’s all folks. I would really appreciate it if you’ve made it till the end. If you enjoyed this, I’d love it if you could hit the clap button 👏. You can also find me on LinkedIn.
