A world in which machines have the ability to “see” and perceive images in the same way that we do. It’s not science fiction! This is becoming a reality thanks to the computer vision. This tech is making a difference in everything from self-driving cars to medical diagnosis. Let’s see how it works.
10 — Foundations of Computer Vision
Computer vision enables computer systems to recognize and utilize images. It has a lot of moving parts working together. We need to hear about the basic ideas on these topics to understand what is happening. This is one of the fundamental concepts of modern computer vision.
14 Image Formation and Representation
How does a computer “see” a picture? The whole thing starts with image formation. A light strikes an object, and a camera records it. This data is converted into a digital image, a grid of pixels. Each pixel has color values. One common way to represent color is using RGB (red, green, blue). Here is another one: HSV (hue, saturation, value). This step is vital!
Image Processing Fundamentals
Once we have an image, we have a way of improving it. We use image processing to clean images and improve them. Similar to filters that blur or sharpen. Edge detection detects lines, borders, and edges. Morphological operations alter the shapes of an image. These are the basic components for more complicated tasks.
Feature Extraction
Let us now train computers to identify important things in an image. This is exactly what feature extraction does. It finds unique patterns. One method is SIFT (Scale-Invariant Feature Transform) Another of them is SURF (Speeded Up Robust Features). HOG (Histogram of Oriented Gradient) analyzes shapes. These create features to allow computers to identify objects.
The million dollar question: What do you need to know?
Computer vision changed a lot with the introduction of deep learning. It made computers dramatically better at “seeing” images. So, let us dive into why deep learning is so special. That basically empowers computers with an extremely intelligent brain of vision,
CNNs (Convolutional Neural Networks)
Convolutional Neural networks (CNNs) are the primary utility all deep learning methodologies are using for computer vision. They are made of layers. Patterns are found by convolutional layers. The pooling layer reduces that information. But activation functions make the network learn. This means that CNNs learn features on their own! It’s a big win.
CNN Architectures
Many CNN designs exist. AlexNet was an early success. VGGNet uses very deep layers. We all know ResNet is much easier to train on deeper networks. “This architecture is known as EfficientNet because it was designed to be efficient. However, each design has its own strengths. These networks improve with the passage of time.
A Beginners Guide to Pre-Trained Networks and How To Implement Them in Python
CNNs must be trained, and training requires time and data. But transfer learning allows us to take advantage of what has already been learned. We can utilize pre-trained models. These models were trained on huge datasets. Then, we tailor them for the specific tasks for which we use them. This helps to save time and resources.
Computer Vision Applications
Machine vision can be found around the globe. Let’s see some cool examples. It assists us in a variety of different means. The Potential is Kinda Crazy!
Familiarize with Visual Trends and Technologies
Object detection identifies objects in images. YOLO(You Only Look Once) is fast. SSD(Single Shot MultiBox Detector) is another possible solution. This process is used in autonomous vehicles. They are also used in surveillance systems. They let us know what else is out there.
Image Segmentation
Image segmentation splits an image into several unique regions. Semantic segmentation annotates a pixel level. Instance segmentation identifies individual objects. In medical imaging, it’s used to locate tumors. That’s what autonomous vehicles are using it to know roads.” It offers clear details.
Image Generation and Editing
Generative models — models that can generate new images. One example is GANs (Generative Adversarial Networks). Another one is VAEs (Variational Autoencoders). They can do image synthesis. That is called style transfer, the process of changing the style of an image. It fills in the missing parts and is called image inpainting. It’s almost like magic.
Challenges and Future Trends
Computer vision still has a long way to go. We need to make it better. Let’s think about what is to come. Now perhaps is where things get interesting!
Adversarial Attacks
Computer vision models can be fooled. Adversarial attacks make slight modifications to an image. Such modifications deceive the models.” This is a security risk. This is why we need to make models more robust.
Understanding and interpreting machine learning models
This is why we have to have an understanding of why models do what they do. This is where explainable AI comes in. Interpretable models reveal the reasons for their decisions. This increases trust in technology itself. We must have transparency!
Future Trends
Conclusion
SHARED: Self-supervised learning is a hot topic Few-shot learning is able to learn from few examples. Merging computer vision with other AI disciplines shows great promise. The future is bright!
But things have come a long way with the computer vision. It’s changing so much of our lives. It’s powerful from self-driving cars to medical imaging. There are still, however, hurdles to surmount. As the field matures, we can only look forward to more marvelous things. Want to explore further? Begin to learn and participate in the journey!
