The world as we know it, is changing with computer vision. Consider self-driving vehicles, robots in factories and doctors relying on computers to diagnose diseases. But how do you learn about this exciting field?
Computer vision enables computers to “see” images and comprehend their contents like human eyes. It’s a big deal in our world. Below is an overview of what a computer vision textbook should include_ We will realize the fundamental elements, cutting edge stuff, and genuine utilizations for it.
Foundations of Image Formation and Processing
To begin with, you have to know how images are produced and how we use them. This is the same as preparing the ground to build a house. This is where you begin to learn about computer vision.
Image Formation Models
How do cameras make pictures? Its all light, reflection and how the camera itself works. Light bounces off of things, enters the camera, and creates an image. We are assuming as the case the pinhole camera model. Lens distortion is a factor. Make sure to mix it up with your radiometric models and lighting conditions.
Image Processing Techniques
And now let’s move on to some image enhancement. That can involve cleaning up blurry images or clarifying things. We can apply cuts to smooth images, detect edges, or enhance the appearance of a photo. A lot can be done by filtering, linear and non-linear. Edge detection also does, e.g. Sobel or Canny. Also, don’t forget image enhancement with histogram equalization. You will absolutely need Fourier transforms!
Human-like: Image Filtering and Edge Detection
Let’s break down further on removing noise from images and edge detection. Noise can render an image difficult to work with, and edges are where things begin and end. Gaussian blur can smooth over noise, a median filter can remove it, and a Laplacian filter can enhance the details. The first derivative of Gaussian used to find edges.
Feature Extraction and Representation
Then, we need to extract better parts from the pictures. These pieces are known as features. They assist the computer in understanding what it is seeing.
Local Feature Detectors
These allow computers to locate key points in an image. These places are distinctive and seldom vary even when you view them from another point of view or in new light. Harris corner detector finds corners in the images. It uses SIFT (Scale-Invariant Feature Transform). The Speeded-Up Robust Features (SURF) is also useful. Both BRIEF (Binary Robust Independent Elementary Features) also works.
Feature Descriptors
After you have found features, you must describe them. This simplifies them so that it becomes easier for the computer to compare them. For example, it could be HOG (Histogram of Oriented Gradients). Secondly, another one the Bag of Words (BoW) model.
Feature Matching and Object Recognition
Now we can cross features across images. This is to assist the computer with object recognition. RANSAC (RANdom SAmple Consensus) excels at this type of fitting. Nearest neighbor search find the closest match. Then you build some simple systems to recognize objects.
3D Vision and Geometry
What about seeing in 3D? Computers can do that too!
Camera Calibration & Geometry
Before we begin, we need to calibrate the camera. It involves understanding how the camera perceives the universe. This includes things like homogeneous coordinates. There is the camera matrix and calibration patterns, as well as Zhang’s calibration method.
Stereo Vision
Take two cameras, look at the same thing, and you can see it in three dimensions — this is how stereo vision works. Here is where epipolar geometry is involved. Consider stereo correspondence, disparity maps, depth estimation.
Structure from Motion
What if you have just your one camera? Can you still see in 3D? Yes! With a moving camera, you can build a 3D scene. So this is critical here for feature tracking. So networks for bundle adjustment or incremental reconstruction.
Deep Learning In Computer Vision
Deep learning is a branch of AI that’s made a splash in computer vision.
Convolutional Neural Networks (CNNs)
In the deep learning world, CNNs are the most important tools for images. They learn to identify patterns. Data pre-processing layers, conv layers, and so on Pooling layers are also useful. Then you have activation functions, such as ReLU. In the end, backpropagation updates the weights. Some well-known CNN architectures are AlexNet, VGGNet, and ResNet.
Object and Semantic Segmentation
Here’s how you identify objects in an image and interpret what each part of the image describes. Explore the R-CNN family of models (Faster R-CNN and Mask R-CNN). YOLO (You Only Look Once) is also really good. We can also use SSD (Single Shot MultiBox Detector). Lastly, look into fully convolutional networks (FCNs).
How do Generative Adversarial Networks (GANs) work?
GANs are cool. They are able to produce original images or modify existing images. GANs consists of generator and discriminator networks. All we can do is train GANig GANs, and deploy them for image synthesis, style transfer, and super resolution.
Computer Vision Applications
Computer vision finds its application in numerous domains!
Autonomous Vehicles
For example, self-driving cars use computer vision to see the road, cars, and people. It needs lane detection, object detection, traffic sign recognition, pedestrian detection.
Medical Image Analysis
Doctors, too, use computer vision to diagnose diseases from medical images. They employ techniques like Image Segmentation for Tumor Detection. Computer-aided diagnosis (CAD) systems can assist in this approach. Add to that medical image registration and you have more to think about.
Robotics and Automation
Computer vision is used by robots to perform tasks in factories and warehouses. It relies on visual servoing, object recognition for robot manipulation, and autonomous navigation.
Conclusion
On the other hand, this article pretty much goes through a computer vision textbook. So we’ve covered making images, finding features, seeing in 3D, deep learning, and real world applications.
Computer vision is revolutionizing our world, hence the need to learn it. You can dive in, find your way into the field, and create your future.
