Introduction to Computer Vision

If you’ve ever looked at a photo and instantly known “that’s a cat,” you’ve performed computer vision.

For us humans, this is easy. We’ve had millions of years of evolution to perfect our visual cortex. But for a computer, an image is just a massive grid of numbers (pixels). Computer Vision is the science of teaching machines to make sense of this grid—to see, not just look.

What is Computer Vision?

At its core, Computer Vision is the discipline of extracting information from visual data.

Think of it as a function where the input is an image and the output is data. While a camera lens merely captures photons, computer vision interprets them. It asks questions like “What object is this?”, “How far away is it?”, or “Is it moving?”. This is an interdisciplinary field sitting at the intersection of Artificial Intelligence, Physics, and Geometry.

Computer Vision vs. Image Processing

It is important to distinguish Computer Vision from Digital Image Processing, although they often work together.

• Image Processing: Input is an image, Output is an image. (e.g., blurring, sharpening, adjusting contrast). The goal is to enhance or transform the visual appearance.
• Computer Vision: Input is an image, Output is data. (e.g., “There is a stop sign at coordinates x,y”). The goal is to understand the content.

We typically categorize fundamental Computer Vision tasks into Recognition (“What is this?”), Detection (“Where is it?”), and Measurement (“What is its 3D geometry?”).

The Inverse of Computer Graphics

To understand Vision, it helps to compare it with its sibling: Computer Graphics. The two fields are mathematically inverses of each other.

In Computer Graphics, you start with data—a 3D wireframe model, light source coordinates, and texture maps—and you run a “forward” process called rendering to generate a 2D image (like a frame from a Pixar movie or a video game). The computer already knows everything about the scene because it created it.

In Computer Vision, you do the exact opposite. You start with the 2D image (or video feed) and try to work backwards to reconstruct the data—the 3D model, the object identity, or the spatial coordinates. This process is often called inverse rendering.

Because you are starting with fewer dimensions (a flat 2D image) and trying to recover a complex 3D reality, this is known as an ill-posed problem. A single 2D image of a circle could be a sphere, a flat disk, or a cylinder viewed head-on. The ambiguity is what makes Computer Vision such a challenging and fascinating field to solve.

Note

The “Inverse Problem”: In math, forward problems (Graphics) are usually deterministic—if you know the model, you know the image. Inverse problems (Vision) are often ambiguous. A single 2D image could represent a tiny object close up or a huge object far away. This ambiguity is what makes Computer Vision so difficult to solve.

Key Differences at a Glance

Feature	Computer Vision	Computer Graphics
Input	Real-world images/video	Models, math, physics rules
Output	Data, understanding, models	Visuals, images, video
Goal	To understand the world	To simulate the world
Key Math	Statistics, Optimization, Linear Algebra	Geometry, Optics, Physics

Real-World Applications

Computer Vision has moved from research labs to our daily lives. Here are the major areas where it’s transforming industries:

Autonomous Systems

Self-driving cars use SLAM (Simultaneous Localization and Mapping) to build 3D maps of the road in real-time, detecting lanes, signs, and pedestrians.

Healthcare

AI systems analyze medical imaging (MRIs, CT scans) to detect anomalies like tumors or fractures, often with higher accuracy than human review.

Manufacturing

Optical Inspection systems watch assembly lines 24/7, spotting microscopic defects in circuit boards or sorting produce by quality.

Security & Identity

Facial recognition unlocks phones and secures buildings, while gait analysis can identify suspicious behavior in surveillance feeds.

Augmented Reality (AR)

AR is the perfect marriage of Vision and Graphics. Vision tracks the real world (to know where the table is), and Graphics renders a virtual object on top of it.

Agriculture

Drones fly over fields using multispectral cameras to monitor crop health, water levels, and pest infestations.

Where OpenCV Fits In

OpenCV (Open Source Computer Vision Library) is the foundation for most of these applications.

While modern “Deep Learning” (like PyTorch or TensorFlow) handles the cognitive tasks (like identifying who is in a photo), OpenCV handles the essential infrastructure and geometry:

1. Image Processing: Noise reduction, color correction, and format conversion.
2. Geometric Transformations: Rotating, resizing, and correcting perspective distortion.
3. Feature Detection: Finding corners, edges, and key points to track objects.
4. Real-time Efficiency: Optimizing code to run on embedded devices like Raspberry Pis or drones.

Academic Resources

If you want to go deeper into the theory, these are the standard textbooks used in university Computer Vision courses:

• “Computer Vision: Algorithms and Applications” by Richard Szeliski. (The “Bible” of classical Computer Vision).
• “Multiple View Geometry in Computer Vision” by Hartley and Zisserman. (Essential for 3D reconstruction and geometry).
• “Deep Learning” by Goodfellow, Bengio, and Courville. (For the modern AI approach).

Next Steps

Now that you see the big picture, let’s look at the atoms of that picture: the pixels.

1. Digital Images - deeper dive into the matrix structure of images.
2. Install OpenCV - Get your environment ready to code.