What is Computer Vision?

What is Computer Vision? AI’s Eyes Explained

Computer vision’s how AI sees—turning pixels into meaning. It’s behind self-driving cars, face unlocks, even OCR on your scanner. Welcome to Decoding Complexities. This post digs into what computer vision is, how it works with neural networks, and why it matters. 

No fluff—let’s break down the tech and see it in action.

Computer Vision Basics

Computer vision’s a chunk of AI that processes images or video—think eyes for machines. It’s not just snapping pics; it’s understanding them. Pixels go in—numbers representing color or brightness—and AI spits out labels like “cat” or “stop sign.”

Key tasks it handles:

  • Image classification—tag a pic as “dog” or “car.”
  • Object detection—spot and box multiple things in one frame.
  • Segmentation—outline exact shapes, pixel by pixel.

It’s deep learning’s playground—neural networks crunch the data. Let’s see how.

How It Works: The Tech

Computer vision leans on convolutional neural networks—CNNs—a twist on the neural nets from my last post. They’re built to handle images. Here’s the rundown.

Step 1: Image to Numbers

Start with raw pixels—say, a 224x224 RGB image. That’s 150,528 numbers (224 * 224 * 3 channels). Normalize them to 0-1 so the math doesn’t choke.

Step 2: CNN Layers

CNNs use convolution—sliding filters over the image to spot edges or textures. Early layers catch lines, later ones nab shapes or faces. Pooling shrinks the data—keeps it manageable. Stack 20-50 layers, and you’ve got deep learning doing the heavy lifting.

  • Filter example: 3x3 grid slides, multiplies, sums—edge detected.
  • Tech bit: ReLU activation zeros negatives—keeps focus.

Step 3: Training and Output

Feed it labeled data—millions of pics with “cat” or “no cat.” Gradient descent tweaks weights till errors drop. Output’s a probability—like 95% “cat.” Test it on new images, and it’s ready.

Flow’s like this:

Pixels → CNN (Convolution + Pooling) → Train → Labels

Real-World Example

Take license plate recognition—handy for IBM i shops with legacy apps. Camera snaps a plate—say, “ABC123.” CNN breaks it down:

  • Pixels: 100x50 grayscale image = 5000 inputs.
  • Layers: Edges → letters → “A,” “B,” “C,” “1,” “2,” “3.”
  • Output: Text string “ABC123” to your RPG program.

Could pipe it to a subfile—think Load All from my last post. Fast, practical, IBM i-friendly.

Why It Matters

Computer vision’s why AI’s exploding. Self-driving cars dodge obstacles—CNNs spot lanes. Medical scans flag tumors—better than some eyes. Your phone’s face unlock? That’s vision at work.

Catch is, it’s hungry—needs big data and GPU power. Small datasets or weak hardware? It flops. Still, it’s a coder’s goldmine—IBM i or not.

Wrapping Up

Computer vision’s AI’s eyes—CNNs turn pixels into smarts. From plates to faces, it’s deep learning in action. More in my Short “What is Computer Vision” or post “What Are Neural Networks?”—links below. Got a vision use case? Comment—let’s decode more AI.

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