How Does Computer Vision Work?

Ever wonder how your phone knows it’s your face or how a factory robot spots a defect in a product in milliseconds? That’s computer vision at work. Behind the buzzword is a fascinating blend of math, data, and mimicry of human sight.

In this post, we’ll break down how does computer vision work and the core concepts behind it in a clear, simple way. If you’re exploring AI solutions for your business, understanding the basics can help you make informed decisions.

What is computer vision?

Computer vision is a field of artificial intelligence that teaches computers how to “see” and make sense of visual information, like photos, videos, or live camera feeds. 

But unlike humans, computers don’t instinctively know what they’re looking at. When we see a stop sign or a smiling face, we understand it instantly because we’ve spent our entire lives building context and experience. 

Computers don’t have that. They need detailed instructions, training data, and algorithms to interpret visuals in a meaningful way.

At its core, computer vision is about closing that gap. It involves taking raw image data (pixels) and turning it into something the computer can understand and act on. This could mean identifying objects, tracking movements, reading text from an image, or even spotting a defect on a product line.

If you’re new to the world of AI or want a broader understanding of how it all fits together, check out our article on What is Artificial Intelligence.

The core process of computer vision

Computer vision operates through 5 key steps that transform raw images into meaningful insights. 

Step 1: Image acquisition

Every computer vision task starts with one basic requirement: an image or video to work with. That’s where image acquisition comes in. 

At this step, the computer captures visual data using devices like digital cameras, webcams, smartphones, drones, scanners, or specialised sensors (like thermal or depth cameras). In industrial settings, high-speed cameras on assembly lines or satellite imagery in agriculture are common examples.

The type of hardware used depends on the task. A self-driving car, for instance, uses multiple cameras and LiDAR sensors to get a full view of its surroundings. Meanwhile, a retail app scanning receipts might just rely on a smartphone camera.

File format and input considerations

Once the image or video is captured, it’s typically saved in standard formats like JPEG, PNG, BMP, or for video, MP4 or AVI. These formats balance file size and quality, but they also affect how easily the data can be processed. 

For example, raw image files contain much more detail but are larger and slower to process. On the other hand, compressed formats like JPEG are more efficient but may lose some image clarity.

At this stage, it’s not about understanding what’s in the image, it’s just about getting the best possible version of it into the system. Lighting conditions, resolution, camera angle, and file size all play a role in how well the computer vision system will perform later on.

Step 2: Image preprocessing

Once an image is captured, it usually isn’t ready to be analysed right away. Raw images can be noisy, blurry, poorly lit, or just inconsistent in size or colour. That’s where image preprocessing comes in. This step cleans up and standardises the image so the computer vision model can make sense of it.

Techniques to clean and normalise input

Preprocessing can involve a variety of techniques, depending on what the image needs. Some of the most common include:

• Resizing: Not all images come in the same dimensions. Resizing ensures they’re uniform, which is crucial for feeding them into machine learning models that expect consistent input.
• Grayscale conversion: Sometimes, colour doesn’t matter, especially for tasks like edge detection or object shapes. Converting to grayscale simplifies the data and speeds up processing.
• Noise reduction: Random pixel variations (noise) can mess with accuracy. Filters like Gaussian blur help smooth out the image without losing important details.
• Normalisation: This adjusts pixel values to a standard range (like 0 to 1), which helps models train and perform more consistently.
• Contrast and brightness adjustments: Poor lighting can hide useful features. Adjusting these settings brings out the important parts of the image.
• Thresholding and binarisation: For certain tasks, simplifying an image into black and white (e.g., text detection) makes interpretation easier.

Step 3: Feature extraction

After the image has been cleaned up during preprocessing, the next step is to figure out what matters in the image. This is called feature extraction. It’s the process of picking out the important visual cues, like edges, corners, textures, shapes, or specific patterns, and translating them into numbers the computer can understand.

Why features? Because raw pixel data on its own doesn’t tell a machine much. For example, a picture of a cat and a dog might both just look like a bunch of colored dots. But when the system identifies features, like the curve of an ear, the shape of eyes, or the texture of fur, it starts to build a more meaningful picture.

All of these features are translated into numerical vectors, which are just structured sets of numbers that describe what’s in the image. These vectors become the building blocks for the next steps, whether that’s object recognition, classification, or something more advanced.

Step 4: Understanding and interpreting the image

This is the point where the computer vision system shifts from simply seeing the image to understanding it.

At the heart of this step are convolutional neural networks (CNNs), which are specially designed to handle visual data. These models use small matrices called kernels or filters that slide across the image to examine small sections of pixels at a time. This process is known as convolution. Here’s how it works in simple terms:

Imagine placing a small square window over a portion of the image and multiplying each pixel value under that window with the corresponding value in the kernel. You then add up the results, and that gives you a single number. 

Slide the kernel to the next section of the image, repeat the operation, and continue until you’ve covered the entire image. This helps the system detect specific features, like a horizontal edge, a corner, or a curve, based on patterns in the pixel values.

But it doesn’t stop at one filter or one pass. CNNs use multiple layers of convolutions, each with different filters. Early layers might detect simple features like edges. Deeper layers combine these into more complex patterns, like a face, a vehicle, or a handwritten number.

Behind the scenes, the system is doing complex math, working with pixel matrices, calculating weighted sums, applying activation functions, and passing data forward through layers. But the outcome is surprisingly practical: the machine starts recognising visual patterns in a way that becomes increasingly abstract and intelligent.

Step 5: Analysis and decision making

Once the system has processed the image and understood the patterns within it, the final step is analysis and decision-making. 

At this stage, the system doesn’t just see that there’s a shape resembling a human; it can now decide whether that person is wearing a helmet, crossing a street, or waving their hand. 

It takes the patterns and features it’s learned and compares them against known categories, behaviours, or thresholds to make sense of what’s happening.

Depending on the application, this step could involve:

• Classification: Determining what’s in the image (e.g., “This is a cat” vs. “This is a dog”).
• Object detection: Identifying and labelling multiple items in an image, along with their locations (e.g., detecting all cars and pedestrians in a street scene).
• Anomaly detection: Spotting something unusual, like a defect in a manufactured part or an irregularity in a medical scan.
• Tracking: Following the movement of objects across video frames—useful for surveillance, sports analytics, or autonomous vehicles.
• Triggering actions: If certain conditions are met, the system might automatically take an action, like unlocking a phone when your face is detected, or stopping a robot arm when a person steps into its path.

Deep learning: Key technology behind computer vision

Deep learning is the engine driving most of today’s computer vision breakthroughs. As a more advanced form of machine learning, it enables systems to learn directly from data, especially visual data, without needing every rule to be programmed manually.

Deep learning relies on structures called neural networks, which are made up of many layers of artificial “neurons.” These neurons pass information forward through mathematical operations, adjusting weights and connections along the way. This layered approach allows the system to gradually learn patterns, starting from simple features and building toward more complex insights.

From manual feature engineering to deep feature learning

In traditional computer vision systems, developers had to manually define which image features to look for, edges, corners, textures, etc. This was time-consuming and limited by human assumptions.

With deep learning, we no longer need to handcraft those features. The network learns to discover the most relevant visual patterns during training. This is known as deep feature learning, and it allows the model to adapt to complex, real-world data with far less manual setup.

Key neural network architectures

Within deep learning, not all neural networks are built the same, especially when it comes to computer vision. Two of the most important types are the following:

Convolutional Neural Networks (CNNs) 

CNNs are designed specifically for processing pixel-based data like images. They work by sliding small filters (called kernels) across the image in a process known as convolution. This allows the network to identify patterns, first low-level ones like edges and shapes, then higher-level details like textures and object parts.

CNNs use labeled data during training to refine their predictions. As the layers go deeper, the model builds a more detailed understanding of the image. CNNs are widely used in tasks like image classification, facial recognition, medical imaging, and object detection.

Recurrent Neural Networks (RNNs)

While CNNs work best with single images, RNNs are better at handling sequences of data, like video. They can remember information from previous frames, allowing them to detect motion, understand behavior, or track objects across time. This makes RNNs ideal for video analysis, activity recognition, and real-time decision-making.

The value of custom-built computer vision solutions

While off-the-shelf computer vision tools exist, they’re often too generic or limited to meet the unique needs of most businesses. That’s where a custom software development company like GoodCore comes in.

A custom solution starts with understanding your specific goals. Every use case is different, and the right solution needs to be tailored accordingly. Here’s how our AI experts can help:

• End-to-end expertise: From choosing the right hardware (cameras, sensors, etc.) to designing the complete software architecture, we handle the full computer vision pipeline.
• Custom model training: Off-the-shelf models often miss the mark when it comes to industry-specific needs. We train deep learning models on your own data, improving accuracy and reliability.
• Integration with existing systems: Your computer vision solution shouldn’t live in isolation. We build systems that integrate smoothly with your current tools, ensuring streamlined workflows and efficient data sharing.
• Ongoing support and optimisation: We don’t just hand off the software and disappear; we provide continuous support, monitor performance, and fine-tune the system as new data comes in.