Computer Vision definition: Working, importance and challenges

Computer Vision is a field of Artificial Intelligence (AI) which uses Machine Learning and neural networks to enable computers to interpret visual information from images and videos. The process involves acquiring, analysing‌ and understanding digital images or videos to replicate human vision capabilities.

Computer Vision is used to automate tasks that need visual perception — enhancing accuracy and efficiency across various industries. Let’s delve in deeper to understand its uses better…

• Automated inspection - in manufacturing, Computer Vision can inspect products on assembly lines to identify defects or deviations from expected standards.

• Healthcare - in the medical field, Computer Vision works in imaging to help diagnose diseases, track the progression of conditions‌ and plan treatment by analysing scans.

• Automotive - self-driving cars use Computer Vision to detect and interpret their surroundings, including other vehicles, pedestrians‌ and road signs, to navigate safely.

• Surveillance - Computer Vision helps detect unusual activities, tracking or recognising faces and objects, improving security and monitoring.

• Agriculture - in farming, Computer Vision is used to monitor and analyse crop health by automating processes such as observing field conditions, detecting pest attacks, inspecting soil moisture‌ and forecasting weather.

Computer Vision works by processing and interpreting digital images and videos to understand and make decisions. Here’s a simplified breakdown of how it works…

Image acquisition

The first step in Computer Vision is acquiring an image or video. This can be done through various devices like cameras, smartphones‌ or other specialised imaging equipment.

Pre-processing

Once an image is captured, it’s processed to improve quality and reduce noise. This might involve adjusting brightness and contrast, cropping‌ or resizing to make subsequent analysis more efficient and accurate.

Feature detection

The system identifies important features or patterns in the image, including edges, corners‌ or specific shapes using Machine Learning techniques. For example, in facial recognition, features like eyes, nose‌ and mouth are identified.

Object classification

After recognising features and patterns, the system classifies them into categories. For example, in a traffic sign recognition system, signs are classified into 'stop' or 'speed limit'.

Decision-making

Based on the classification, the system can now make decisions with the help of Deep Learning, a subset of Machine Learning.

Deep Learning uses neural networks with many layers to process input data and decide a course of action. For example, in an autonomous vehicle, if the Computer Vision system recognises a pedestrian crossing the road, it decides to stop the vehicle.

Post-processing

The output from the Computer Vision system might sometimes undergo further processing to enhance the results.

Here are some of the common tasks performed by Computer Vision systems, along with examples 👇

Image classification

This involves categorising images into predefined classes. For example, a Computer Vision system can classify images of animals, identifying whether an image contains a cat, dog, bird, etc.

Object detection

Here, computers identify and locate objects within an image or video by indicating their location and drawing a bounding box around each object. For example, on a street, a Computer Vision system can detect cars, pedestrians‌ and traffic signs.

Segmentation

Segmentation assigns a label to every pixel in an image for a deeper analysis.

For example, in medical imaging, computer vision can segment an MRI scan to identify and isolate different tissues, organs‌ or anomalies..

Face recognition

This involves identifying or verifying a person from a digital image or a video frame. It’s most commonly applied in security systems that unlock phones or doors based on facial recognition.

Motion analysis

This includes tasks like tracking the movement of objects in video. For example, in sports, motion analysis is used to track players and the ball for decision making by the referee.

Computer Vision, despite its advancements and widespread applications, faces several challenges that researchers and practitioners continue to address…

Variations in visual data

One of the biggest challenges is variations in the appearance of objects due to changes in lighting, perspective, occlusion and environmental conditions. This makes it difficult for models to consistently recognise objects.

High-quality data requirement

Training Computer Vision models requires large amounts of high-quality, annotated data. Collecting and labelling this data can be time-consuming and expensive.

Real-time processing

Many applications, such as autonomous driving and real-time surveillance, need Computer Vision systems to process and interpret visual data in real-time. Achieving high-speed processing without sacrificing accuracy can be challenging.

Ethical and privacy concerns

Using Computer Vision in areas like surveillance and personal data analysis raises significant ethical and privacy issues. Therefore, it's important to make sure these AI systems are used ethically.

Integration with other systems

Integrating Computer Vision with other systems and technologies can be complex, especially when dealing with those not originally designed to incorporate AI.