Computer vision is a subset of artificial intelligence that focuses on enabling computers and systems to interpret and understand visual data from the world, such as images and videos. Just as humans use their eyes and brain to make sense of their surroundings, computer vision aims to replicate this process using sensors, data processing, and algorithms.
The ultimate goal is for machines to be able to extract meaningful information from visual inputs and make informed decisions or take relevant actions based on that information. While human vision has had years of experience to train it to distinguish objects, determine distances, and notice movement or anomalies, computer vision strives to accomplish these tasks in a shorter amount of time using cameras, data, and computational power.
This technology holds great promise across various industries, including manufacturing, healthcare, security, and entertainment, among others.
Why Computer Vision Is Important?
Visual information processing technology has existed for a long time, but it historically required extensive human intervention, was time-consuming, and prone to errors. For instance, early facial recognition systems needed developers to manually label thousands of images with key data points, such as the width of the nose bridge and the distance between the eyes. Automating these tasks was challenging due to the unstructured nature of image data, demanding significant computing power, and making vision applications costly and out of reach for many organizations.
Today, advancements in the field and a significant increase in computing power have enhanced both the scale and accuracy of image data processing. Modern computer vision systems leveraging cloud computing resources are now accessible to all organizations. This technology can be used for various applications, including identity verification, content moderation, streaming video analysis, and error detection.
How Computer Vision Works?
Computer vision works in three basic steps:
- Acquiring an image : Images, even large sets, can be acquired in real-time through video, photos or 3D technology for analysis.
- Processing the image: Deep learning models automate much of this process, but the models are often trained by first being fed thousands of labelled or pre-identified images.
- Understanding the image: The final step is the interpretative step, where an object is identified or classified.
Today’s AI systems can go a step further and take actions based on an understanding of the image. There are many types of computer vision that are used in different ways:
- Image segmentation partitions an image into multiple regions or pieces to be examined separately.
- Object detection identifies a specific object in an image. Advanced object detection recognises many objects in a single image: a football field, an offensive player, a defensive player, a ball and so on. These models use an X,Y coordinate to create a bounding box and identify everything inside the box.
- Facial recognition is an advanced type of object detection that not only recognises a human face in an image, but identifies a specific individual.
- Edge detection is a technique used to identify the outside edge of an object or landscape to better identify what is in the image.
- Pattern detection is a process of recognising repeated shapes, colours and other visual indicators in images.
- Image classification groups images into different categories.
- Feature matching is a type of pattern detection that matches similarities in images to help classify them.
Simple applications of computer vision may only use one of these techniques, but more advanced uses, like computer vision for self-driving cars, rely on multiple techniques to accomplish their goal.
What Are Uses Cases Of Computer Vision?
Computer vision has various applications across different industries. Some of these applications include:
- Predictive Maintenance: AI detects machine anomalies by analyzing camera data, alerting operators to take preventative measures.
- Agriculture: AI models assist in crop monitoring, automated harvesting, weather analytics, animal health, and plant disease diagnosis.
- Transportation and Mobility: Self-driving cars rely on AI to interpret images and create 3D maps for navigation. AI also monitors driver behavior to detect distraction, fatigue, or drowsiness in semi-autonomous vehicles.
- Education: AI enables advanced student assessment by detecting eye movement and body language during online exams. It also automates administrative tasks like resource management and attendance recording.
- Facial Recognition: Used in surveillance, facial recognition matches face images against databases. Despite privacy concerns, it’s seen as a tool for crime prevention and detection.
- Manufacturing: AI improves production line inspections by identifying defects and inconsistencies. It also optimizes warehouse management and inventory handling.
- Retail: Businesses use AI for store layout optimization, shelf stocking, and shopper engagement. Cashierless stores employ AI for seamless shopping and theft reduction.
- Healthcare: Medical professionals use AI for diagnostics and patient care, analyzing medical images for early detection and precise treatments in pathology, radiology, ophthalmology, and dermatology.
Conclusion
Computer vision is revolutionizing the way we process and understand visual data, making it more efficient and accurate than ever before. With technological advancements and increases in computing power, computer vision applications are now widely accessible, allowing organizations to leverage this technology for tasks such as identity verification, content moderation and fraud detection.
At AInexxo, we harness the power of computer vision for document layout analysis. We train a neural network to “see” and recognize the core structure of PDFs, allowing us to accurately assign metadata to document content and simplify information management and retrieval. Our approach involves identifying both geometric roles, such as text and tables, and logical roles, such as titles and headings, to build better semantic.