How Does Computer Vision Work in AI?
Computer Vision is a subfield of Artificial Technology (AI) that enables computers to interpret, process, and analyze visual data like images and videos from the real world. By translating these inputs, Computer Vision models can construct data into usable outputs applicable to different software features.
Computer Vision plays a crucial role in many of today’s most advanced technologies such as self-driving cars and image editing programs that are now deploying generative background fillers. With constant innovation, computer vision is beginning to change how humans see the world, but how does computer vision work?
Understanding Computer Vision
In technical terms, computer vision is a form of AI that interprets its visual world by automating the human visual system. This gives machines the ability to analyze, interpret, and even extract data from static images or moving pictures.
Computer vision can be an effective tool for many reasons in our digital world:
Efficiency: Machines can process large amounts of visual data without the same fatigue that humans experience. This can be beneficial in environments that are not optimal for humans.
Safety: There are many ways that Computer Vision can help make conditions safer for people like when the tech is integrated into self-driving cars, helping to make roads safer.
Security: Facial recognition helps improve safety features on devices like smartphones to security deposit boxes. Applications can also be installed in security surveillance equipment for guards to monitor.
Data Analysis: Computer vision can be applied to businesses to help analyze visual data to help improve the flow of customer traffic by analyzing the visual layouts of stores or parking lots.
Accessibility: Computer Vision is helping the disabled and handicapped to perceive the world, helping to support object detection and image recognition for the visually impaired.
Role of Computer Vision in AI
Humans are largely visual creatures which gives Computer Vision extensive potential for application in aiding us. With its powerful ability to interpret visual data, machines are slowly beginning to see the world the same way that we do:
Pattern Recognition: Identifying and understanding patterns in image/video sequences is a fundamental aspect of many computer vision features like facial recognition, object detection, and image classification.
Feature Extraction: Extraction can help AI models distinguish between different objects within an image such as differing between a dog or cat. This can be seen in most camera filters on social media apps that track the face and overlay it with imagery.
Image Analysis: Images can be leveraged by AI to analyze small details that are more difficult for humans to detect. This can be especially helpful in industries like Healthcare where the difference can be life and death.
Real-Time Processing: Computer vision can also help interpret real-time data such as traffic, surveillance, and self-automation. This can help with adjustments in driving, traffic flow, and finding safer routes to travel.
Computer Vision has a vast range of applications that it can be integrated with, here are a few examples:
Social Media: Online socializing has exploded over the past two decades and is becoming increasingly populated with images and videos across all platforms. With computer vision, users are able to tag photos, filter content, and view more accurate recommended content.
Wildlife Conservation: Computer Vision can help preserve wildlife and endangered ecosystems by monitoring trap-camera images or tracking animal movement patterns to keep endangered animals safe from poachers and deforestation.
Robotics: Advancements in robotics are taking huge leaps with innovations in Computer Vision, giving autonomous robots more ability to interpret their environment for traveling and manufacturing.
Sports Analytics: Sports are one unique industry that Computer Vision can help improve, giving players a more immersive understanding of strategies and film analysis from previous games to help achieve better future performance.
How Computer Vision Works in AI
The process for Computer Vision can be broken down into five steps:
Image acquisition: The process begins with the AI model being fed an image or video. This can be done using a camera, scanner, or file import. The quality of the fed data can have a major influence on the results.
Image processing: Once data is acquired, the model then pushes the image or video through pre-processing to improve quality for further analysis. This includes adjusting different aspects of the images including sharpness, contrast, and grayscale.
Feature extraction: The model will identify important features within an image, tagging them for reference. This can include edges, color patches, or specific shapes.
Machine learning algorithms: Machine Learning is used to process data extracted from the image/video, using image tags to help identify the contents of an image.
Interpretation and decision-making: After all the data has been analyzed, the AI model will use what it’s learned about the information to make a decision or prediction.
Deep Learning and Computer Vision
Deep Learning plays a vital role in the development of Computer Vision, helping it to process data and self-evaluate results upon completion. Due to the complex nature of Computer Vision, Deep Learning’s large network of neurons helps it to process information effectively.
One of the most common examples of deep learning in Computer Vision is through Convolutional Neural Networks which are models that use an extensive web of processing nodes to identify grid-like data.
Challenges of Computer Vision in AI
Despite its many innovations and advancements, there are still a large number of challenges ahead of Computer Vision that need to be solved:
Lack of annotated data: Annotated and Labeled data is still required for supervised learning models which require time-consuming human oversight. While unsupervised models can avoid this issue, they are highly complex systems that require more resources.
Variations in real-world data: Data from the real world is constantly changing and unpredictable, unlike more controlled environments that are used during training.
Computational requirements: As deep learning models become more advanced, the need for additional resources in electricity, computing power, and human intelligence only gets higher.
Interpretability: Deep Learning models have a “black box” feature that makes it difficult for researchers to analyze, requiring Explainable AI to tell programmers how an AI model made its decision.
Ethical Concerns: Privacy is a major challenge for Computer Vision with AI surveillance cameras having the ability to track people using facial recognition without their knowledge or the misuse of AI to create deep fakes.
The Future of Computer Vision in AI
Despite its technical and ethical challenges, the advent of Computer Vision is causing major innovation in nearly all facets of digital technology from simple camera filters to self-driving cars that can observe the road better than humans.
With further improvements, we can soon see new integration into other sectors including transportation including public transport systems or more automotive machinery in manufacturing that can improve safety for workers by identifying potential hazards.
By giving AI the power of vision, the possibilities for new applications in our digital world are nearly endless in both real and virtual environments. Who knows what will come next?
