Computer vision isn’t just another AI-related buzzword – it’s fast becoming a vital part of different industries and our daily lives. Advances in computer vision are transforming automation and data analytics across industries from retail to telecommunications, utilities, military, and healthcare.
Convolutional Neural Networks (CNNs), a class of deep neural networks commonly applied to analysing visual imagery, continue to drive advances in image understanding and analytics, setting a new benchmark for visual processing.
In our latest article, we’ll look at some of the most exciting new trends in the advancement of computer vision in 2024, their applications and impact.
Computer vision (or machine vision) is a branch of Artificial Intelligence (AI) and computer science that focuses on allowing computers to interpret and understand visual information from visual data. This could be images, videos, or metadata. The process includes developing algorithms and techniques that help machines replicate human vision like object recognition or scene understanding.
By analysing visual data, computer vision can identify objects, detect patterns, and extract meaningful information. This enables computer vision technology to perform tasks that traditionally rely on human vision, thus eliminating human error, enhancing work efficiency and optimising workflows.
However, reaching high levels of accuracy and quality requires computer vision models to be meticulously trained by data scientists with high-quality data. If you’d like to learn more, we have an in-depth article that covers all things computer vision.
Did you know that annotations have become pivotal in refining computer vision algorithms, driving a projected 26.6% CAGR (compound annual growth rate) in the data annotation market from now until 2024? And that's just the beginning.
Technological advancements in hardware, software, and algorithms, particularly in deep learning, have also fueled progress in image analysis. In the past few years, digital cameras and smartphones have provided a wealth of visual data for training models, enabling tasks like object detection and image classification.
Computer vision solutions are driving cost reduction and streamlining operations by automating manual processes and improving efficiency. Further, as businesses leverage AI and automation to gain a competitive advantage, the demand for computer vision technologies continues to grow further accelerating their integration with emerging technologies such as augmented reality and the Internet of Things (IoT).
Whether you’re actively researching the use of AI and its development or you’re just casually reading up on the latest trends, chances are you’ve heard of generative AI. It’s another field of AI, that’s capable of generating new, original content like images, text, or even music, based on patterns learned from existing data.
Generative AI algorithms use generative adversarial networks (GANs) or variational autoencoders (VAEs) to create realistic and diverse outputs, often with applications in creative fields, data augmentation or generating synthetic data for training other AI models. Let’s explore these two a bit deeper.
By following the patterns in their training sets, these algorithms allow computer vision to generate data that mirrors existing characteristics, enhancing the diversity of training data and augmenting datasets.
A wide range of industries benefit from generative AI, including marketing content creation, manufacturing quality assurance, and digital asset management. But beyond GAN and VAE algorithms, there are also:
However, generative AI has its challenges. Potential inaccuracies, embedded bias, and privacy and information security risks are some of the key hurdles today. Moreover, the use of AI-generated data for further AI training could spiral into a feedback loop, complicating the learning process.
Yet the popularity and usability of generative AI can’t be ignored and we’re sure to see some interesting developments soon.
Initiated by Andrew Ng, a professor at Stanford University's Departments of Computer Science and Electrical Engineering, data-centric AI is a response to the disproportionate emphasis on model research, which currently accounts for approximately 99% of AI exploration. A new focus is needed, posits Ng, acknowledging that AI algorithms depend on data as much as they do on algorithms.
Data-centric AI focuses on optimising the entire lifecycle of data, from collection and preparation to analysis and deployment, to improve the performance and reliability of AI models. It emphasises the importance of high-quality, diverse, and relevant data. It also focuses on efficient data management practices to ensure that AI systems are robust, accurate, and ethical in their decision-making processes.
Implementing data-centric AI effectively hinges on the maturity of MLOps (machine learning operations) tools and the capacity to create custom solutions tailored to the needs of different industries. In practice, this means deploying advanced data optimisation techniques. Examples would be wireless communication, LiDAR (Light Detection and Ranging) technology, healthcare, and automotive engineering.
Finally, data-centric AI encourages greater collaboration among multidisciplinary teams. Continuing research and development in this area is essential for a greater impact on society since computer vision technology progresses largely based on the quality and integrity of data.
Merged reality combines elements of both augmented reality (AR) and virtual reality (VR). This enables digital content to interact with the real world in real-time. Incorporating virtual objects and environments seamlessly into the physical world allows users to experience the best of both worlds.
But what about examples? You might be surprised that virtual reality isn’t a new concept. In fact, the first VR headset was created in the 70s. But it wasn’t until companies like Oculus launched high-quality VR headsets that it really appealed to the global gamer market.
Today, the market is growing. It’s projected that the AR and VR market size will reach USD 71.2 billion by 2028 at a CAGR of 23.2%. And we’re talking about more than just VR headsets – we’re looking at a whole new development of smartphones, AR glasses, and more. Here are some areas where AR and VR are expected to grow the most:
Facial recognition technology is growing relentlessly – it’s already estimated that the global market will reach USD 13.4 billion by 2028 at a CAGR of 16.3%. This technology leverages AI algorithms to identify or verify individuals based on their facial features. It’s versatile, capable of recognising faces across different ethnicities, genders, and ages, and has become an essential tool in various sectors, including security, finance, and healthcare. Here are some examples:In retail and eCommerce, companies are incorporating facial recognition for a more secure and user-friendly payment system, enhancing the customer checkout experience.
Government and finance sectors are leveraging this technology for digital access and bolstering cybersecurity measures, demonstrating its versatility beyond consumer applications.
Masked-face recognition was born out of the necessity to adapt to the challenges posed by global health events. Facial recognition technology now includes the ability to accurately identify individuals even when masks cover part of their faces.
Retail and payment integration can potentially offer the opportunity to use facial recognition for payments, personalised shopping experiences and driver monitoring systems. Despite these advancements, facial recognition technology faces challenges, including concerns over privacy, equity, and civil liberties. The U.S. National Academies of Sciences, Engineering, and Medicine have urged the White House to establish guidelines for the use of facial recognition technology until more defined laws are in place. This recommendation emphasises the need for a balance between technological innovation and ethical standards.
In the Asia-Pacific region, countries like China and India are leading the charge in facial recognition growth, while Europe's General Data Protection Regulation (GDPR) provides a stringent framework for its use. As this technology continues to evolve, hybrid solutions that combine facial recognition with other biometric measurements are being explored to enhance both accuracy and security.
3D model accuracy in computer vision technology has taken a quantum leap forward, thanks to the integration of AI with computer graphics. This fusion, known as AI 3D modelling, is crucial in the automatic generation of high-quality 3D models. The technology combines machine learning and deep learning algorithms to process a variety of perceptual data like images and videos.
Pix2pix3D models and Imagine 3D are breaking new ground. They help create 3D object representations from simple hand-drawn labels or textual prompts. This development can have a significant impact on the retail sector by allowing quick and customisable product visualisations.
Photoneo's groundbreaking Parallel Structured Light technology, as implemented in their MotionCam-3D camera, has set a new standard for capturing high-resolution, high-accuracy 3D images of moving scenes without the distortion typically caused by motion.
Furthermore, deep learning techniques, particularly convolutional neural networks (CNNs) and generative adversarial networks (GANs), have significantly improved the accuracy and robustness of 3D reconstruction, contributing to the growth of the 3D modelling industry.
Advancements in automation and data analysis are reshaping the landscape of multiple sectors. By embracing and integrating these innovations, industries can utilise their potential to drive growth, efficiency, and innovation on an unprecedented scale.
Looking to the future, it's clear that the implications of technological strides in computer vision extend far beyond its current applications. In light of these trends, computer vision technology is on its way to driving continuous, collaborative efforts to address ethical concerns and unlock amazing technological possibilities.
Let’s explore the possibilities.
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