The earliest applications of computer vision were mostly devoted to creating algorithms that could identify plane shapes and edges in 2D pictures. This began in the 1960s when Larry Roberts described the process of deriving 3D information about solid objects from 2D photographs. Later in 1979, Dr. Kunihiko Fukushima proposed Neocognitron – a revolutionary hierarchical multilayered neural network that was capable of robust visual pattern recognition that could detect basic shapes in an image.

This led to the development of an entirely new field of AI – Computer Vision.

It was, however, not until the 2000s when computer vision technology advanced rapidly. The point of rapid development in computer vision was in the year 2001 when the first real-time face recognition video application was developed. This was achieved using the Viola-Jones object detection framework for faces, which was later launched as an application called AdaBoost – the first real-time frontal-view face detector.

Today, Computer Vision powered apps are used in wide range of applications. Content organisation, facial recognition, automatic checkout, spotting defects in manufacturing, detecting early signs of plant disease in agriculture, and many more.

The interest in Computer Vision is growing every year, and it is projected to be around 24 Billion when in 2027. Source: verifiedmarketresearch.com

The range of practical applications for computer vision technology makes it a central piece of innovation for many technology-based enterprises. However, amidst the growing numbers and growing interest, the rate of growth in this field hasn’t shown much positive results.

Computer vision, though rapidly evolving, is facing several challenges that make it harder to implement and pose a barrier to continual growth and success. Here are some of the challenges you might have to face in computer vision:

• Inadequate hardware: Implementing computer vision requires powerful hardware. These include components such as processors, memory chips, graphics cards and cameras. These components affect the performance, accuracy, and efficiency of computer vision algorithms. Thus, inadequate hardware can limit the amount of data that can be processed, the complexity of the models that can be trained, and the speed of the inference that can be done.
• Poor data quality: Collecting relevant and sufficient data can be challenging, and poor data quality can lead to a lack of training data for computer vision systems. Poor data quality can result from various factors, such as noise, blur, occlusion, distortion, illumination, or annotation errors. These factors can affect the performance and accuracy of computer vision models, leading to undesirable outcomes or failures.
• Weak planning for model development: Developing computer vision models requires careful planning, and weak planning can lead to poor performance and inaccurate results. This means, that having a lack of clear objectives, requirements, and evaluation criteria could affect the computer vision models negatively – leading to poor performance, inefficiency, and wasted resources. To overcome this challenge, computer vision practitioners need to follow a systematic and rigorous process of problem definition, data collection, model selection, training, testing, and deployment.
• Lack of annotated data: Gathering labelled data (or data in general) for computer vision systems can be challenging. Annotated data is data that has been labelled with some information that can help a computer vision model learn from it. However, annotating data is a time-consuming and expensive process that requires human experts or crowdsourcing platforms, and a lack of annotated data can lead to poor performance and inaccurate results.
• Lack of experienced professionals: Humans are the weakest link in the security chain, but the necessary component in any architecture. The complexity and diversity of computer vision problems requires a combination of mathematical, algorithmic and domain-specific knowledge. Moreover, there has been a rapid pace of innovation and research in computer vision, which makes it hard for enterprises to keep up with the latest developments and best practices. Developing and implementing computer vision systems, thus, requires skilled professionals – a lack of which can have serious consequences for businesses if overlooked.

However, this field of Computer vision is evolving rapidly.

On the infrastructure front, faster, cheaper, and more efficient edge computing storage has given computer vision a boost and better performance. Improved hardware, on the other hand, has made the implementation of computer vision applications more efficient and cost-friendly.

Moreover, modern enterprises are making a shift towards data-centric computer vision, where the primary emphasis is laid on collecting and processing high-quality data – instead of using algorithms of unprocessed and unlabeled datasets. Furthermore, advancements in the field of Natural Language Processing have also boosted the capabilities of computer vision applications, as it is being integrated with computer vision to enable machines to understand and respond to human language

The adoption of computer vision technology is increasing across various industries – including retail, healthcare and self-driving cars.

Overall, the trends and interests in computer vision technology will have a significant impact on businesses and organisations of tomorrow, in the coming years.

But to really make a mark with your Computer Vision applications, one needs to understand how to correctly implement computer vision models in their enterprise. Optimisation of computer vision models, using reliable and robust AI, differs for each enterprise – depending on the scale and needs of the organisation.

This is where AiProff can assist you. We are a group of skilled experts with a wealth of knowledge and experience in machine learning, artificial intelligence, and data science.

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