In 2022, Afraz Jaffri, Director Analyst at Gartner, said that the AI Hype Cycle is “full of innovations expected to drive high or even transformational benefits.” He stressed that enterprises need to “pay attention” to innovations, such as “decision intelligence and edge AI,” that will hit mainstream adoption soon.

Fast forward to today, and the AI space is rife with groundbreaking advancements. One emerging and prominent use case is the synergy between generative AI and computer vision.

Generative AI and computer vision are both rooted in artificial intelligence. In essence, generative AI is a computational system that creates high-quality content based on prompts. Generative data can help computer vision systems improve their detection and classification capabilities. 

Unveiling the Power of Generative AI

As per Gartner’s analysis, generative AI is projected to play a substantial role in data production and analysis on a global scale. By the year 2025, it is estimated that approximately 10% of all generated data will be attributed to the contributions of generative AI technologies. The rigorous amelioration in the areas of generative AI has been a significant contributor to its ability to produce high-quality content.

Generative AI harnesses advanced machine learning techniques such as generative adversarial networks, reinforcement learning, and unsupervised learning. These techniques enable the technology to produce large volumes of high-quality data in the form of videos, text, images, etc. By 2027, approximately 30% of manufacturers are expected to embrace generative AI technology to optimize their product development processes.

The realm of generative AI serves as a support to computer vision, creating appealing and realistic imagery for video and camera surveillance systems. The prevalence of generative AI tools is anticipated to improve the efficiency of these systems significantly. 

Up until now, we have already seen the capabilities of AI-based tools like Dall-E 2, Midjourney, Deep Dream Generator, and Big Sleep, which can generate images based on textual descriptions. Moreover, applications of generative AI chatbots, such as ChatGPT and Bing AI, have found their way into diverse sectors, including education, finance, advertising, and healthcare. By liberating the world from the limitations of poorly generated data, generative AI is poised to bring forth a multitude of solutions.

Illuminating the Realm of Computer Vision

As per Allied Market Research, the global computer vision market was valued at $9.45 billion in 2020. It is expected to grow to $41.11 billion by 2030 at a CAGR of 16.0%. Deep learning, a fundamental form of machine learning, and convolutional neural networks (CNNs) are the key technologies employed to achieve computer vision capabilities.

To better understand the role that generative AI can play, here’s an overview of the computer vision working methodology:

• Image acquisition: Obtain image or video data using devices like cameras.
• Image preprocessing: Prepare data by removing noise and enhancing quality.
• Feature extraction: Extract image or video features using mathematical techniques.
• Object detection: Identify objects through matching or machine learning methods.
• Object tracking: Monitor object movement using tracking techniques like filtering.
• Image understanding: Comprehend image or video meaning through recognition techniques.

The Synchronized Symphony Between Generative AI And Computer Vision

While generative AI facilitates cognitive processes, computer vision enables perception, observation, and a comprehensive understanding of the visual world. Within the realm of computer vision, generative adversarial networks (GANs) can be employed for various tasks, including conditional image generation, 3D object generation, and video synthesis. And this serves several purposes. 

For example, generative AI technologies can be used to create high-quality images (synthetic data) that can inform engineering decisions, help institutions conceptualize threat prevention strategies, and more. In fact, with such lifelike visuals, institutions can work towards predicting how events can unfold. This could completely alleviate the issues that otherwise stem from post-mortem analysis of problems when they have escalated. 

The creation of synthetic data serves an even more granular purpose of helping train computer vision models. By examining synthetic data, organizations can gain knowledge about different visual representations in a controlled environment. The models can be aloof of bias that can potentially arise from data that is collected in the real world. This can help organizations train better computer vision models that can better suffice applications such as fraud detection, diagnostics, etc.

Overall, the synergy between generative AI and computer vision can be a significant contributor to driving innovations in visual analytics as well as boosting the security surveillance capabilities of businesses. Applications like perimeter security, hard-hat detection, etc. that are catered to by profound visual analytics can become even more efficient.

Charting the Path Ahead – How a Computer Vision Platform Approach Helps?

Regardless of the industry use case, businesses that wish to leverage generative AI and computer vision need to have the appropriate tools to facilitate their visual analytics operations. As it stands, a computer vision platform approach enables organizations to capture, process, and analyze visual data in real-time and make intelligent decisions to curb disruptions and potential threats. 

With our intelligent, computer vision-based automation platform, enterprises are well-equipped to unlock the full potential of visual data. By leveraging advanced AI, deep learning, and artificial neural networks, we enable businesses to go beyond post-mortem analysis and human monitoring, and instead, make reliable sense of visual data in real-time. 

All in all, through predictive capabilities and timely action-inspiring insights, KamerAI empowers businesses to achieve greater efficiency, automation, and decision-making prowess. See KamerAI in action here.