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From Pixels to Insights: How Computer Vision Helps Make Sense of Big Data

Over the past decade, the use of computer vision technology across different business sectors has soared through the ranks. From simple tasks such as reading or interpreting text from a document or picture, computer vision has branched its growth into high-tech domains. The use of AI cameras for traffic management, access control, etc., are modern applications in this regard. 

Computer Vision’s Viability for Big Data

As technology progresses, computer vision is being supplemented by an array of new and intelligent learning techniques powered by machine learning. It can uncover deeper context from not just textual documents or images but also from large media files like video streams. In short, computer vision helps businesses, as well as other stakeholders, make more sense of large volumes of data created in multiple formats.

With such capabilities, businesses can certainly leverage the power of computer vision in their big data initiatives. Let us explore some of the major areas where computer vision can help make better sense of big data:

Image Analytics

In the past, big data analytics initiatives were focused on extracting insights from data supplied as binary and textual information across a business operational landscape. This limited the ability of decision-makers to dive deep into several large data streams. For example, decision-makers could not process data to identify people or items from a large set of image data, learn about their relationships, determine similarities and differences, etc. 

Such capabilities find tremendous uses in modern-day businesses. For example, a social media platform can use computer vision to correctly identify the content within images posted by users. They can apply censorship rules and automatically classify images according to their type for organizing and labeling them for future search results, etc. This allows them to:

  • Accelerate their automated image verification systems
  • Respond more effectively to requests by users or government agencies to take down offensive images

Capturing Dynamic Data Streams

Continuously flowing data streams are always a challenge for traditional data analytics initiatives. There is a lack of observability that can provide a stable snapshot of the data from a highly dynamic data stream. 

Suppose a stable data snapshot is not obtained. In that case, it will be nearly impossible to run analytics operations because the predictions or insights from the analytics would have arrived only after the actual observable scenario has progressed a distance. This is where computer vision can make a difference by tracking discovered data objects in a stream and continuously doing that through their lifetime of progression. 

This allows analytics initiatives to learn more behavior patterns from the continuous data stream and arrive at more precise insights. An example of this scenario would be driverless cars. The cars can be guided by a decision-making system powered by big data and computer vision. The intelligent computer vision system continuously tracks the motion of the vehicle from all dimensions. It can quickly adapt to traffic scenarios by relaying the exact upcoming driving conditions on the road in real-time from video feeds to the analytics engine.

Deeper Analysis

With computer vision, it becomes easier to dive deep into internal patterns within visual data, such as images. This allows big data analytics tools to uncover information about deeper ingrained contexts within images by comparing them with similar patterns discovered and classified earlier. 

One of the best examples for this use case would be healthcare diagnostics. Doctors and other medical professionals can rely on computer vision-powered big data platforms to quickly diagnose conditions from MRI or CT scan results. 

Some internal patterns may not be detected by the naked eye, but computer vision can definitely:

  • Pick them up
  • Compare them with known patterns of deviant behavior
  • Generate accurate diagnostic observations

Cognitive Visual Intelligence

With integrated computer vision, it becomes easier to model possible outcomes by analyzing a stream of related event data captured as images or video. For example, by analyzing a series of images like graphical data or charts, or videos about the progression of a weather system, it becomes easier to model a potential travel map for the next couple of days or months. This is extremely useful for functions like storm predictions and related preventive measures. 

Another example could be the generation of possible facial characteristics of a person over time, which can assist authorities in identifying suspects of tough crime cases that happened years ago.

Wrapping It All Up: The Key Takeaways

Computer vision will go a long way in helping enterprise big data initiatives understand better about the visual data at disposal. Some of the applications we have covered here are just the minimum possibilities that can be realized with this combo of computer vision and big data. 

However, to achieve better traction and progress with such innovative technologies, it is crucial to have a well-structured and data-driven approach to operations in your business. Only then would such high-end initiatives be able to work with accurate data sources to deliver the best results. This is where a technology partner like Ascentt can be your biggest advantage. Get in touch with us to know more.

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