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Editshare - Enhancing EditShare’s flow media asset management with Automated AI

Editshare - Enhancing EditShare’s flow media asset management with Automated AI

Thought leadership

Top 3 reasons why computer vision is changing the asset management game

February 16, 2023
April 1, 2022
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Let’s face it, we are generating record-breaking amounts of visual content every day. Systems are overwhelmed by the sheer volume of images coming in and marketeers are struggling to find the perfect image that captures a brand's value. Customers then have to navigate a series of patchy omni-channel campaigns that constantly present visual inconsistencies that have a negative impact on their experience.

Professionals working with large visual archives are now turning to AI-powered computer vision solutions to redefine how they work with content. Next-generation solutions like Superhuman Vision™ from Mobius Labs instantly understand everything about an image or a video to transform them into precise insights, improved customer experiences, and strategic business differentiators. This technology is a game-changer for anyone working with large amounts of visual content, and these are the top three reasons why:

Really understands your visual content

Next-generation computer vision technology can easily understand and categorize abstract concepts and physical objects to find emotional, meaningful, context-rich visuals that perfectly capture a brand's essence and build an emotional connection with a customer.  This opens the door to a wealth of business opportunities because now you can better connect customers with visual content that match their tastes and buying behavior across a variety of channels.

Simplifies content creation workflows

According to the latest research on visual content production by the Harvard Business Review, 36% of marketeers said that producing visually consistent content was their biggest challenge when building new omni-channel marketing campaigns.

The latest iterations of computer vision technologies analyze and tag visual content not only by objects or faces, but also by aesthetics, trends, lighting, shot style, logos and emotions. Videos and images can then be easily found by searching in simple language that specifies moods, atmospheres or activities. This simplifies the discovery process by ensuring anyone can easily find the right visuals for their content.

Keeps burgeoning content libraries under control

Speed and accuracy are critical to keep large visual archives properly organized and up to date with the latest developments. You can tag up to 50 images a second, the equivalent of four million images per day and process video at 10 times normal speed.

Next-generation computer vision technology can be deployed directly in-house, reducing image processing time by more than 50% without having to sacrifice accuracy or precision.  It also keeps your content where it belongs, by removing the need for cloud storage or expensive third party integrations.

To discover more about how computer vision can help you unlock the true potential of your visual content, meet us at stand #8 at the DAM Europe event in London, on 22nd-23rd at the Thistle Hotel Marble Arch, or schedule a meeting with a Mobian in advance following this link.

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Mobius Labs GmbH is receiving additional funding by the ProFIT program of the Investment Bank of Berlin. The goal of the ProFIT project “Superhuman Vision 2.0 for every application- no code, customizable, on- premise  AI solutions ” is to revolutionize the work with technical images. (f.e.) This project is co-financed by the European Fund for Regional Development (EFRE).

In the ProFIT project, we are exploring models that can recognize various objects and keywords in images and can also detect and segment these objects into specific pixel locations. Furthermore, we are investigating the application of ML algorithms on edge devices, including satellites, mobile phones, and tablets. Additionally, we will explore the combination of multiple modalities, such as audio embedded in videos and the language extracted from that audio.