In this post, we’ll define vision AI, what it means for different businesses and stakeholders, and how any user can get started executing their computer vision project today.
What is Vision AI, and why is it important?
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Computer Vision and Retail’s Digital Transformation
When it comes to digital transformation, retailers are no longer asking, “What if?” They’re saying the time is now. For the retail industry in particular, integrating vision AI applications into traditional store operations can open up a world of fresh insights that empower better decision making.
See More to Learn More and Solve More with Plainsight Vision AI
Visual data truly is the last great untapped resource for enterprises hoping to analyze and better understand their business. Without computer vision technology to derive insights from their images or video streams, organizations are unable to do much more than just collect visual data. AI-powered computer vision solutions offer the means to put visual data to work, improving processes and solving business challenges.
Computer Vision and Waste Recycling: Solving the Trash Problem
Happy Earth Day! Waste management might not be the first thing you think about when someone mentions computer vision but it’s actually an excellent use case for the technology.
Lights, Camera, Vision: AI and Oscars Fashion
To get in the Oscars spirit, Plainsight decided to shake up the conversation once again and introduce vision AI to Academy Awards. We dove into the Oscars fashion archives to develop a model for classifying suits and dresses. Our goal was to determine whether or not a vision AI model trained on Best and Worst-dressed lists would agree with popular consensus.
Tiling: The Key to Small Object Detection
Tiling is an important process for analysis of images with computer vision and allows for a more detailed look at specific sections of an image without sacrificing resolution. After dividing an image into tiles, computer vision algorithms can then inference on each individual tile before reassembling them into a new composite image.
Groundhog Day 2022: Keep Computer Vision Accuracy Out of the Shadows
Shadows can pose a unique problem for model accuracy, specifically in the case of instance segmentation and object detection. Without a robust dataset that includes shadows, your model may mistake the shadow as an additional detection–which can skew accuracy.
Dog? Pig? or Loaf of Bread? Computer Vision and Object Detection
In Netflix’s new animated film The Mitchells vs. The Machines, an object detection error is the unlikely hero.
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