Concession and retail sales are a particular area where vision AI can make a huge impact for sports fans and other stadium patrons. With vision AI technology and a rich stream of visual data, spectators can expect a next-generation experience.
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.
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.
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.
From bird watching to cancer detection, smart camera technology is enhancing our ability to improve our lives and transform the ways businesses operate using visual information from the world around us.
In Netflix’s new animated film The Mitchells vs. The Machines, an object detection error is the unlikely hero.
Data Annotation for Computer Vision
Feb 8th 2022 | Labeling Tutorial
Detecting Unwanted & Hazardous Objects
Oct 26th 2021 | Computer Vision
Dew-able AI: Solving the Mountain Dew Super Bowl Challenge
Feb 13th 2021 | Computer Vision
Plainsight Pipelines, the latest addition to our computer vision platform, makes it even easier for users to build and scale computer vision models, prediction pipelines, and custom data transformations.
October 11th-13th, Plainsight’s Co-Founder and CEO, Carlos Anchia, is joining AI professionals from around the globe to discuss the future of computing at Google Cloud Next. Get the full run down on the events must watch sessions.
The NFL has partnered with Amazon Web Services (AWS) to create the Digital Athlete, an AI model that replicates any NFL player within a virtual environment that’s chock-full of real-world scenarios. Within this digital setting, league doctors and coaches can test out new techniques, equipment, and even the implications of new rules before they play out on the field.