Mosaic delivers intelligent image processing solutions powered by custom AI & computer vision models that automate laborious tasks, de-risk operational processes, and streamline decisions with analytics that perform and users can trust.
Aerial Vantage has received Federal Aviation Administration (FAA) approval of a waiver allowing it to conduct advanced remote sensing missions using unmanned aircraft systems (UAS, or drones) over a large ranch in Florida.
In this Q&A, we sat with Principal Data Scientist Priya Karanth for her take on the latest innovations and most powerful applications behind the ever-changing landscape of computer vision. Let’s see what lies beyond the surface.
Mosaic built a computer vision solution to improve the process of a vehicle moving through an automated car wash.
Wielding the power of computer vision human action extraction to gain insights from video data presents an opportunity for logistics companies to make improvements for new and current customers.
Mosaic developed a proof-of-concept (POC) system that uses computer vision technologies to infer human subjects’ positions, proximity, and gaze directions from video feeds. Future human missions to Mars will pose mental and behavioral challenges for crew members.
Filtering search results is an essential part of any eCommerce website. Can you remember the last time you shopped online without filtering on product attributes such as color, size, brand, etc. Additionally, rich product attributes are critical to Google SEO which drives traffic and website sales.
We decided to approach this problem as a similarity learning modeling effort. We used convolutional neural networks to train a model that takes an image or video as input and outputs a vector representation of the input, such that similar inputs will be close to each other in the vector space. The vector learning is driven by a triplet loss function.
We decided to approach this problem as a similarity learning modeling effort. We used convolutional neural networks to train a model that takes an image or video as input and outputs a vector representation of the input, such that similar inputs will be close to each other in the vector space. The vector learning is driven by a triplet loss function.
Object detection in video has become a matter of routine, however, expanding these models to detect an object of your choosing requires many thousands, if not tens of thousands, of training examples. Few shot learners seek to make this process cheaper and easier by learning to detect new objects with only a small handful of examples (i.e. 1-30).
Deep learning, specifically computer vision and natural language processing, can be designed to identify defects during the product packaging process. These deep learning models can verify that a label on a package is present, correct, straight, and readable.