The use of modern machine learning techniques insure accurate image representaion through color correction and image de-skewing.
Software designed to solve some of the core problems associated with image recongnition and AI.
Cyclone supports elements needed for large-scale data analytics and includes image processing and an Artificial Intelligence platform.
The PYAA Promotion Selection Board AI is a software stack designed to solve some of the core problems associated with detecting and tagging USMC promotion selection board application images.
Our application is capable of photo color profile correction to insure consistent palate variances in the image capture process. It then auto de-skews the image as necessary and identifies the ribbon's sub-components and constructs a ribbon rack template.
Throughout the business world, Artificial Intelligence (AI) is being used to greatly enhance business efficiency and practices that currently require extensive human involvement. It is with this motivation that PYAA, in partnership with Troika, undertook a Phase I project to demonstrate an automated AI-based approach to ribbon detection and OMPF supporting document detection, as a way of potentially improving the efficiency of the Promotion Selection Board process.
Image quality dramatically affects the success of ribbon and device recognition. Dithered, compressed or color altered images are much more difficult to obtain accurate patterns. Also lighting, perspective and skew provide significant challenges.