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Image recognition and comparison apps for Zoos and Museums

About the Client

° This is our own Product
° Product aims to provide information about an image. The tagline for the product is “picture says 1000 words, but zoomage says 1000 words about a picture”
° The primary target group is Zoos and Museums. Secondary target group is any industry that would like to share info based on images
° Mobile apps captures the images and send it to AI image processing engine, which in turns provides back the data about the image.
° Gaming and advertisements have also be included as part of the apps

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Our Solutions

° Zoo or Museum registers the entity in the cloud based application
° Define animals/assets and upload the video (preferably 360°)
° Application generates images from the videos
° Generation of training data, feature extracts, descriptors, key points and store on the server side (offline process)
° Mobile apps to capture the image and send to the Server for image comparison
° Generation of feature extracts, descriptors, key points from the image and compare it with training feature extracts, descriptors, key points to predict the image
° APIs return information of the image that will be displayed in the mobile apps for the user
° The application does not just classify the animal or asset but exactly describes the identity including name and other parameters
° The application is based on 3 languages French, German and English

Executive Summary

° Subitomf application and the public webpage are integrated currently. For marketing purposes, FCM wanted the public landing pages and the application to be separated.
° Implementation of the public search which allows the Visitors to search the musicians without Orchestra registration
° Redesign of the application UI/UX Templates to suit the new Theme and layout that will be purchased for public pages

Business Challenges

° Application was helping the users to find musicians in their locality
° The Objective of the Project is to take thumbnail photo from the profile picture (which is of different sizes and poses) uploaded by the musicians using AI (Image recognition)

Technology Landscape

Layer Technology
Backend API Frame work Node js and React js
Version control Git
AI Ruby language converted from Python for Image recognition and cropping
Database Postgres
Analytics Google Charts, Google analytics
Testing tool Cucumber
Web Server nginx
App Server Passenger

Application Preview

Value Proposition

° Application of ANN and Machine Learning algorithms
° Used Python’s Face Recognition
° Interlinked Python and Ruby using RubyPython and produce a unique ruby module that can get thumbnail photo of musicians.

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