The job

The team built a system for automatically identifying tigers using a specific and unique number or name in the images it gets from the camera traps. The system then lets users input information for each tiger: the unique ID, name, gender, age, number of times they’ve been photographed, the places they’ve been photographed, links to family members, ability to draw familial ties. Tigers are identified using computer vision algorithms built on convolutional neural networks.

The job

We built a system that detects objects that match previously uploaded data sets for different object classes. A convolutional neural network drives detection, while users can perform criminal and suspect searches, add pictures, and delete pictures using the facial comparison system. In addition, there is a tool for adding new object classes.

The job

We built algorithms for the entire biometric process used for iris recognition, which takes place over three stages:

• segment the iris and highlight textures
• highlight singularities and create biometric patterns
• combine the patterns

The job

After studying the problem, we created algorithms that segment objects into a 3D cloud of points. The solution works through a series of steps: processing the cloud of points using inputted data (the entire cloud of points that makes up the tower surrounded by land), pulling the tower out of the cloud, extracting the tower platforms, and then processing each platform’s cloud of points independently. Once that is complete, a set of algorithms kicks off to process each cloud in order to segment the objects on each platform with a high degree of accuracy.