The project team will create a small database of famous paintings and their parodies. (Go to Google Images and query “arnolfini parody” to see what I mean).

Then, the team will develop image similarity measures to automatically detect parodies. Deep neural network and transfer learning approaches will be used. 

See the following paper for more information:

The project will involve writing AI bots for the slither.io game. 

Reinforcement learning and deep reinforcement learning approaches can be implemented eventually, but the first attempts will be based on heuristics. 

The bot will learn in two modes:

-Self play: many instances of the bot will play the game with different parameters, and the parameters will be adjusted based on the success of the bot.

-Learning by demonstration: a human will play the bot, and the game will be stored to learn from it.

Automated localization of logos and handwritten signatures in scanned document is a promising facility for many business activities. We describe here a discriminative framework for detecting logos and signatures from a document of any type. The method is based on the classification of segmented image regions using a set of features. The segmentation is done for both known logo and signature regions, and non-logo & non-signature regions, and feature sets are extracted for both positive (containing logo or signature) and negative segments.