Learned Collaborative Stereo Refinement

Stereo Refinement for Photo Editing

We present a method for refining depth information generated by a stereo-matching algorithm with the goal to provide depthaware photo-effect applications. Our key idea is to use structural features of the base image to enhance the depth information. Our method pre-processes the original disparity map by revising the sky region and removing incorrect data (on the left-side of the disparity map) ...

STEREO Fault Protection Challenges and Lessons Learned

Curve Synthesis from Learned Refinement Models

We present a method for generating refined 2D illustrations from hand drawn outlines consisting of only curve strokes. The system controllably synthesizes novel illustrations by augmenting the hand drawn curves’ shape, thickness, color and surrounding texture. These refinements are learned from a training ensemble. Users can select several examples that depict the desired idealized look and tra...

Diversification and Refinement in Collaborative Filtering

This paper considers a popular class of recommender systems that are based on Collaborative Filtering (CF) and proposes a novel technique for diversifying the recommendations that they give to users. Items are clustered based on a unique notion of prioritymedoids that provides a natural balance between the need to present highly ranked items vs. highly diverse ones. Our solution estimates items...

Collaborative games: Lessons learned from board games

Collaborative mechanisms are starting to become prominent in computer games, like massively multiplayer online games (MMOGs); however, by their nature, these games are difficult to investigate. Game play is often complex and the underlying mechanisms are frequently opaque. In contrast, board games are simple. Their game play is fairly constrained and their core mechanisms are transparent enough...