Project Overview

Sampling a Trajectory

    We study the use of games and crowd sourcing to capture and analyze scientific trajectory data.  Advanced imaging tools give scientists the ability to capture images from the microscopic scale to the planetary scale. However, the scientific value of such images is often limited by the manual and time consuming work required to process them.  Meanwhile, the public spends millions of hours playing Farmville, Angry Birds and Sudoku. This represents an opportunity to harness Human Power Units (HPU) cycles for more productive but still enjoyable work. Recently, it was shown that untrained citizen scientists can be effectively used to help solve problems in various scientific settings: from Galaxy Zoo, which helps astronomers with image labeling, to FoldIt, which helps biologists with protein folding.

Color-tagged Ant Subjects

    Our AngryAnts game employs citizen scientists to trace (i) static objects in an image and (ii) moving objects such as ants in a video.  The goal is to tackle the challenging data validation problem of selecting an accurate trajectory for each object from the several (possibly inaccurate) trajectories submitted by the citizen scientists whom play the game.  A simple quantitative consensus of several estimates is found by taking an average. However, computing the consensus of traces is not as straight forward, and we design algorithm which use clustering, geometric alignment, sampling, as well as other methods for this purpose.    


Partial support from NSF grant 1053573 "ImageQuest: Citizens Advancing Biology with Calibrated Imaging and Validated Analysis"