Skip navigation
Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01dn39x4152
Full metadata record
DC FieldValueLanguage
dc.contributor.advisorFunkhouser, Thomas A.-
dc.contributor.authorChen, Peter-
dc.date.accessioned2017-07-20T13:47:56Z-
dc.date.available2017-07-20T13:47:56Z-
dc.date.created2017-05-05-
dc.date.issued2017-5-5-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/dsp01dn39x4152-
dc.description.abstractPose estimation of RBG-D images is useful in studying the behavior of laboratory mice. We generate synthetic datasets of mice using an anatomical 3D mesh model; we manipulate it and output depth images as heatmaps with ground truth labels of 48 joints corresponding to body parts. Using these datasets, we trained a convolutional neural network to estimate the position of joints given a depth image of a mouse. Using these methods, we achieved 95% accuracy for the head joint and 92% accuracy for other joints.en_US
dc.language.isoen_USen_US
dc.titleSynthetic Dataset and Pose Estimation for Miceen_US
dc.typePrinceton University Senior Theses-
pu.date.classyear2017en_US
pu.departmentComputer Scienceen_US
pu.pdf.coverpageSeniorThesisCoverPage-
pu.contributor.authorid960864804-
pu.contributor.advisorid910083875-
Appears in Collections:Computer Science, 1987-2024

Files in This Item:
File SizeFormat 
pcchen_thesis.pdf532.31 kBAdobe PDF    Request a copy


Items in Dataspace are protected by copyright, with all rights reserved, unless otherwise indicated.