Two approaches to robust hand pose estimation : generative modeling and semantic relations
Name
1128024162-MIT.pdf
Size
3.48 MB
Format
Adobe PDF
Checksum (MD5)
84ee71fa015ec1c20717794bfc3b87b0
Author(s)
Mata, Cristina(Christina Florica)
Advisor(s)
Boris Katz.
Alternative Title
2 approaches to robust hand pose estimation : generative modeling and semantic relations
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
This thesis explores hand pose estimation through the use of two methods. We first investigate the use of segmentation networks within pose estimation pipelines with a focus on fine parts segmentation. We present two implementations of a novel method for fine parts segmentation employing a higher-order Conditional Random Field (CRF) that measures attachment and containment of fine parts. The first implementation is of the CRF as a post-processing module on top of a Convolutional Neural Network (CNN). The second addresses efficiency bottlenecks in the first by implementing the CRF as a Recurrent Neural Network (RNN) and allowing for end-to-end training with the CNN. Limited by the accuracy of fine parts segmentation and wishing to avoid propagation of segmentation errors through a pipeline, we turn to generative modeling methods for hand pose estimation and present an inverse-graphics approach implemented in a probabilistic programming language. Spurred by the lack of occlusion in hand image datasets, we present the MIT Partially Occluded Hands Dataset, a large-scale dataset of single RGB images, half of which feature natural hand-object interactions, and evaluate several baselines on this dataset.
Description
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 107-111).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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