Two approaches to robust hand pose estimation : generative modeling and semantic relations
Author(s)Mata, Cristina(Christina Florica)
2 approaches to robust hand pose estimation : generative modeling and semantic relations
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
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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.
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, 2019Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 107-111).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.