Modeling human vision using feedforward neural networks
Name
1014181870-MIT.pdf
Description
Full printable version
Size
3.21 MB
Format
Adobe PDF
Checksum (MD5)
d9e30cb4849d93d54a7add573b802821
Author(s)
Chen, Francis Xinghang
Advisor(s)
Tomaso Poggio.
Date Issued
2016
Publisher
Massachusetts Institute of Technology
Abstract
In this thesis, we discuss the implementation, characterization, and evaluation of a new computational model for human vision. Our goal is to understand the mechanisms enabling invariant perception under scaling, translation, and clutter. The model is based on I-Theory [50], and uses convolutional neural networks. We investigate the explanatory power of this approach using the task of object recognition. We find that the model has important similarities with neural architectures and that it can reproduce human perceptual phenomena. This work may be an early step towards a more general and unified human vision model.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 81-86).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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