Modeling human vision using feedforward neural networks

• Main Author: Chen, Francis Xinghang
• Other Authors: Tomaso Poggio.
• Format: Others
• Language: English
• Published: Massachusetts Institute of Technology 2017
• Subjects: Electrical Engineering and Computer Science.
• Online Access: http://hdl.handle.net/1721.1/112824

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). === 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. === by Francis Xinghang Chen. === M. Eng.