First Advisor

Dan Hammerstrom

Term of Graduation

Summer 2020

Date of Publication

9-29-2020

Document Type

Thesis

Degree Name

Master of Science (M.S.) in Electrical and Computer Engineering

Department

Electrical and Computer Engineering

Language

English

Subjects

Pattern perception, Associative storage, Support vector machines, Computer engineering

DOI

10.15760/etd.7453

Physical Description

1 online resource (xi, 81 pages)

Abstract

With recent developments in deep networks, there have been significant advances in visual object detection and recognition. However, some of these networks are still easily fooled/hacked and have shown "bag of features" kinds of failures. Some of this is due to the fact that even deep networks make only marginal use of the complex structure that exists in real-world images. Primate visual systems appear to capture the structure in images, but how?

In the research presented here, we are studying approaches for robust pattern matching using static, 2D Blocks World images based on graphical representations of the various components of an image. Such higher-order information represents the "structure" or "shape" of the visual object. This research led to a technique for representing an object's structural information in a Sparse Distributed Representation (SDR) loosely based on the kinds of cortical circuits found in primate visual systems.

We apply probabilistic graph isomorphism and subgraph isomorphism to our 2D Blocks World images and achieve O(1) and O(nk) complexity for an approximate match. The image labeled graph is created using OpenCV to find the object contours and objects' labels and a fixed radius nearest neighbor algorithm to build the edges between the objects. Pattern matching is done using the properties of SDRs. Next, we use SVM to learn and distinguish images. SVM partitions the vector space where classification accuracy on noisy images gives us an assessment of how much information the SDR is capturing.

Rights

In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

Comments

Part of the work has been published in an International Conference on Neuromorphic Systems (ICONS) paper in 2019. Some passages included in the thesis have been taken from this paper.

This work was supported in part by the C-BRIC, one of six centers in JUMP, a Semiconductor Research Corporation (SRC) program sponsored by DARPA, SRC Award Number 2018-JU-2777.

Persistent Identifier

https://archives.pdx.edu/ds/psu/33997

Download

Share

COinS