Full metadata
Title
Reconstruction-free inference from compressive measurements
Description
As a promising solution to the problem of acquiring and storing large amounts of image and video data, spatial-multiplexing camera architectures have received lot of attention in the recent past. Such architectures have the attractive feature of combining a two-step process of acquisition and compression of pixel measurements in a conventional camera, into a single step. A popular variant is the single-pixel camera that obtains measurements of the scene using a pseudo-random measurement matrix. Advances in compressive sensing (CS) theory in the past decade have supplied the tools that, in theory, allow near-perfect reconstruction of an image from these measurements even for sub-Nyquist sampling rates. However, current state-of-the-art reconstruction algorithms suffer from two drawbacks -- They are (1) computationally very expensive and (2) incapable of yielding high fidelity reconstructions for high compression ratios. In computer vision, the final goal is usually to perform an inference task using the images acquired and not signal recovery. With this motivation, this thesis considers the possibility of inference directly from compressed measurements, thereby obviating the need to use expensive reconstruction algorithms. It is often the case that non-linear features are used for inference tasks in computer vision. However, currently, it is unclear how to extract such features from compressed measurements. Instead, using the theoretical basis provided by the Johnson-Lindenstrauss lemma, discriminative features using smashed correlation filters are derived and it is shown that it is indeed possible to perform reconstruction-free inference at high compression ratios with only a marginal loss in accuracy. As a specific inference problem in computer vision, face recognition is considered, mainly beyond the visible spectrum such as in the short wave infra-red region (SWIR), where sensors are expensive.
Date Created
2015
Contributors
- Lohit, Suhas Anand (Author)
- Turaga, Pavan (Thesis advisor)
- Spanias, Andreas (Committee member)
- Li, Baoxin (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
vii, 47 p. : ill. (some col.)
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.29658
Statement of Responsibility
by Suhas Anand Lohit
Description Source
Viewed on June 11, 2015
Level of coding
full
Note
thesis
Partial requirement for: M.S., Arizona State University, 2015
bibliography
Includes bibliographical references (p. 44-47)
Field of study: Electrical engineering
System Created
- 2015-06-01 08:04:18
System Modified
- 2021-08-30 01:30:17
- 3 years 3 months ago
Additional Formats