The smashed filter for compressive classification and target recognition

By Mark A. Davenport, Marco F. Duarte, Michael B. Wakin, Jason N. Laska, Dharmpal Takhar, Kevin F. Kelly, Richard Baraniuk. Electronic imaging, 2007

307 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/b00cb6230124779c9918c76f220d0f70e2203f02

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Surya Ganguli
2. Richard Baraniuk
3. Philippe Steeghs
4. Miles J. Padgett
5. Graham M. Gibson
6. Marco F. Duarte
7. Justin K. Romberg
8. Steven L. Brunton

In Computational Mechanics

1. Surya Ganguli
2. Madhu S. Advani
3. Subhaneil Lahiri
4. Richard Baraniuk
5. Philippe Steeghs
6. Miles J. Padgett
7. Graham M. Gibson
8. Steven D. Johnson

In Radiology, Nuclear Medicine and Imaging

1. Richard Baraniuk
2. Philippe Steeghs
3. Surya Ganguli
4. Subhaneil Lahiri
5. Madhu S. Advani

Citations in the corpus

• Signal Processing With Compressive Measurements (2010, unknown): “Furthermore, the filtering procedure we describe facilitates the separation of signals after they have been acquired in the compressive domain so that each signal can be processed by the appropriate algorithm, depending on the information sought by the user.”
• Statistical mechanics of complex neural systems and high dimensional data (2013, background): “For example, regression [109], signal detection [110], classification [111]–[114], manifold learning [115] and nearest neighbor finding [102] can all be accomplished in a low”
• Single-pixel imaging 12 years on: a review. (2020, background): “In the case of compressive classification, the resulting dimensionally reduced matched filters are sometimes termed “smashed filters” [41].”
• Compressed sensing, sparsity, and dimensionality in neuronal information processing and data analysis. (2012, unknown): “For example, regression (Zhou et al. 2009), signal detection (Duarte et al. 2006), classification (Blum 2006, Haupt et al. 2006, Davenport et al. 2007, Duarte et al. 2007), manifold learning (Hegde et al. 2007), and nearest neighbor finding (Indyk & Motwani 1998) can all be accomplished in a…”
• Compressive Radar Imaging (2007, background): “Information scalability enables us to compute close approximations to these matched filter results directly from incoherent measurements without having to perform expensive reconstruction or approximation computations [14].”