Fingerprint classification using an AM-FM model

By Marios S. Pattichis, George Panayi, A. Bovik, Shun-Pin Hsu. 2001

59 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Safety Research, Signal Processing

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Constantinos S. Pattichis
2. Chris Biemann
3. Douglas B. Kell
4. Andreas Holzinger
5. Marios S. Pattichis
6. Víctor Murray
7. E. Barriga
8. Peter Soliz

In Safety Research

1. Marios S. Pattichis

In Signal Processing

1. Douglas B. Kell
2. E. Barriga
3. Constantinos S. Pattichis
4. Andreas Holzinger
5. Víctor Murray
6. Peter Soliz
7. Chris Biemann
8. Marios S. Pattichis

Citations in the corpus

• What do we need to build explainable AI systems for the medical domain? (2017, methodology): “Earlier work with AM-FM models had demonstrated its promise with textured images, as in the example of fingerprint image classification (Pattichis et al., 2001), tree image analysis analysing growth seasons (Ra-machandran et al., 2011), non-stationary wood-grain characterization, and other texture…”
• Multiscale AM-FM Demodulation and Image Reconstruction Methods With Improved Accuracy (2010, methodology): “1) Least-Squares Reconstructions using AM-FM harmonics (LESHA): We first consider reconstructing an image using its AM-FM harmonics (see [19]):”
• Tree Image Growth Analysis Using Instantaneous Phase Modulation (2011, methodology): “To appreciate the complexities involved in processing these images, we present three examples in Figure 1 (data acquisition is described in Section 2.5).”
• Recent multiscale AM-FM methods in emerging applications in medical imaging (2012, background): “An early example of the use of frequency-domain filtering to target a particular application can be found in the fingerprint examples in [17].”