Independent component analysis applied to feature extraction from colour and stereo images

By Patrik O. Hoyer, Aapo Hyvärinen. 2000

289 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

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Citations in the corpus

High-Performance Neural Networks for Visual Object Classification (2011, methodology): “Unsupervised learning methods applied to patches of natural images tend to produce localized ﬁlters that resemble oﬀ-center-on-surround ﬁlters, orientation-sensitive bar detectors, Gabor ﬁlters (Schmidhuber et al. , 1996; Olshausen and Field, 1997; Hoyer and Hyv¨arinen, 2000).”
Learning Invariance from Natural Images Inspired by Observations in the Primary Visual Cortex (2012, unknown): “There are many approaches for learning simple-cell properties (Ol-shausen & Field, 1996; Bell & Sejnowski, 1997; van Hateren & van der Schaaf, 1998; Hoyer & Hyv¨arinen, 2000; Falconbridge, Stamps, & Badcock, 2006; Hamker & Wiltschut, 2007; Rehn & Sommer, 2007; Weber & Triesch, 2008; Wiltschut &…”
A committee of neural networks for traffic sign classification (2011, methodology): “Unsupervised learning methods applied to patches of natural images tend to produce localized filters that resemble offcenter-on-surround filters, orientation-sensitive bar detectors, Gabor filters [2], [3], [ 4 ].”
Learning Optimized Features for Hierarchical Models of Invariant Object Recognition (2003, unknown): “Extensions for complex cells (Hyvärinen & Hoyer 2000; Hoyer & Hyvärinen 2002) and color and stereo coding cells (Hoyer & Hyvärinen 2000) were shown.”
Independent Component Analysis as a Tool to Eliminate Artifacts in EEG: A Quantitative Study (2003, methodology): “It has also been used for MRI, positron emission tomograms, audiograms, and others (Arfanakis et al., 2000; Calhoun et al., 2001; Hoyer and Hyvarinen, 2000; Lee et al., 2001; Suzuki et al., 2002).”
A two-layer sparse coding model learns simple and complex cell receptive fields and topography from natural images. (2001, methodology): “On the other hand, the ai are better to visualize because because they actually correspond to parts of the image data; especially with data that is not purely spatial, visualization of the filters would not be straightforward (Hoyer and Hyvärinen, 2000).”
Finding a roadmap to achieve large neuromorphic hardware systems (2013, methodology): “…Component Analysis (PCA) (e.g., Linsker, 1988; MacKay and Miller, 1990), Independent Component Analysis (ICA) (e.g., Bell and Sejnowski, 1995, 1997; Hoyer and Hyvarinen, 2000), and a range of modiﬁed approaches based on this work (Blais et al., 1998; Zylberberg et al., 2001; Falconbridge et al.,…”
Multi-column deep neural network for traffic sign classification (2012, methodology): “Unsupervised learning methods applied to patches of natural images tend to produce localized filters that resemble off-center-on-surround filters, orientationsensitive bar detectors, Gabor filters (Schmidhuber et al., 1996; Olshausen & Field, 1997; Hoyer & Hyvärinen, 2000).”