ArticleCognitive neurodynamics2011
Bottom-up attention: pulsed PCA transform and pulsed cosine transform.
Article in Cognitive neurodynamics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 14 citations in OpenAlex.
- Visual Saliency via Multiscale Analysis in Frequency Domain and Its Applications to Ship Detection in Optical Satellite Images.Frontiers in neurorobotics · 2021Article
- Saliency computation via whitened frequency band selection.Cognitive neurodynamics · 2016Article
- Predicting the eye fixation locations in the gray scale images in the visual scenes with different semantic contents.Cognitive neurodynamics · 2016Article
- Reduced multiple empirical kernel learning machine.Cognitive neurodynamics · 2015Article
- Airport detection in remote sensing images: a method based on saliency map.Cognitive neurodynamics · 2013Article
- Spatio-temporal saliency perception via hypercomplex frequency spectral contrast.Sensors (Basel, Switzerland) · 2013Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper we propose a computational model of bottom-up visual attention based on a pulsed principal component analysis (PCA) transform, which simply exploits the signs of the PCA coefficients to generate spatial and motional saliency. We further extend the pulsed PCA transform to a pulsed cosine transform that is not only data-independent but also very fast in computation. The proposed model has the following biological plausibilities. First, the PCA projection vectors in the model can be obtained by using the Hebbian rule in neural networks. Second, the outputs of the pulsed PCA transform, which are inherently binary, simulate the neuronal pulses in the human brain. Third, like many Fourier transform-based approaches, our model also accomplishes the cortical center-surround suppression in frequency domain. Experimental results on psychophysical patterns and natural images show that the proposed model is more effective in saliency detection and predict human eye fixations better than the state-of-the-art attention models.
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