High-definition human visual attention mapping using wavelets

In this work, three novel approaches to detecting visual attention in images are presented. The idea behind detecting areas within images or video that naturally attract a viewer’s attention is based on the concept of generating pre-attentive saliency maps. Saliency, in and of itself, relates to some measure of “conspicuity” in the visual field and is believed to be an important precursor for many tasks in computer vision. One of the proposed methods in this thesis detects salient regions, while the other two detect salient edges. The classical approach to saliency detection proposed by Itti is extended by introducing wavelets as a lossless resizing tool while maintaining the aspect of biological inspiration. In addition to this, the spectral residual method and the frequency tuned method are modified using wavelets to allow for salient edge detection. Tests show that the proposed methods yield results that are not only comparable to leading,cutting-edge methods, but also exceed them in terms of correct and complete object detection as well as noise reduction.

High-definition human visual attention mapping using wavelets

In this work, three novel approaches to detecting visual attention in images are presented. The idea behind detecting areas within images or video that naturally attract a viewer’s attention is based on the concept of generating pre-attentive saliency maps. Saliency, in and of itself, relates to some measure of “conspicuity” in the visual field and is believed to be an important precursor for many tasks in computer vision. One of the proposed methods in this thesis detects salient regions, while the other two detect salient edges. The classical approach to saliency detection proposed by Itti is extended by introducing wavelets as a lossless resizing tool while maintaining the aspect of biological inspiration. In addition to this, the spectral residual method and the frequency tuned method are modified using wavelets to allow for salient edge detection. Tests show that the proposed methods yield results that are not only comparable to leading,cutting-edge methods, but also exceed them in terms of correct and complete object detection as well as noise reduction.

SWRL Net: A Spectral, Residual Deep Learning Model for Improving Short-Term Wave Forecasts

Skillful nearshore wave forecasts are critical for providing timely alerts of hazardous wave events that impact navigation or recreational beach use. While typical forecasts provide bulk wave parameters (wave height and period), spectral details are needed to correctly predict wave and associated circulation dynamics in the nearshore region. Currently, global wave models, such as WAVEWATCH III (WW3), make spectral predictions, but do not assimilate regional buoy observations. Here, Spectral Wave Residual Learning Network (SWRL Net), a fully convolutional neural network, is trained to take recent WW3 forecasts and buoy observations, and produce corrections to frequency-directional WW3 spectra, transformed into directional buoy moments, for up to 24 h in the future. SWRL Net is trained with 10 years of collocated NOAA’s WW3 CFSR reanalysis predictions and buoy observations at three locations offshore of the U.S. western coast. At buoy locations SWRL Net residual corrections result in wave height root-mean-square error (RMSE) reductions of 23%–50% in the first 6 h and 10%–20% thereafter. Sea frequencies (5–10 s) show the most improvement compared to swell (12–20 s). SWRL Net reduces mean direction RMSE by 28%–54% and mean period RMSE by 20%–56% over 24 forecast hours. While each model is trained and tested at independent locations, SWRL Net exhibits generalization when introduced to data from other locations, suggesting future development may be composed of training sets from multiple locations.