Deep learning-based solutions for precision agriculture have recently achieved promising results. learning has been used to identify crop diseases at the initial stages of disease development in an effort create effective management systems. However, use deep and unmanned aerial system (UAS) imagery track spread diseases, diseased regions within cornfields, notify users with actionable informat...

Segmentation techniques play an important role in understanding high-resolution polarimetric synthetic aperture radar (PolSAR) images. PolSAR image segmentation is widely used as a preprocessing step for subsequent classification, scene interpretation and extraction of surface parameters. However, speckle noise and rich spatial features of heterogeneous regions lead to blurred boundaries of hig...

Speed and accuracy are important factors when dealing with time-constraint events for disaster, risk, and crisis-management support. Object-based image analysis can be a time consuming task in extracting information from large images because most of the segmentation algorithms use the pixel-grid for the initial object representation. It would be more natural and efficient to work with perceptua...

Text segmentation is an important step in the process of character recognition. In literature, there have been numerous methods that work very well in practical applications. However, when an image includes strong noise or surface reflection distraction, accurate text segmentation still faces many challenges. Observing that the stroke width of text is stable and significantly different from tha...

BACKGROUND Nonadherence to medications occurs in up to 70% of patients with asthma. The effect of improving adherence is not well quantified. We developed a mathematical model with which to assess the population-level effects of improving medication prescribing and adherence for asthma. METHODS A mathematical model, calibrated to clinical trial data from the U.S. NHLBI-funded SOCS trial and v...