We propose a novel hybrid otitis media (OM) computer aided detection (CAD) system, designed to aid in the self-diagnosis of various forms of OM. OM is a prevalent disease in both children and adults. Our system is able to differentiate normal ear from acute otitis media (AOM), otitis media with effusion (OME) and the multi-categories of chronic otitis media including perforation, retraction, cholesteatoma, etc. We propose a modified double active contour segmentation method designed for use with otoscope images, and enabled to handle user acquired data. To describe the visual symptoms (e.g., red, bulging, effusion, perforation, retraction, etc.) of otitis media accurately, we extract color, geometric and texture features by grid color moment, Gabor filter, local binary pattern and histogram of oriented gradients. A powerful classification structure based on Adaboost is used to select the most useful features and build a strong classifier. Our system achieves classification accuracy as high as 88.06% and is suitable for real use. In addition, some interesting observations about OM otoscope images are also discussed.
We propose a novel borderless design for dual displays by using novel BLU and innovative light deflecting film above the display panel. Compared to the previous design, this new structure provides much better borderless image quality, thinner module, and higher brightness. We also set up a simulation model to optimize the borderless performance. Finally, we demonstrate great borderless effect on 5.8 inch dual displays and successfully eliminate the 3mm border width from A-A to A-A visually.
In this paper, we successfully develop a new optical touch which is highly integrated with current structure of TFT-LCD display. The advantages are high touch resolution, no active force, free object touch, low cost and easy integration with panel. Besides, we have set up a simulation system to predict and optimize the touch performance. In addition, the signal to noise ratio (SNR) is measured around 21 by 4" mockup sample. A superb touch display can be achieved in the near future.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.